Business Cycles, Patterns and Trends Version 6 PDF

Abiliti: Future Systems

Business Cycles, Patterns and Trends

Throughout eternity, all that is of like form comes around again –

everything that is the same must return in its own everlasting cycle.....

• Marcus Aurelius – Emperor of Rome •

Many Economists and Economic Planners have arrived at the same

conclusion – that most organizations have not yet widely developed, nor

adapted, sophisticated Economic Modelling and Forecasting systems – let

alone integrated their model outputs into core Strategic Planning and

Financial Management process.....

Stoicism – a branch of Philosophy

“All human actions share one or more of these causes: -

chance, reason, nature, habit, delusion, desire, passion and obsession.....”


Stoicism – Motivation for Human Actions

Reason – logic

Human Actions

chance

reason

obsession

passion

habit

nature

delusion

desire

Human Nature – (good and evil)

altruism, heroism

curiosity, inquiry,

ignorance, malice

Desire – need, want

Passion – love, fixation

Obsession – compulsion Serendipity – randomness, chaos

Ritual, ceremony, repetition Primal Instinct–

anxiety, fear, anger, hate

Stochastic

Emotional Deterministic

Reactionary

• Marcus Aurelius • Emperor of Rome

• “Throughout eternity, all

that is of like form will

come around again –

everything that is the same

must always return in its

own everlasting cycle.....”

• “Look back over time, with

past empires that in their

turn rise and fall – through

changing history you may

also see the future.....”

• Marcus Aurelius followed

• Stoic Philosophy •

Business Cycles, Patterns and

Trends • This Slide Pack forms part of a Futures Study Training Module - the purpose of which

is to provide cross-functional support to those client stakeholders who are charged by their

organisations with thinking about the future – research, analysis, planning and strategy: -

– Finance, Corporate Planners and Strategists – authorise and direct the Futures Study.

– Product Innovation, Research & Development – plan and lead the Futures Study.

– Marketing and Product Engineering – review and mentor the Futures Research Study.

– Economists, Data Scientists and Researchers – undertakes the detailed Research Tasks.

– Research Aggregator – examines hundreds of related Academic Papers, “Big Data” & other

relevant global internet content - looking for hidden or missed findings and extrapolations.

– Author – compiles, documents, edits and publishes the Futures Study Research Findings.

– Business Analysts / Enterprise Architects – provide the link into Business Transformation.

– Technical Designers / Solution Architects – provide the link into Technology Refreshment.


Trends • The purpose of a Futures Study Training Module is based on the need to enable clients

to anticipate, prepare for and manage the future - by guiding them towards understanding

of how the future might unfold. This involves planning, organising and running Futures

Studies and presenting the results via Workshops, Seminars and CxO Forums – working

with key client executives responsible for Stakeholder Relationships, Communications and

Benefits Realisation Strategies by helping to influence and shape organisational change

and driving technology innovation to enable rapid business transformation, ultimately to

facilitate the achievement of stakeholder’s desired Business Outcomes – and scoping,

envisioning and designing the Future Systems required to support client objectives -

integrating BI / Analytics and “Big Data” Futures Study and Strategy Analysis outputs

into their core Corporate Planning and Financial Management processes.....

– CxO Forums – executive briefings on new and emerging technologies & trends

– Workshops – discovery workshops to explore future SWAT and PEST matrices

– Seminars – presents the detailed Futures Study findings and extrapolations.

– Special Interest Groups (SIGs) – for stakeholder Subject Matter Experts (SMEs)


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Creative Destruction – drives Disruptive Change

Disruptive Futurism as the "gales of creative destruction" forecast by Austrian economist Joseph Schumpeter in the 1940s - are blowing just

as hard today as they ever were.....

The twin disruptive forces of a severe economic environment and technology-driven innovation are giving birth to novel products and

services, new markets and new opportunities.

• Joseph Schumpeter – Economist •

Joseph Schumpeter

"Creative Destruction drives Disruptive Change" • Joseph Schumpeter – Economist •

JOSEPH SCHUMPETER (1883–1950) in describing Capitalism coined the paradoxical term

“creative destruction”. Numerous economists have since adopted “creative destruction” as a

shorthand description of the FREE MARKET’s disruptive mechanism for delivering economic

progress. In Capitalism, Socialism, and Democracy (1942), the Austrian economist wrote: -

• “The opening up of new markets, foreign or domestic, and the organizational development

from the craft shop to such concerns as U.S. Steel illustrate the same process of industrial

mutation - if I may use that biological analogy - that continuously revolutionises the economic

structure from within, incessantly destroying the old one, ceaselessly creating a new one.

This process of Creative Destruction is the essential character of capitalism.” (p. 83)

• The paradox of the process of technology innovation in economic development - creative

destruction - forms the basis of the discipline of "Disruptive Futurism". Society cannot reap

the benefits of disruptive change – the rewards of creative destruction – without accepting

that there will be both winners and losers in the process of economic transformation. Some

individuals will prosper as new economic opportunities are created. Some individuals will be

worse off - not just in the short term, but perhaps for the remainder of their lives.

http://www.econlib.org/library/Enc/bios/Schumpeter.html





http://www.econlib.org/library/Enc/FreeMarket.html





Joseph Schumpeter


• Joseph Alois Schumpeter was an Austria-American economist and political scientist, and a

member of the Austrian (Real) School of Economics. He briefly served as Finance Minister of

Austria in 1919. In 1932 he became a professor at Harvard University where he remained

until the end of his career. Schumpeter said that "the process of creative destruction is the

essence of capitalism.”

• 'Creative Destruction' is a term that was coined by Joseph Schumpeter in his work entitled

"Capitalism, Socialism and Democracy" (1942) to denote a "process of industrial mutation

constantly changing the economic structure from within, incessantly destroying the old

economy, incessantly creating a new economy.“

• Disruptive Futurists discover, analyse and interpret the "gales of creative destruction" which

were forecast by Austrian economist Joseph Schumpeter in the 1940s – and are blowing

harder today than ever before. The twin disruptive forces of the globalisation of a dynamic

and chaotic economy coupled with technology-driven innovation are giving birth to emerging

digital markets which generate new business models and revenue streams, novel products

and services – accompanied by clear and present dangers - as well as hidden threats.

Joseph Schumpeter


• Although Schumpeter devoted only a six-page chapter to “The Process of Creative

Destruction,” in which he described CAPITALISM as “the perennial gale of creative destruction,”

it has become the centrepiece of economic thinking on how modern economies evolve.

Schumpeter and the other Austrian School economists who adopt his succinct theory of the

free market’s ceaseless churning - echo capitalism’s critics, such as Karl Marx, in recognising

that lost jobs, ruined companies, and vanishing industries are the result of the inherent

consequences of the disruptive mechanism of economic growth.

• The corollary is that wealth springs eternal from the turmoil and chaos. Those societies that

allow creative destruction to operate in a free market economy with less state intervention –

tend, over time, to be more productive, grow faster and acquire more wealth. In those

societies where increased wealth is retained by Entrepreneurs – such as in the UK and USA

– there has been no real increase in the standard of living of the workforce for nearly sixty

years (forty years in the UK) – as measured by the number of Mars chocolate bars which may

be purchased on the average wage. In those societies where wealth is shared more equally

– such as in Northern Europe – their citizens work in new industries, have access to novel

and innovative products and services, and reap the benefits of shorter working hours, better

health, wealth, education and jobs, along with increased wages and higher living standards.

http://www.econlib.org/library/Enc/Capitalism.html

http://www.econlib.org/library/Enc/Capitalism.html

Joseph Schumpeter


• At the same time, attempts to soften the harsher aspects of creative destruction by trying to

preserve jobs or protect industries will lead to stagnation and decline, short-circuiting the

march of progress. Schumpeter’s enduring insights reminds us that capitalism’s pain and gain

are inextricably linked. The process of creating new industries does not go forward without

sweeping away the pre-existing order. The key enabler for economic transformation is the flow

of wealth from mature and stagnant industries to new and emerging industries - the transfer of

Capital generated by older companies (Cash Cows) into successor companies (Rising Stars).

• 'Creative Destruction‘ occurs when the arrival and adoption of new methods of production

effectively kills off older, established industries. An example of this is the introduction of

personal computers in the 1980's. This new industry, led by Microsoft and Intel, destroyed

many mainframe manufacturers. In doing so, technology entrepreneurs created one of the

most important industries of the 20th century. Personal computers are now being replaced by

devices from agile and innovative companies such as Apple and Samsung. Microsoft and

Nokia are in turn being destroyed - Windows-based smart phones and tablets from Microsoft

and Nokia now cling to less than 3% market share.

Joseph Schumpeter


• Companies show the same pattern of destruction and rebirth over many industrial cycles. Only

five of today’s hundred largest public companies were among the top hundred in 1917. Half of

the top hundred of 1970 had been replaced in the rankings by 2000. The Power of Technology

Innovation – driven by ENTREPRENEURSHIP and competition – drives the process of creative

destruction through the flow of capital from older, stagnant industries to new and emerging

industries. Schumpeter summed up the process of economic transformation as follows: -

• “The fundamental spark that sets up and keeps the economic engine in motion comes from

innovation – arranging existing resources in new and different ways to create novel and

innovative products and services. Entrepreneurial endeavour creates the new consumer

products and services, new markets, innovative methods of production, distribution or transport,

and new forms of industrial organization which drives economic growth.“ (p. 83)

• Entrepreneurs introduce new products and technologies with an eye toward making themselves

better off—the profit motive. New goods and services, new firms, and new industries compete

with existing ones in the marketplace, taking customers by offering lower prices, better

performance, new features, catchier styling, faster service, more convenient locations, higher

status, more aggressive marketing, or more attractive packaging. In another seemingly

contradictory aspect of creative destruction, the pursuit of self-interest ignites the progress that

makes others better off.

http://www.econlib.org/library/Enc/Entrepreneurship.html

http://www.econlib.org/library/Enc/Entrepreneurship.html

Joseph Schumpeter


• Producers survive by streamlining production with newer and better tools that make workers

more productive. Companies that no longer deliver what consumers want at competitive prices

lose customers, and eventually wither and die. The market’s “invisible hand” - a phrase owing

not to Schumpeter but to ADAM SMITH - shifts resources from declining sectors to more valuable

uses as workers, inputs, and financial capital seek their highest returns.

• The source of Joseph Schumpeter's dynamic, change-oriented, and innovation-based

economics was the Historical School of economics. Although Schumpeter’s writings could be

critical of the School, Schumpeter's work on the role of innovation and entrepreneurship can be

seen as a continuation of ideas originated by the Historical School – especially from the work of

Gustav von Schmoller and Werner Sombart. Schumpeter's scholarly learning is readily apparent

in his posthumous publication of the History of Economic Analysis – but many of his views

now appear to be somewhat idiosyncratic – and some even seem to be downright cavalier......

• Schumpeter criticized John Maynard Keynes and David Ricardo for the "Ricardian vice." Ricardo

and Keynes often reasoned in terms of abstract economic models, where they could isolate,

freeze or ignore all but a few major variables. According to Schumpeter, they were then free to

argue that one factor impacted on another in a simple monotonic cause-and-effect fashion. This

has led to the mistaken belief that one could easily deduce effective real-world economic policy

conclusions directly from a highly abstract and simplistic theoretical economic model.

http://www.econlib.org/library/Enc/bios/Smith.html





http://en.wikipedia.org/wiki/Historical_school_of_economics

http://en.wikipedia.org/wiki/Entrepreneurship

http://en.wikipedia.org/wiki/Gustav_von_Schmoller

http://en.wikipedia.org/wiki/Gustav_von_Schmoller

http://en.wikipedia.org/wiki/Werner_Sombart

http://en.wikipedia.org/wiki/Werner_Sombart

http://en.wikipedia.org/wiki/Idiosyncratic

http://en.wikipedia.org/wiki/John_Maynard_Keynes

http://en.wikipedia.org/wiki/David_Ricardo

http://en.wikipedia.org/wiki/Monotonic

Joseph Schumpeter

• Schumpeter thought that the greatest 18th century economist was Turgot, not Adam Smith, as

many economists believe today, and he considered Léon Walras to be the "greatest of all

economists", beside whom other economists' theories were just "like inadequate attempts to

capture some particular aspects of the Walrasian truth".

• Schumpeter's relationships with the ideas of other economists were quite complex - following

the views of neither Walras nor Keynes. There was actually some considerable professional

rivalry between Schumpeter and his peers. Schumpeter starts his most important contribution

to economic analysis The Theory of Economic Development – which describes business

cycles and economic development – with a treatise on circular flow in which he postulates that

slow or stationary economic growth occurs whenever innovation wave input from technology

research and development activities is reduced - or simply ceases. This form of stagnation in

economic development is, according to Schumpeter, described by Walrasian equilibrium.

• In developing the Economic Wave theory, Schumpeter postulated the idea that the

entrepreneur is the primary catalyst of industrial activity which develops in a cyclic fashion

along several discrete and interacting timelines – connecting generation waves with

entrepreneurship and capital funding, technology innovation with manufacturing process

improvements, and industrial investment cycles with economic growth These cycles acts to

stimulate the status-quo in an otherwise stagnant economic equilibrium or stationary economic

growth into a circular flow Thus the true hero of his story is the entrepreneur..

http://en.wikipedia.org/wiki/Anne_Robert_Jacques_Turgot,_Baron_de_Laune

http://en.wikipedia.org/wiki/Adam_Smith

http://en.wikipedia.org/wiki/L%C3%A9on_Walras



http://en.wikipedia.org/wiki/Business_cycles

http://en.wikipedia.org/wiki/Business_cycles

http://en.wikipedia.org/wiki/Circular_flow

http://en.wikipedia.org/wiki/Walrasian_equilibrium



http://en.wikipedia.org/wiki/Entrepreneur

Joseph Schumpeter

• Schumpeter also kept alive the Russian Nikolai Kondratiev's concept of economic cycles in with

50-year periodicity - Kondratiev waves - and by extension, the 100-year cycle of the Century

Wave or Saeculum. Schumpeter suggested an integrated Economic Cycle Model in which the

four main cycles, Kondratiev (54 years), Kuznets (18 years), Juglar (9 years) and Kitchin (about

2-4 years) can be aggregated together to form a composite economic waveform. The economic

wave form series suggested here did not include the Kuznets Cycle simply because Schumpeter

did not recognize it as a valid cycle (see "Business Cycle" for further information). There was

actually some considerable professional animosity between Schumpeter and Kuznets. As far as

the segmentation of the Kondratiev cycle goes, Schumpeter further postulated that a single

Kondratiev cycle might be consistent with the aggregation of three lower-order Kuznets cycles.

• Each Kuznets wave could, itself, be made up of two Juglar waves. Similarly two or three Kitchin

cycles could form a higher-order Juglar cycle. If each of these were in harmonic phase, more

importantly if the downward arc of each was simultaneous so that the nadir (perigee) of each

cycle was coincident - it could explain disastrous economic slumps and their consequential

recessions and depressions. Schumpeter never proposed a rigid, fixed-periodicity model. He

saw that these cycles could vary in length over time - impacted on by various random, chaotic

and radically disruptive “Black Swan” events - catastrophes such as War, Famine and Disease.

http://en.wikipedia.org/wiki/Nikolai_Kondratiev

http://en.wikipedia.org/wiki/Nikolai_Kondratiev

http://en.wikipedia.org/wiki/Kondratiev_wave




http://en.wikipedia.org/wiki/Clement_Juglar

http://en.wikipedia.org/wiki/Kitchin_cycle

http://en.wikipedia.org/wiki/Waveform

http://en.wikipedia.org/wiki/Kuznets_swing

http://en.wikipedia.org/wiki/Business_Cycle

http://en.wikipedia.org/wiki/Nadir


Trends Figure 1. Joseph Schumpter – Variable-length Economic Wave Series

Figure 2. Strauss-Howe – Variable-length Generation Wave Series

Cycle Pre-industrial (before 1860) Modern (post 1929)

Kitchen Inventory Cycle (KI-cycle) Stock-turn Cycle (3-5 years) One KI-cycle – 4.5 years

Juglar Fixed Investment Cycle (J-cycle) Business Cycle (7-11 years) One J-cycle - 9 years

Kuznets Infrastructure Cycle (KU-cycle) Property Cycle (15-25 years) One KU-cycle -18 years

Kondratiev Cycle (KO-cycle) Technology Cycle (45-60 years) One KO-cycle – 54 years

Grand-cycle / Super-cycle (GS-cycle) Saeculum (70 years +) Two KO-cycles - 108 years


Kitchen Cycle (KI-cycle) Production Cycle (3-5 years) Inventory Wave – 4.5 years

Juglar Fixed Investment Cycle (J-cycle) Business Cycle (8-11 years) Economic Wave - 9 years

Kuznets Infrastructure Cycle (KU-cycle) Property Cycle (20-25 years) Infrastructure Wave - 18 years

Strauss-Howe Cycle (SH-cycle) Population Cycle (20-30 years) Generation Wave - 24 years

Grand-cycle / Super-cycle (GS-cycle) Saeculum (70 years +) Century Wave – 96-108 years

Strauss–Howe Generation Wave

• The Strauss–Howe Generation Wave theory, created by authors William Strauss and Neil Howe, identifies a recurring generational cycle in European and American history. Strauss and Howe lay the groundwork for the theory in their 1991 book Generations, which retells the history of America as a series of generational biographies going back to 1584. In their 1997 book The Fourth Turning, the authors expand the theory to focus on a fourfold cycle of generational types and recurring mood eras in American history. Their consultancy, Life Course Associates, has expanded on the concept in a variety of publications since then.

• The Strauss–Howe Generation Wave theory was developed to describe the history of the United States, including the founding 13 colonies and their Anglo-Saxon antecedents, and this is where the most detailed research has been done. However, the authors have also examined generational trends elsewhere in the world and identified similar cycles in several developed countries. The books are best-sellers and the theory has been widely influential and acclaimed in the USA. Eric Hoover has called the authors pioneers in a burgeoning industry of consultants, speakers and researchers focused on generations.

• Academic response to the theory has been somewhat mixed - some American authorities applauding Strauss and Howe for their "bold and imaginative thesis," and others (mostly European) criticizing the theory for the lack of any rigorous empirical evidence for their claims,

and a perception that many aspects of their “one size fits all” argument gloss over real differences within the population of each generation. What is apparent is that Strauss and Howe have failed miserably to grasp the importance of Generation Waves driving Technology Innovation in the economy – instead, referring to weak and insipid hypotheses of “Spiritual Awareness” and suchlike driving Generational Change through the saeculum (Century Wave).

https://en.wikipedia.org/wiki/William_Strauss

https://en.wikipedia.org/wiki/Neil_Howe

https://en.wikipedia.org/wiki/Neil_Howe

https://en.wikipedia.org/wiki/Generation

https://en.wikipedia.org/wiki/American_history

Strauss–Howe Generation Waves

1. Arthurian Generation (1433–1460) (H)

2. Humanist Generation (1461–1482) (A)

3. Reformation Generation (1483–1511) (P)

4. Reprisal Generation (1512–1540) (N)

5. Elizabethan Generation (1541–1565) (H)

6. Parliamentary Generation (1566–1587) (A)

7. Puritan Generation (1588–1617) (P)

8. Cavalier Generation (1618–1647) (N)

9. Glorious Generation (1648–1673) (H)

10. Enlightenment Generation (1674–1700) (A)

11. Awakening Generation (1701–1723) (P)

12. Liberty Generation (1724–1741) (N)

13. Republican Generation (1742–1766) (H)

14. Compromise Generation (1767–1791) (A)

15. Transcendental Generation (1792–1821) (P)

16. Gilded Generation (1822–1842) (N)

17. Progressive Generation (1843–1859) (A)

18. Missionary Generation (1860–1882) (P)

19. Lost Generation (1883–1900) (N)

20. G.I. Generation (1901–1924) (H)

21. Silent Generation (1925–1942) (A)

22. Baby Boom Generation (1943–1960) (P)

23. Generation X (Gen X) (1961–1981) (N)

24. Millennial Generation (Gen Y) (1982–2004) (H)

25. Homeland Generation (Gen Z) (2005-present) (A)

https://en.wikipedia.org/wiki/Lost_Generation

https://en.wikipedia.org/wiki/Greatest_Generation

https://en.wikipedia.org/wiki/Silent_Generation

https://en.wikipedia.org/wiki/Baby_Boom_Generation

https://en.wikipedia.org/wiki/Generation_X

https://en.wikipedia.org/wiki/Millennial_Generation

https://en.wikipedia.org/wiki/Homeland_Generation

Generation and Century Waves Saeculum McLaughlin

Cycle

Spiritual Age High, Awakening,

Secular Crisis

Strauss-Howe

Generation

Generation

Date / Type

1415 - 1514 Pre-Columbian Renaissance

(1517-1539) Retreat from France

Arthurian Generation 1433–1460) (H)

Wars of the Roses

(1455-1487) War of the Roses

Humanist Generation 1461–1482) (A)

High: Tudor Renaissance Reformation Generation 1483–1511) (P)

1515 - 1614 Columbian Reformation

(1517-1539)

Awakening: Protestant

Reformation

Reprisal Generation 1512–1540) (N)

Spanish Armada

(1580-1588) Intolerance and Martyrdom

Elizabethan Generation 1541–1565) (H)

Crisis: Armada Crisis Parliamentary Generation 1566–1587) (A)

High: Merrie England Puritan Generation 1588–1617) (P)

1615 - 1714 Colonial Early Enlightenment

(1610-1640)

English Civil War

(1675-1704)

Cavalier Generation 1618–1647) (N)

Glorious Generation 1648–1673) (H)

Enlightenment Generation 1674–1700) (A)

1715 - 1814 Revolutionary Late Enlightenment

(1730-1760)

American Revolution

(1773-1794)

Awakening Generation 1701–1723) (P)

Liberty Generation 1724–1741) (N)

Republican Generation 1742–1766) (H)

Compromise Generation 1767–1791) (A)

http://en.wikipedia.org/wiki/Hundred_Years'_War

http://en.wikipedia.org/wiki/Wars_of_the_Roses

http://en.wikipedia.org/wiki/English_Renaissance

http://en.wikipedia.org/wiki/Protestant_Reformation


http://en.wikipedia.org/wiki/Spanish_Armada

http://en.wikipedia.org/wiki/Merry_England




Cycle


Secular Crisis

Strauss-Howe

Generation

Generation

Date / Type

1815 - 1914 Victorian Transcendental

(1800-1830)

Napoleonic Wars

(1860-1865)

Transcendental Generation 1792–1821) (P)

Gilded Generation 1822–1842) (N)

Progressive Generation 1843–1859) (A)

Missionary Generation 1860–1882) (P)

Lost Generation 1883–1900) (N)

1915 - 2014 Loss of Empires Missionary Awakening

(1890-1920)

WWI, Depression & WWII

(1929-1946)

G.I. Generation 1901–1924) (H)

Silent Generation 1925–1942) (A)

Baby Boom Generation 1943–1960) (P)

Cold War Baby Boom Awakening

(1960-1980)

Regional Wars, Terrorism,

Insecurity

Generation X (Generation X) 1961–1981) (N)

Millennial 21st century Awakening

(2000 - 2020)


Insecurity

Millennial Generation (Gen Y) 1982–2004) (H)


Insecurity

Homeland Generation (Gen Z) 2005–2025 (A)

2015-2114 Post-Millennial 21st century Apocalypse

(2020 - 2040)

Global Food, Energy and

Water (FEW) Crisis

Apocalyptic Generation (Gen A) 2025–2050 (P)

Post-Apocalyptic

Realisation (2040 - 2060)

Wars, Disease, Famine ,

Terrorism and Insecurity

Post-Apocalyptic Generation

(Gen B)

2050–2070 (N)

Post-Apocalyptic Recovery

(2040 - 2060)



Recovery Generation (Gen C) 2070–2090) (H)








Business Cycles, Patterns and Trends – Innovation and Capital

Complex Market Phenomena are simply: - "the outcomes of endless conscious, purposeful human actions, by countless

individuals exercising personal choices and preferences - each of whom is trying as best they can to optimise their

circumstances in order to achieve various needs and desires. Individuals, through economic activity strive to attain their

preferred outcomes - whilst at the same time attempting to avoid any unintended consequences leading to unforeseen

outcomes.....”

• Ludwig von Mises – Economist •

Horizon and Environment Scanning, Tracking and Monitoring Processes

• Horizon and Environment Scanning Event Types – refer to Weak Signals of any unforeseen,

sudden and extreme Global-level transformation or change Future Events in either the military,

political, social, economic or environmental landscape - having an inordinately low probability of

occurrence - coupled with an extraordinarily high impact when they do occur (Nassim Taleb).

• Horizon Scanning Event Types

– Technology Shock Waves

– Supply / Demand Shock Waves

– Political, Economic and Social Waves

– Religion, Culture and Human Identity Waves

– Art, Architecture, Design and Fashion Waves

– Global Conflict – War, Terrorism, and Insecurity Waves

• Environment Scanning Event Types

– Natural Disasters and Catastrophes

– Human Activity Impact on the Environment - Global Massive Change Events

• Weak Signals – are messages, subliminal temporal indicators of ideas, patterns, trends or

random events coming to meet us from the future – or signs of novel and emerging desires,

thoughts, ideas and influences which may interact with both current and pre-existing patterns

and trends to predicate impact or effect some change in our present or future environment.

HUMAN ACTIVITY CYCLES

SHORT PERIOD HUMAN ACTIVITY WAVES

• Price Curves – short-term, variable Market Trends,

• Seasonal Activities – Farming, Forestry and Fishing

• Trading and Fiscal Cycles – Diurnal to Annual (1 day to 1 year)

MEDIUM PERIOD HUMAN ACTIVITY WAVES – Joseph Schumpter Series

• Kitchin inventory cycle of 3–5 years (after Joseph Kitchin);

• Juglar fixed investment cycle of 7–11 years (often referred to as simply 'the business cycle’);

• Kuznets infrastructural investment cycle of 15–25 years (after Simon Kuznets);

• Generation Wave – 15, 20, 25 or 30 years (four or five per Saeculum and Innovation Wave)

• Innovation Wave – Major Scientific, Technology and Industrial Innovation Cycles of about 80 years

– Sub-Innovation Waves – Minor Technology Innovation Cycles @ 40 years (2 x Kuznets Waves ?)

• Kondratiev wave or long technological cycle of 45–60 years (after Nikolai Kondratiev)

• Saeculum or Century Wave – Major Geo-political rivalry and conflict waves of about 100 years

– Sub-Century Waves – Minor Geo-political Cycles @ 50 years (Kondratiev long technological wave)

HUMAN ACTIVITY CYCLES

LONG PERIOD HUMAN ACTIVITY WAVES

• Culture Moments – Major Human Activity achievements - Technology, Culture and History

• Industrial Cycles –phases of evolution for any given industry at any specific time / location

• Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Information Ages etc.

– Stone – Tools for Hunting, Crafting Artefacts and Making Fire

– Fire – Combustion for Warmth, Cooking and changing the Environment

– Agriculture – Neolithic Age Human Settlements

– Bronze – Bronze Age Cities and Urbanisation

– Ship Building – Communication, Culture and Trade

– Iron – Iron Age Empires, Armies and Warfare

– Gun-powder – Global Imperialism and Colonisation

– Coal – Mining, Manufacturing and Mercantilism

– Engineering – Bridges, Boats and Buildings

– Steam Power – Industrialisation and Transport

– Chemistry – Dyestuff, Drugs, Explosives and Agrochemicals

– Internal Combustion – Fossil Fuel dependency

– Physics – Satellites and Space Technology

– Nuclear Fission – Globalisation and Urbanisation

– Digital Communications – The Information Age

– Smart Cities of the Future – The Solar Age – Renewable Energy and Sustainable Societies

– Nuclear Fusion– The Hydrogen Age - Inter-planetary Human Settlements

– Space-craft Building – The Exploration Age - Inter-stellar Cities and Galactic Urbanisation

Business Cycles, Patterns and Trends – Economic Boom and Bust

Business Cycles, Patterns and Trends – Innovation and Capital

• The purpose of this section is to examine the nature and content of Clement Juglar’s contribution

to Business Cycle Theory and then to compare and contrast it with that of Joseph Schumpeter’s

analysis of cyclical economic fluctuations. There are many similarities evident - but there are

also some important differences between the two competing theories. Schumpeter’s classical

Business Cycle is driven by a series of multiple co-dependent technology innovations of low to

medium impact - whereas according to Juglar the trigger for a runaway bull markets is market

speculation fuelled by the over-supply of credit. A deeper examination of Juglar’s business

cycles can reveal the richness of Juglar’s original and very interesting approach. Indeed Juglar,

without having proposed a complete theory of business cycles, nevertheless provides us with an

original Money Supply theory of economic boom cycles supporting a more detailed comparison

and benchmarking between these two co-existing and compatible theories of business cycles.

• In a specific economic context characterised by the rapid development of both industry and

trade, Juglar's theory interconnects the development of new markets with credit availability for

speculative investments – and the bank’s behaviours in response to the various phases of the

Business Cycle – Crisis, Liquidation, Recovery, Growth and Prosperity, . The way that the

money supply, credit availability and industrial development interact to create business cycles is

quite different in Juglar’s viewpoint than that expressed by Schumpeter in his theory of economic

development – growth driven by innovation - but does not necessarily express any fundamental

contradiction. Entrepreneurs, through innovation , attract capital funding from investors for start-

ups and scale-ups. Compared and contrasted, these two different approaches refer to market

phenomena which are both separate and different – but still entirely compatible and co-existent.

Price Index Inflation

Waves, Cycles, Patterns and Trends

• Business Cycles were once thought to be an economic phenomenon due to periodic fluctuations in economic activity. These mid-term economic cycle fluctuations are usually measured using Real (Austrian) Gross Domestic Product (rGDP). Business Cycles take place against a long-term background trend in Economic Output – growth, stagnation or recession – which affects Money Supply as well as the relative availability and consumption (Demand v. Supply and Value v. Price) of other Economic Commodities. Any excess of Money Supply may lead to an economic expansion or “boom”, conversely shortage of Money Supply (Money Supply shocks – the Liquidity Trap) may lead to economic contraction or “bust”. Business Cycles are recurring, fluctuating levels of economic activity experiences in an economy over a significant timeline (decades or centuries).

• The five stages of Business Cycles are growth (expansion), peak, recession (contraction), trough and recovery. Business Cycles were once widely thought to be extremely regular, with predictable durations, but today’s Global Market Business Cycles are now thought to be unstable and appear to behave in irregular, random and even chaotic patterns – varying in frequency, range, magnitude and duration. Many leading economists now also suspect that Business Cycles may be influenced by fiscal policy as much as market phenomena - even that Global Economic “Wild Card” and “Black Swan” events are actually triggered by Economic Planners in Government Treasury Departments and in Central Banks as a result of manipulating the Money Supply under the interventionist Fiscal Policies adopted by some Western Nations.

Scenario Planning and Impact Analysis

• Many Economists and Economic Planners have widely arrived at the consensus that a large

majority of organizations have yet to develop sophisticated Economic Modelling systems and

integrated their outputs into the strategic planning process. The objective of this paper is to

shed some light into the current state of the business and economic environmental scanning,

tracking, monitoring and forecasting function in organizations Impacted by Business Cycles.

• Major periodic changes in business activity are due to recurring cyclic phases in economic

expansion and contraction - classical “bear” and “bull” markets, or “boom and bust” cycles.

The time series decomposition necessary to explain this complex phenomenon presents us

with many interpretive difficulties – due to background “noise” and interference as multiple

business cycles, patterns and trends interact and impact upon each other. We are now able

to compare cyclical movements in output levels, deviations from trend, and smoothed growth

rates of the principal measures of aggregate economic activity - the quarterly Real (Austrian)

GDP and the monthly U.S. Coincident Index - using the phase average trend (PAT).

• This section provides a study of business cycles - which are defined as periodic sequences of

expansion and contraction in the general level of economic activity. The proposed Wave-

form Analytics approach helps us to identify discrete Cycles, Patterns and Trends in Big Data.

This approach may be characterised as periodic sequences of high and low business activity

resulting in cyclic phases of increased and reduced output trends – supporting an integrated

study of disaggregated economic cycles that does not require repeated multiple and iterative

processes of trend estimation and elimination for every possible business cycle duration..

Economic Waves, Cycles, Patterns and Trends

• Real (Austrian) business cycle theory assigns a central role to shock waves as the primary source of economic fluctuations or disturbances. As King and Rebelo (1999) discuss in .Resuscitating Real Business Cycles, when persistent technology shocks are fed through a standard real business cycle model – then the simulated economy displays impact patterns which are similar to those exhibited by actual business cycles. While the last decade has seen the addition of other types of shocks in these models - such as monetary policy and government spending - none has been shown to be a central impulse to business cycles.

• A trio of recent papers has called into question the theory that technology shocks have anything to do with the fundamental shape of business cycles. Although they use very different methods, Galí (1999), Shea (1998) and Basu, Kimball, and Fernald (1999) all present the same result: positive technology shocks appear to lead to declines in labour input.1 Galí identifies technology shocks using long-run restrictions in a structural VAR; Shea uses data on patents and R&D; and Basu, Kimball and Fernald identify technology shocks by estimating Hall-style regressions with proxies for utilization.

• In all cases, they find significant negative correlations of hours with the technology shock waves, Gail's paper also studies the effects of the non-technology shocks – such as Terrorism, Insecurity and Military Conflicts, as well as Monetary Supply and Commodity-price Shocks - which he suggests might be interpreted as demand / supply shocks. These shocks produce the typical business cycle co-movement between output and hours. In response to a positive shock, both output and hours show a rise in the typical hump-shaped pattern. Productivity also rises - but with only temporarily economic effect – modifying Business Cycles rather than radically altering them.

Economic Waves, Cycles, Patterns and Trends

Wholesale Price Index – 1790-1640

Introduction - Business Cycles,

Patterns and Trends • Prior to widespread international industrialisation (Globalisation), the Kondratiev Cycle (KO-

cycle) represented phases of industrialisation – successive waves of incremental development in

the fields of Technology and Innovation – which, in turn could be resolved into a further series of

nested Population Cycles (Human Generation Waves – popularised by Strauss and Howe). The

economic impact of Generation Waves was at least partially influenced by the generational war

cycle, with its impact on National Fiscal Policy (government finances). Shorter economic cycles

appeared to fit into the longer KO-cycle, rather existing independently - possibly harmonic in

nature. Hence financial panics followed a real estate cycle of about 18 years, denoted as the

Kuznets Cycle (KU-cycle) . Slumps occurring in between the Kuznets cycle at a half-cycle that

were of similar length to the “Boom-Bust” Business Cycles first identified by Clement Juglar.

• Business Cycles were apparently of random length - up to a full Juglar Business Cycle in the

range of 8 to 11 years . With the arrival of industrialisation, the ordinary Business Cycle was

now joined by a new Economic phenomenon – the Inventory Cycle, or Kitchen Cycle (KI-cycle)

with a range of 3-5 years duration – which was later challanged by a new, decreased and lower,

more uniform length (average 40 months). The Kuznets Cycle (KU-cycle) and Kondratiev Cycles

carried on much as before. From the changes induced by industrialisation, the Robert Bronson

SMECT structure emerged, in which sixteen 40 month Kitchen cycles "fit" into a standard

Kondratiev cycle – and the KO-cycle subdivided into 1/2, 1/4 and 1/8-length sub-cycles.

http://csf.colorado.edu/authors/Alexander.Mike/War-Cycle.htm


http://csf.colorado.edu/authors/Alexander.Mike/Image106.gif

Innovation Waves


Trend - Introduction • In his recent book on the Kondratiev cycle, Generations and Business Cycles - Part I -

Michael A. Alexander further developed the idea first postulated by Strauss and Howe - that the

Kondratiev Cycle (KO-cycle) is fundamentally generational in nature. Although it had been 28

years since the last real estate peak in1980 - property valuations had yet to reach previous

peak levels when the Sub-Prime Crisis began in 2006. Just as it had done in 1998 – 2000, the

property boom spawned by the Federal Reserve's rate cuts continued to drive increasing real

estate valuations for a couple of more years -- until finally the Credit Crunch arrived in 2008.

• From late Medieval times up until the early 19th century, the Kondratiev Cycle (KO-cycle) was

thought to be roughly equal in length to two human generation intervals - or approximately 50

years in duration. Thus two Kondratiev cycles in turn form one saeculum, a generational cycle

described by American authors William Strauss and Neil Howe. The KO-cycle was closely

aligned with Technology Arms Races and wars – so a possible mechanism for the cycle was

alternating periods (of generational length) featuring government debt growth and decline

associated with war finance. After the world economy became widely industrialised in the late

19th century – the relation between the cycles seem to have changed. Instead of two KO-

cycles per saeculum – Alexander claimed that there was now only found to be one.

• Such theory-driven Deterministic attempts to fit the observed Economic Data into fixed-length

hypothetical Business Cycles or Economic Waves – are doomed to failure. Much better

results are obtained from data-driven Probabilistic approaches – let the Data define the Cycles.

http://www.amazon.com/exec/obidos/tg/detail/-/0595217117



http://www.safehaven.com/author/7/michael-a-alexander

http://www.fourthturning.com/html/history___turnings.html

http://www.fourthturning.com/html/generations___archetypes.html


http://csf.colorado.edu/authors/Alexander.Mike/Image106.gif

Innovation Waves


Trends Figure 3. Robert Bronson's Deterministic SMECT System of Fixed-length Cycle Periodicity

Figure 4. Michael Alexander – Fixed-length Business Cycle and Bear Market Cycle Periodicity


Juglar Cycle (J-cycle) Business Cycle (8-11 years) Economic Wave - 9 years

K0-trend / Infrastructure Wave Property Cycle (20-25 years) Infrastructure Wave - 18 years

K0-wave / Generation Wave Population Cycle (20-30 years) Generation Wave - 36 years

K0-cycle / Innovation Wave Technology Cycle (45-60 years) Innovation Wave - 72 years

Grand-cycle / Super-cycle (GS-cycle) Saeculum (70 years +) Century Wave - 108 years


Kitchen Cycle (KI-cycle) Production Cycle (3-5 years) Inventory Wave- 40 months

Juglar Cycle (J-cycle) Business Cycle (8-11 years) Economic Wave - 9 years

Kuznets Cycle (KU-cycle) Property Cycle (20-25 years) Infrastructure Wave -18 years

Strauss-Howe Cycle (SH-cycle) Population Cycle (20-30 years) Generation Wave - 36 years

Kondratiev Cycle (KO-cycle) Technology Cycle (45-60 years) Innovation Wave - 72 years

http://www.safehaven.com/article/85/showarticle.cfm?id=87

Periodicity - Business Cycles,

Patterns and Trends • Economic Periodicity appears less metronomic and more irregular from 1860 to 1929 (and

from 2000 onwards). Strauss and Howe claim that these changes in Economic Periodicity

were created by a shift in economic cycle dynamics caused by industrialisation around the

time of the American Civil War – hinting towards Schumpter’s view that Innovation and

Black Swan events can impact on Economic Cycle periodicity. Michael Alexander claims

that this new pattern only emerged after1929 – when the Kondratiev Cycle (KO-cycle)

appeared lengthened and at the same time the Saeculum shortened - to the point where

they both became roughly equal, and merged with a Periodicity of about 72 years long.....

• Michael Alexander further maintains that each Kondratiev wave can be subdivided into two

Kondratiev seasons, each associated with a secular market trend. Table 1 shows how

these cycles were related to each other before and after industrialization. The Kondratiev

cycle itself consists of two Kondratiev waves, each of which is associated with sixteen

occurrences or iterations of the Stock Cycle. The Juglar cycle was first noted by Clement

Juglar in 1860’s and existed in pre-industrial economies. The other two cycles were

identified much later (Kitchen in 1923). The Kuznets real-estate cycle, proposed in 1930,

still persists and this might be thought of as a periodic infrastructure investment cycle

which is typical of industrialised economies after the 1929 Depression. Shorter economic

cycles also exist, such as the Kuznets cycle of 15-20 years (related to building/real estate

valuation cycles), along with the Juglar cycle of 7-11 years (related to Stock Market

activity) and the Kitchen cycle of about 40 months (related to Stock or Inventory Cycles).



http://cepa.newschool.edu/het/profiles/kuznets.htm

http://cepa.newschool.edu/het/profiles/juglar.htm

http://johnlowe.net/Academic/Tech/Tech.htm

Economic Models

• At the onset of the Great Depression 1927-29, many economists believed that : -

“left alone, markets were self-correcting and would return to an ‘equilibrium’ that efficiently

utilised capital, workers and natural resources… this was the inviolate and core axiom of

‘scientific economics’ itself…

• A month after the Great Crash, economists at Harvard University, had made a statement (from

Richard Parker - John Kenneth Galbraith: his life, politics and economics, 2005, p.12) that : -

“a severe depression like that of 1920-21 is outside the range of probability.”

• They could not have been more wrong. In a new theory, Neo-liberal Keynesianism, which

emerged with the publication of John Maynard Keynes’ “The General Theory of Employment,

Interest and Money.” - Keynes had made use of a radically different set of assumptions, which

could lead to a startling new possibility of an alternative economic equilibrium consisting of

simultaneous high unemployment and low income – a stark and different equilibrium condition

where the economy could be forced into a deep state of inefficient economic equilibrium - or

stagnation - where the economy would stagnate (get stuck in a deep trough) – a condition from

which it was very difficult to escape. In Neo-classical Economic theory – this economic condition

was thought to be both implausible and impossible.

Economic Modelling and Long-range

Forecasting – Boom and Bust • The way that we think about the future must mirror how the future actually

unfolds. We have learned from recent experience, that the future is not a straightforward extrapolation of simple, single-domain trends. We now have to consider ways in which random, chaotic and radically disruptive events may be factored into enterprise threat assessment and risk management frameworks - and incorporated into enterprise decision-making structures and processes.

• Economic Modelling and Long-range Forecasting is driven by Data Warehouse Structures and Economic Models containing both Historic (up to 20 years daily closing prices for LNG and all grades of crude) and Future values (daily forecast and weekly projected price curves, monthly and quarterly movement predictions, and so on for up to 20 years into the future – giving a total timeline of 40-year (+ / - 20 years Historic and Future trends summary, outline movements and highlights). Forecast results are obtained using Economic Models - Quantitative (Technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis - Economic growth . contraction and Recession / Depression shapes along with Commodity Price Curve Data Sets) – in turn driving Qualitative (Narrative) Scenario Planning and Impact Analysis techniques.

Robert Bronson's SMECT Forecasting Model

Each thing is of like form from everlasting and comes round again in its cycle - Marcus Aurelius

Alongside Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves - is Robert

Bronson's SMECT Forecasting Model - which integrates both multiple Business and Stock-Market

Cycles into its structure.....

Robert Bronson SMECT System

• Alongside Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves is Robert Bronson's SMECT Forecasting Model - which Integrates Multiple Business and Stock-Market Cycles in its structure.. After 1933, the Kondratiev cycle, representing Technology and Innovation Waves still persisted - but its length gradually increased to about 72 years - as it remains today. The Kuznets real estate cycle continued, but was much weaker for about 40 years until the 1970's when something like the old cycle was reactivated again in the economy.

• A number of ears ago, Bob Bronson, principal of Bronson Capital Markets Research, developed a useful model for predicting certain aspects of the occurrence characteristics of both Business cycles (stock-market price curves) and Economic cycles (Fiscal Policies). The template for this model graphically illustrates that the model not only explains the interrelationship of these past cycles with a high degree of accuracy - a minimum condition for any meaningful modelling tool, but it also has been, and should continue to be, a reasonably accurate forecasting mechanism.

• Robert Bronson's SMECT System is a Forecasting Model that integrates multiple Business (Stock-Market Movement) and Economic Cycles. Since there is an obvious interrelationship between short-term business cycles and short-term stock-market cycles, it is useful to be able to discover and understand their common elements - in order to develop an economic theory that explains the underlying connections between them and, in our case, to form meaningful, differentiating forecasts - especially over longer-term horizons. By pulling back from the close-up differences and viewing the cycles from a longer-term perspective, their common features become more apparent , Business Cycles are also subject to unexpected impact from external or “unknown” forces - Random Events – which are analogous to Uncertainty Theory in the way that they become manifest - but are subject to different interactions and feedback mechanisms.



Robert Bronson SMECT System

• It is a well-know and widely recognised phenomenon that stock market movements are

the single best short-term economic indicator. Dynamic stock market movements

anticipate the phases of short-term business cycles. Although there have been bear

markets which were not followed by recessions, there has never been a U.S. recession

that was not preceded by a bear market. Since 1854, there have been 33 recessions,

as determined by the National Bureau of Economic Research (NBER) - each economic

contraction always preceded by a bear stock market "anticipating" it. Most relevant for

our purposes, the stock market also anticipated the end of each recession with bear-

market lows, or troughs – occurring on average six months before economic growth in

consecutive quarters signalled the official end of those recessions.

• An alternative thesis proposed Strauss and Howe has also noted the discontinuous

behaviour of their Generation Waves at the same time – the so-called “War Anomaly”.

What is happening here ? Strauss and Howe attribute these changes to a skipped or a

“lost generation” caused by catastrophic human losses in the American Civil War - and

later, the Great War. The unusually poor economic outcomes after these conflicts may

be due to massive War Debts and the absence of economic stimulation through

Entrepreneurship and Innovation – caused by the absence of a “lost generation”.


Wave-form Analytics in Econometrics

Wave-form Analytics

Track and Monitor

Investigate and

Analyse

Scan and Identify

Separate and Isolate

Communicate Discover

Verify and Validate Disaggregate

Background Noise

Individual Wave

Composite Waves

Wave-form Characteristics


• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in a living organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

• The Wigner-Gabor-Qian (WGQ) spectrogram method demonstrates a distinct

capability for identifying revealing multiple and complex superimposed cycles or

waves within dynamic, noisy and chaotic time-series data sets – without the need

for using repetitive individual wave-form estimation and elimination techniques.


• Wave-form Analytics – characterised as periodic sequences of regular, recurring

high and low activity resulting in cyclic phases of increased and reduced periodic

trends – supports an integrated study of complex, compound wave forms in

order to identify hidden Cycles, Patterns and Trends in Economic Big Data.

• The existence of fundamental stable characteristic frequencies found within large

aggregations of time-series economic data sets (“Big Data”) provides us with

strong evidence and valuable insights about the inherent structure of Business

Cycles. The challenge found everywhere in business cycle theory is how to

interpret very large scale / long period compound-wave (polyphonic) time series

data sets which are in nature dynamic (non-stationary) such as the Schumpter

Economic Wave series - Kitchen, Juglar, Kusnets, Kondriatev - along with other

geo-political and economic waves - the Saeculum Century Wave and Strauss /

Howe Generation Waves.


Schumpter Economic Wave series: -

1. Kitchen Inventory Cycle - 1.5 - 3 years

2. Juglar Business Cycle - 7 - 11 years

3. Kusnets Technology Innovation Cycle - 20-25 years

4. Kondriatev Infrastructure Investment cycle - 40-50 years

Strauss / Howe Generation Waves

1. Generation Waves - 18-25 years

2. The Saeculum - 80-100 years

Black Swan Event Types – Fiscal Shock Waves

1. Money Supply Shock Waves

2. Commodity Price Shock Waves

3. Sovereign Debt Default Shock Waves

Wave-form Analytics in Econometrics The generational interpretation of the post-depression era

• The generational model holds that the Kondriatev Infrastructure Investment Cycle (K-cycle ) has

shifted from one-half to a full saeculum in length as a result of industrialization and is now about 72

years long. The cause of this lengthening is the emergence of government economic management,

which itself is a direct effect of industrialization as mediated through the generational saeculum

cycle.


The generational interpretation of the post-depression era

• The generational model holds that the Kondriatev Infrastructure Investment Cycle (K-cycle ) has shifted from one-half to a full saeculum in length as a result of industrialization and is now about 72 years long. The cause of this lengthening is the emergence of government economic management, which itself is a direct effect of industrialization as mediated through the generational saeculum cycle. The rise of the industrial economy did more than simply introduce the Kitchen cycle. It also increased the intensity of the generation- related Kitchen, Kuznets and Kondratiev cycles - all of which had already been part of the pre-industrial economy.

• Thus, while the Kuznets-related Panic of 1819 was the first panic to make it into the history books, it was a pretty mild bear market. The Panic of 1837 was worse and the one in 1857 worse yet. The Panic of 1873 ushered in the second worst bear market of all time. The depression following the Panic of 1893 was the worst up to that time. This depression was the first to take place with a majority of the population involved in non-agricultural occupations. Although hard times on the farm were a frequent occurrence, depressions did not usually mean hunger. Yet for the large numbers of urban workers thrown onto "the industrial scrap heap" the depression of the 1890's produced a level of suffering unprecedented for a business fluctuation.

Saeculum or Century Waves

• Long-term Economic

and Geopolitical Wave

Series – 50-100 years

• Regional and Global

Geopolitical Rivalry –

Human Conflict fuelling

Technology Arms Races

• Entrepreneurial-driven

Generation Waves

creating Technology

Innovation and driving

Economic Growth.

Natural v. Human Activity Cycles

• It seems entirely possible, even probable, that much Periodic Human Activity – Business,

Economic, Social, Political, Historic and Pre-historic (Archaeology) Human Activity Cycles

– may be compatible with, and map onto ,one or more of the Natural Periodic Cycles.: -

• Terrestrial Lunar and Solar Natural Cycles - Diurnal to Annual (1 day to 1 year)

– Tidal Deposition Lamellae in Deltas, Estuaries and Salt Marshes – Diurnal

– Seasonal Growth rings in Stromatolites, Stalagmites and Trees - Annual / Biannual

– Lamellae in Ice Cores, Calcite Deposits, Lake and Marine Sediments – Annual / Biannual

• Human Activity - Annual Cycles –

– Daily / Seasonal Agriculture, Trading and Ritual Cycles – Diurnal to Annual (1 day to 1 year)

• Short Period Natural Resonance / Harmonic / Interference Waves –

– Southern Oscillation / Lunar, / Solar Activity @ 3, 5, 7,11 and 19 years

• Schumpeter Composite Economic Wave Series -

– Resonance / Harmonic Wave Cycles @ 3, 5, 7,11 & 15, 20, 25 years

– Kitchin inventory cycle of 3–5 years (after Joseph Kitchin);

– Juglar fixed investment cycle of 7–11 years (often referred to as 'the business cycle’);

– Kuznets infrastructural investment cycle of 15–25 years (after Simon Kuznets);

Natural v. Human Activity Cycles

• It appears that many Human Activity Cycles - Business, Social, Political, Economic, Historic and Pre-

historic (Archaeology) Cycles - may be compatible with, and map onto the twenty-six iterations of

Dansgaard Oeschger and Bond Cycles Climatic Series with major periodicity 1470 years (and 800

to 1000 years) Oceanic Climate Forcing - Bond Climatic Cycles - 1470 years (and 800 to 1000 years) –

• Solar Climate Forcing - Milankovitch Cycles – Solar Insolation driving Pleistocene Ice Ages: –

– Neanderthal Culture

– Solutrean Culture

– Clovis Culture

– Neolithic Agricultural Revolution

• Oceanic Climate Forcing - Dansgaard-Oeschger and Bond Cycles - driving the duration of Civilisations

– Bronze Age City States

– Iron Age Mercantile Armies and Empires

– Western Roman Empire (300 BC – 500 AD

– Eastern Roman Empire (500 – 1300 AD)

– Islamic Empire – (800 - 1300 AD)

– Vikings and Normans - Nordic Ascendency (700-1500 AD) (Medieval “mini Ice Age”)

– The Anglo-French Rivalry – Norman Conquest to Entente Cordial (1066 -1911)

– Pre-Columbian Americas – Mayan, Inca and Aztec Civilisations

– Pueblo Indians (Anastasia) – drought in South-Western USA

– Asian Civilisation – Han, Chin, Ming Chinese Dynasties, Aryan, Mongol and Khmer (Amkor)

– Pacific – Polynesian Expansion – from Hawaii to Easter Island and New Zealand

Wave Theory Of Human Activity

• Wave-Form Analytics and Cycle Mapping - It also appears that many Human Activity

Cycles - Social, Business, Political, Economic, Historic & Archaeology (Pre-historic)

Cycles - may be compatible with, and map incrementally onto one another, over time .....

• Schumpter Business Cycles –

– Kitchen, Juglar, and Kuznets Business Cycles map onto

– Strauss and Howe Generation Wave Series (20-25 years)

• Industry Cycles –

– Strauss and Howe Generation Wave Series (20-25 years) which map onto

– Innovation waves (40-80 years) - and Generation Waves may also map onto

– Kondratiev - long technology innovation investment cycle (50 years)

• Economic Waves –

– Kondratiev long infrastructure investment cycle (50 years) maps onto

– Saeculum Century Waves – Geo-political cycles (100 years)

• Saeculum Century Waves –

– Saeculum Century Waves – Geo-political cycles map onto Civilisations (variable)

– Civilisations (variable) map onto Technology Shock waves (variable)

• Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Wind Power, Water Power,

Steam Power, Internal Combustion, Nuclear Fission, Nuclear Fusion etc.

Human Activity Cycles

SHORT PERIOD HUMAN ACTIVITY WAVES

• Price Curves – short-term, variable Market Trends,

• Seasonal Activities – Farming, Forestry and Fishing

• Trading and Fiscal Cycles – Diurnal to Annual (1 day to 1 year)

MEDIUM PERIOD HUMAN ACTIVITY WAVES – Joseph Schumpter Series

• Kitchin inventory cycle of 3–5 years (after Joseph Kitchin);

• Juglar fixed investment cycle of 7–11 years (often referred to as 'the business cycle’);

• Kuznets infrastructural investment cycle of 15–25 years (after Simon Kuznets);

• Generation Wave – 15, 20, 25 or 30 years (four or five per Saeculum and Innovation Wave)

• Innovation Wave – Major Scientific, Technology and Industrial Innovation Cycles of about 80 years

– Sub-Innovation Waves – Minor Technology Innovation Cycles @ 40 years (2 x Kuznets Waves ?)

• Kondratiev wave or long technological cycle of 45–60 years (after Nikolai Kondratiev)

• Saeculum or Century Wave – Major Geo-political rivalry and conflict waves of about 100 years

– Sub-Century Waves – Minor Geo-political Cycles @ 50 years (Kondratiev long technological wave)


• Saeculum or Century Waves – Human Conflict, Technology and Innovation waves

– Industrial / Technology Arms Race Cycles – 25 year cycles (four per Saeculum)

• American Civil War 1863

• Anglo-Chinese Opium War - 1888

• The Great War - 1914

• The Second World War – European Theatre 1939

– Geo-political Rivalry and Conflict – 20 year cycles (four per Saeculum)

– Olympics Years - even decades

• The Second World War – Pacific Theatre 1940

• Malayan Emergency - 1960

• Russian War in Afghanistan - 1980

• Balkan Conflict - 2000

• Culminating in a future Middle East Conflict before 2020 ?

– Geo-political Rivalry and Conflict – 20 year cycles (four per Saeculum)

– World Cup years - odd decades

• Korean War - 1950

• Vietnam War - 1970

• 1st Gulf War - 1990

• “Arab Spring” Uprisings - 2010

• Culminating in a future Trade War between USA and China before 2030 ?





















20. G.I. Generation (1901–1924) (H)






• Industrial / Technology Arms Races – 25 years

– American Civil War - 1863

– Anglo-Chinese Opium War - 1888

– The Great War - 1914

– The Second World War – 1939

• Geo-political Rivalry and Conflict – 20 years

(Olympic Games Years - even decades)

– The Second World War - 1940

– Malayan Emergency - 1960

– Russian War in Afghanistan - 1980

– Balkan Conflict – 2000

– Culminating in a future Middle East Conflict by

2020 ?


(Football World Cup years - odd decades)

– Korean War - 1950

– Vietnam War - 1970

– 1st Gulf War - 1990

– “Arab Spring” Uprisings – 2010

– Culminating in a future Trade War between USA

and China by 2030 ?

Generation and Century Waves – Human Conflict:- Technology and Innovation waves








Human Activity Cycles

LONG PERIOD HUMAN ACTIVITY WAVES

• Culture Moments – Major Human Activity achievements - Technology, Culture and History

• Industrial Cycles –phases of evolution for any given industry at a specific location / time

• Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Information Ages etc.

– Stone – Tools for Hunting, Crafting Artefacts and Making Fire

– Fire – Combustion for Warmth, Cooking and changing the Environment

– Agriculture – Neolithic Age Human Settlements

– Bronze – Bronze Age Cities and Urbanisation

– Ship Building – Communication, Culture and Trade

– Iron – Iron Age Empires, Armies and Warfare

– Gun-powder – Global Imperialism and Colonisation

– Coal – Mining, Manufacturing and Mercantilism

– Engineering – Bridges, Boats and Buildings

– Steam Power – Industrialisation and Transport

– Chemistry – Dyestuff, Drugs, Explosives and Agrochemicals

– Internal Combustion – Fossil Fuel dependency

– Physics – Satellites and Space Technology

– Nuclear Fission – Globalisation and Urbanisation

– Digital Communications – The Information Age

– Smart Cities of the Future – The Solar Age – Renewable Energy and Sustainable Societies

– Nuclear Fusion– The Hydrogen Age - Inter-planetary Human Settlements

– Space-craft Building – The Exploration Age - Inter-stellar Cities and Galactic Urbanisation

• A saeculum is equivalent of the complete renewal of a human population - or a length

of time roughly equal to the potential lifetime of the longest-lived person in a generation.

The term was first used by the Etruscans. Originally it meant the period of time from the

moment that something happened (for example the founding of a city) until the point in

time that all people who had lived at the first moment or founding event of a saeculum -

had died. At this point a new saeculum would start – marked by a new founding event.

According to legend, the gods had allotted a certain number of saecula to every nation

or civilization; the Etruscans themselves, for example, had been given ten saecula.

• By the 2nd century BC, Roman historians were using the saeculum to measure out

historic periodicity in their chronicles - and to track wars. At the time of the reign of

emperor Augustus, the Romans decided that a saeculum was 110 years. In 17 BC

Caesar Augustus organised Ludi saeculares ('century-games') for the first time to

celebrate the 'fifth saeculum of Rome'. Later emperors like Claudius and Septimius

Severus have celebrated the passing of saecula with games at irregular intervals. In

248, Philip the Arab combined Ludi saeculares with the 1000th anniversary of

the founding of Rome 'ab urbe condita'. The new millennium that Rome entered was

called the Saeculum Novum, a term that had a metaphysical connotation in Christianity,

referring to the worldly age (hence the term secular)

Saeculum - Century Waves

http://en.wikipedia.org/wiki/Etruscan_civilization

http://en.wikipedia.org/wiki/Legend

http://en.wikipedia.org/wiki/Augustus

http://en.wikipedia.org/wiki/Classical_Rome

http://en.wikipedia.org/wiki/Secular_games



http://en.wikipedia.org/wiki/Claudius

http://en.wikipedia.org/wiki/Septimius_Severus



http://en.wikipedia.org/wiki/Philip_the_Arab

http://en.wikipedia.org/wiki/Founding_of_Rome

http://en.wikipedia.org/wiki/Ab_urbe_condita





http://en.wikipedia.org/wiki/Millennium

http://en.wikipedia.org/wiki/Metaphysics

http://en.wikipedia.org/wiki/Christianity

http://en.wikipedia.org/wiki/Secularity

Saeculum – Strauss & Howe Generation Type Birth years Formative era

Late Medieval Saeculum

Arthurian Generation Hero (Civic) 1433-1460 (27) Unravelling: Retreat from France

Humanist Generation Artist (Adaptive) 1461–1482 (21) Crisis: War of the Roses

Reformation Saeculum (104)

Reformation Generation Prophet (Idealist) 1483–1511 (28) High: Tudor Renaissance

Reprisal Generation Nomad (Reactive) 1512–1540 (28) Awakening: Protestant Reformation

Elizabethan Generation Hero (Civic) 1541–1565 (24) Unraveling: Intolerance and Martyrdom

Parliamentary Generation Artist (Adaptive) 1566–1587 (21) Crisis: Armada Crisis

New World Saeculum (112)

Puritan Generation Prophet (Idealist) 1588–1617 (29) High: Merrie England

Cavalier Generation Nomad (Reactive) 1618–1647 (29) Awakening: Puritan Awakening

Glorious Generation Hero (Civic) 1648–1673 (25) Unraveling: Reaction and Restoration

Enlightenment Generation Artist (Adaptive) 1674–1700 (26) Crisis: King Philip's War, Glorious Revolution

Revolutionary Saeculum (90)

Awakening Generation Prophet (Idealist) 1701–1723 (22) High: Augustan Age of Empire

Liberty Generation Nomad (Reactive) 1724–1741 (17) Awakening: Great Awakening

Republican Generation Hero (Civic) 1742–1766 (24) Unraveling: French and Indian War

Compromise Generation Artist (Adaptive) 1767–1791 (24) Crisis: American Revolution

Civil War Saeculum (67)

Transcendental Generation Prophet (Idealist) 1792–1821 (29) High: Era of Good Feeling

Gilded Generation Nomad (Reactive) 1822–1842 (20) Awakening: Transcendental Awakening

Progressive Generation Hero (Civic) 1843–1859 (16) Unravelling: Slavery abolished - British Empire

Missionary Generation Artist (Adaptive) 1860–1882 (22) Crisis: American Civil War









http://en.wikipedia.org/wiki/Puritan_Awakening

http://en.wikipedia.org/wiki/King_Philip's_War


http://en.wikipedia.org/wiki/Glorious_Revolution


http://en.wikipedia.org/wiki/Augustan_literature

http://en.wikipedia.org/wiki/First_Great_Awakening

http://en.wikipedia.org/wiki/French_and_Indian_War

http://en.wikipedia.org/wiki/American_Revolution

http://en.wikipedia.org/wiki/Era_of_Good_Feeling

http://en.wikipedia.org/wiki/Second_Great_Awakening

http://en.wikipedia.org/wiki/American_Civil_War

Saeculum – Strauss & Howe Generation Type Birth years Formative era

Great Power Saeculum (85)

Missionary Generation Prophet (Idealist) 1860–1882 (22) High: Reconstruction/Gilded Age

Lost Generation Nomad (Reactive) 1883–1900 (17) Awakening: Missionary Awakening

G.I. Generation Hero (Civic) 1901–1924 (23) Unravelling: World War I/Prohibition

Silent Generation Artist (Adaptive) 1925–1942 (17) Crisis: Great Depression/World War II

Millennial Saeculum (65+)

Baby Boom Generation Prophet (Idealist) 1943–1960 (17) High: Superpower America

Generation X1

"13th Generation" Nomad (Reactive) 1961–1981 (20) Awakening: Consciousness Revolution

Millennial Generation2 Hero (Civic) 1982–2004 (22) Unravelling: Culture Wars, Postmodernism

Homeland Generation3,4 Artist (Adaptive) 2005–present Crisis: Climate Change, War on Terror,

Global Financial Crisis

The current saeculum runs from the start of WWI in 1914 and so ends in 2015 – the same

time as the current 50-year Kondriatev Wave also ends. The new saeculum can mark the

beginning of a new period of unprecedented growth and prosperity – or global crisis. Strauss

and Howe have defined all of the saeculae over the past 600 years based on Anglo-American

history, from the start of the Protestant Reformation until the present day. In common usage,

a saeculum is not usually allocated to any fixed time period, but any duration from 80 up to

100 years. Saeculae may be divided into four "seasons" or generations of 15-30 years each;

Strauss and Howe represent these seasons as youth, rising adulthood, midlife, and old age.

The basis of the Strauss and Howe saeculum definition is, however, somewhat debatable.....

http://en.wikipedia.org/wiki/Reconstruction_era_of_the_United_States

http://en.wikipedia.org/wiki/Gilded_Age

http://en.wikipedia.org/wiki/Lost_Generation

http://en.wikipedia.org/wiki/Third_Great_Awakening

http://en.wikipedia.org/wiki/G.I._Generation

http://en.wikipedia.org/wiki/World_War_I

http://en.wikipedia.org/wiki/Prohibition_in_the_United_States

http://en.wikipedia.org/wiki/Silent_Generation

http://en.wikipedia.org/wiki/Great_Depression_in_the_United_States

http://en.wikipedia.org/wiki/World_War_II

http://en.wikipedia.org/wiki/Baby_Boom_Generation

http://en.wikipedia.org/wiki/Generation_X

http://en.wikipedia.org/wiki/Strauss%E2%80%93Howe_generational_theory

http://en.wikipedia.org/wiki/Consciousness_Revolution

http://en.wikipedia.org/wiki/Millennial_Generation


http://en.wikipedia.org/wiki/Culture_War


http://en.wikipedia.org/wiki/Postmodernism

http://en.wikipedia.org/wiki/Homeland_Generation



http://en.wikipedia.org/wiki/Climate_Change

http://en.wikipedia.org/wiki/Climate_Change

http://en.wikipedia.org/wiki/War_on_Terror

http://en.wikipedia.org/wiki/War_on_Terror

http://en.wikipedia.org/wiki/Global_Financial_Crisis


• In their book Generations, William Strauss and Neil Howe introduce a fascinating theory

that interprets the whole of Western history in terms of a repeating series of four basic

types of generations. Innovation Generations create technology, which drives economies,

and the wealth created in turn influences the social and political ambitions of their peers. In

their follow-up work, The Fourth Turning, Strauss and Neil Howe propose that history

moves in long cycles or waves, each of four or five generations duration, which they call

the saeculum, after the ancient Etruscan cycle of a similar length. The saeculum contains

four or five periods, called turnings, or a sequence of generations - each associated with

a unique set of Technology Shock Waves - a clustered series of technology Innovations

that are discovered, developed, exploited, plateau and are then replaced and phased out.

• The Lost generation, born at the end of the nineteenth century, and Generation X have

similar peer personalities, making them the same generation type. The Lost generation

were the conservative elders of the Edwardian Period, who tended to be conservative not

because they were old - but because they had been born into a more conservative society.

Similarly, today's elder generation are more liberal because they were born (baby boom)

and grew up (1960s) in a more liberal post-war society. Strauss and Howe might argue

that the move towards the political right over the last couple of decades, and the liberal era

before that - simply reflect the impact of different combinations of generations in the adult

stages of life occupying the Power-bases in Political, Economic and Social Structures.


• Table 2 illustrates this by comparing Strauss and Howe Social Moment turnings (the period

of generational length that encompass Social Moments) with McLoughlin's awakenings.

Strauss and Howes’ Awakening turnings are located 16-27 years from the nearest secular

crisis with an average spacing of 23 years, close to their standard 22 year generation.

• In contrast, McLoughlin's dates are located 6-35 years from the nearest secular crisis and

can hardly be said to be spaced a generation apart from crisis eras. That is, a saeculum

which is defined by McLoughlin Awakenings isn't very regular - suggesting either that such a

regular century cycle may not exist - or at least cannot easily be revealed by a simplistic

survey of a timeline of major historical events.....

Saeculum Spiritual Awakenings Secular Crises*

Strauss and Howe* McLoughlin6

1515 - 1614 1621-1649 1610-1640 1569-1594

1615 - 1714 1727-1746 1730-1760 1675-1704

1715 - 1814 1822-1844 1800-1830 1773-1794

1815 - 1914 1886-1908 1890-1920 1860-1865

1915 - 2014 1964-1984 1960-0000 1929-1946

Social Generations

• Strauss and Howe define a social generation as the aggregate of all people born

over a span of roughly twenty years or about the length of one phase of life:

childhood, young adulthood, midlife, and old age. Generations are identified

(from first year-of-birth to last) by looking for cohort groups of this length that

share three criteria. First, members of a generation share what the authors call

an age location in history: they encounter key historical events and social trends

while occupying the same phase of life. In this view, members of a generation

are shaped in lasting ways by the eras they encounter as children and young

adults and they share certain common beliefs and behaviours. Aware of the

experiences and traits that they share with their peers, members of a generation

would also share a sense of common perceived membership in that generation.

• Strauss and Howe say they based their definition of a generation on the work of

various writers and social thinkers, from ancient writers such as Polybius and Ibn

Khaldun to modern social theorists like José Ortega y Gasset, Karl Mannheim,

John Stuart Mill, Émile Littré, Auguste Comte, and François Mentré.[19]

http://en.wikipedia.org/wiki/Generation

http://en.wikipedia.org/wiki/Childhood

http://en.wikipedia.org/wiki/Young_adult_(psychology)

http://en.wikipedia.org/wiki/Middle_age

http://en.wikipedia.org/wiki/Old_age

http://en.wikipedia.org/wiki/Demographics

http://en.wikipedia.org/wiki/Polybius

http://en.wikipedia.org/wiki/Ibn_Khaldun



http://en.wikipedia.org/wiki/Jos%C3%A9_Ortega_y_Gasset

http://en.wikipedia.org/wiki/Jos%C3%A9_Ortega_y_Gasset

http://en.wikipedia.org/wiki/Karl_Mannheim

http://en.wikipedia.org/wiki/John_Stuart_Mill

http://en.wikipedia.org/wiki/%C3%89mile_Littr%C3%A9



http://en.wikipedia.org/wiki/Auguste_Comte





Saeculum Spiritual Awakenings Secular Crises*

Strauss and Howe* McLoughlin6

1515 - 1614 1621-1649 1610-1640 1569-1594

1615 - 1714 1727-1746 1730-1760 1675-1704

1715 - 1814 1822-1844 1800-1830 1773-1794

1815 - 1914 1886-1908 1890-1920 1860-1865

1915 - 2014 1964-1984 1960-0000 1929-1946

Saeculum Strauss-Howe

Cycle

Spiritual Awakening Secular Crisis

1415 - 1514 Pre-Columbian Renaissance (1517-1539) Wars of the Roses (1455-1487)

1515 - 1614 Columbian Reformation (1517-1539) Spanish Armada (1580-1588)

1615 - 1714 Colonial Puritan Awakening (1621-1640) Glorious Revolution (1675-1692)

1715 - 1814 Revolutionary Great Awakening (1734-1743) American Revolution (1773-1789)

1815 - 1914 Victorian Transcendental Awakening (1822-1837) American Civil War (1857-1865)

1915 - 2014 Great Power Missionary Awakening (1886-1903) WWI, Depression & WWII (1932-1945)

Cold War Baby Boom Awakening (1967-1980) Regional War, Terrorism and Insecurity

Millennial Post-Cold War Awakening (2000-2014) Regional War, Terrorism and Insecurity

Generational Archetypes and Turnings

Turnings • While writing Generations, Strauss and Howe discovered a pattern in the historical generations they examined which

revolved around generational events which they call turnings. In Generations, and in greater detail in The Fourth Turning,

they identify the four-stage cycle of social or mood eras (i.e. turnings).

High • According to Strauss and Howe, the First Turning is a High. This is a post-Crisis era when institutions are strong and

individualism is weak. Society is confident about where it wants to go collectively, though those outside the majoritarian

centre often feel stifled by the conformity.[20]

• According to the authors, America’s most recent First Turning was the post-World War II American High, beginning in

1946 and ending with the assassination of President John F. Kennedy on November 22, 1963. The Silent Generation

(Artist archetype, born 1925 to 1942) came of age during this era. Known for their caution, conformity, and institutional

trust, Silent young adults epitomized the mood of the High. Most married early, sought stable corporate jobs, and moved

into new suburbs.[21]

Awakening • According to the theory, the Second Turning is an Awakening. This is an era when institutions are attacked in the name

of personal and spiritual autonomy. Just when society is reaching its high tide of public progress, people suddenly tire of

social discipline and want to recapture a sense of personal authenticity. Young activists look back at the previous High as

an era of cultural and spiritual poverty.[22]

• America’s most recent Awakening was the “Consciousness Revolution,” which spanned from the campus and inner-city

revolts of the mid-1960s to the reelection of Ronald Reagan. The Boom Generation (Prophet archetype, born 1943 to

1960) came of age during this era. Their idealism and search for authentic self-expression epitomized the mood of the

Awakening.[23]


http://en.wikipedia.org/wiki/History_of_the_United_States_(1945%E2%80%931964)

http://en.wikipedia.org/wiki/John_F._Kennedy_Assassination



http://en.wikipedia.org/wiki/Consciousness_Revolution


Generational Archetypes and Turnings

Unraveling • According to Strauss and Howe, the Third Turning is an Unraveling. The mood of this era is in many ways the

opposite of a High: Institutions are weak and distrusted, while individualism is strong and flourishing. Highs come after

Crises, when society wants to coalesce and build. Unravelings come after Awakenings, when society wants to atomize

and enjoy.

• America’s most recent Unraveling was the Long Boom and Culture War, beginning in the mid-1980s and ending in the

late 2000s. The era began during the second term of (Reagan’s “Morning in America”), which eventually developed

into a "debased" popular culture, a pervasive distrust of institutions and leaders, and the splitting of national consensus

into competing “values” camps. Generation X (Nomad archetype, born 1961–1981) came of age during this era.

Crisis • According to the authors, the Fourth Turning is a Crisis. This is an era in which institutional life is destroyed and rebuilt

in response to a perceived threat to the nation’s survival. Civic authority revives, cultural expression redirects towards

community purpose, and people begin to locate themselves as members of a larger group. Fourth Turnings have all

been new “founding moments” in America’s history, moments that redefined the national identity.[25] America’s most

recent Fourth Turning began with the stock market crash of 1929 and climaxed with the end of World War II. The G.I.

Generation (a Hero archetype, born 1901 to 1924) came of age during this era. Their confidence, optimism, and

collective outlook epitomized the mood of the era.[26] Today’s youth, the Millennial Generation (Hero archetype, born

1982 to 2004), show many traits similar to those of the G.I. youth, including rising civic engagement, improving

behavior, and collective confidence.[27]

http://en.wikipedia.org/wiki/1990s_United_States_boom


http://en.wikipedia.org/wiki/Morning_in_America



http://en.wikipedia.org/wiki/Stock_market_crash_of_1929





• The situation for spiritual awakenings is even more problematic. The spiritual awakenings in

Table 1 roughly correspond to periods of religious fervour identified by historian William

McLoughlin in his book Revivals, Awakenings and Reform..

• McLoughlin defines awakenings as periods of cultural revision caused by a crisis in value

and belief systems - producing a reorientation in those values and beliefs. McLoughlin

identifies awakenings in 1610-40, 1730-60, 1800-30, and 1890-1920.6 Comparison of these

dates with those for spiritual awakenings in Table 1 shows a rough correspondence. The

spiritual awakenings are subsets of the McLoughlin Cycles and tend to be located midway

between secular crises so that a regular pattern of alternating social moments is evident.

Saeculum McLaughlin Cycle Spiritual Awakening Secular Crisis

1415 - 1514 Pre-Columbian Renaissance (1517-1539) Wars of the Roses (1455-1487)

1515 - 1614 Columbian The Reformation (1517-1539) Spanish Armada (1569-1594)

1615 - 1714 Colonial Early Enlightenment (1610-1640) English Civil War (1675-1704)

1715 - 1814 Revolutionary Late Enlightenment (1730-1760) American Revolution (1773-1794)

1815 - 1914 Victorian Transcendental (1800-1830) Napoleonic Wars (1860-1865)

1915 - 2014 Loss of Empires Missionary Awakening (1890-1920) WWI, Depression & WWII (1929-1946)

Cold War Baby Boom Awakening (1960-1980) Regional Wars, Terrorism, Insecurity

Millennial 21st century Awakening (2000 - 2014) Regional Wars, Terrorism, Insecurity


Saeculum - Century Waves – Human Conflict, Technology Arms Race & Innovation cycles: -

• Industrial / Technology Arms Race Saeculum – 100 years / generation intervals @ 25 years




– The Second World War – European Theatre 1939

• Cold War Geo-political Rivalry and Conflict – @ 20 years (Olympic Games - even decades)

– The Second World War – Pacific Theatre 1940




– Culminating in a future Middle East Conflict by 2020 ?

• Cold War Geo-political Rivalry and Conflict – @ 20 years (Soccer World Cup - odd decades)





– Culminating in a future Trade War between USA and China by 2030 ?





















20. G.I. Generation (1901–1924) (H)






• Industrial / Technology Arms Races – 25 years




– The Second World War – 1939


(Olympic Games Years - even decades)

– The Second World War - 1940




– Culminating in a future Middle East Conflict by

2020 ?


(Football World Cup years - odd decades)





– Culminating in a future Trade War between USA

and China by 2030 ?

Generation and Century Waves – Human Conflict:- Technology and Innovation waves








Generation Wave Archetypes

Prophet • Abraham Lincoln, born in 1809. Strauss and Howe would identify him as a member of the Transcendental generation.

• Prophet generations are born near the end of a Crisis, during a time of rejuvenated community life and consensus

around a new societal order. Prophets grow up as the increasingly indulged children of this post-Crisis era, come of

age as self-absorbed young crusaders of an Awakening, focus on morals and principles in midlife, and emerge as

elders guiding another Crisis.[44]

• Due to their location in history, such generations tend to be remembered for their coming-of-age fervor and their

values-oriented elder leadership. Their main societal contributions are in the area of vision, values, and religion. Their

best-known historical leaders includeJohn Winthrop, William Berkeley, Samuel Adams, Benjamin Franklin, James

Polk, Abraham Lincoln, Herbert Hoover, and Franklin Roosevelt. These people were principled moralists who waged

idealistic wars and incited others to sacrifice. Few of them fought themselves in decisive wars, and they are

remembered more for their inspiring words than for great actions. (Example among today’s living generations: Baby

Boomers.)

Nomad • Nomad generations are born during an Awakening, a time of social ideals and spiritual agendas, when young adults

are passionately attacking the established institutional order. Nomads grow up as under-protected children during this

Awakening, come of age asalienated, post-Awakening adults, become pragmatic midlife leaders during a Crisis, and

age into resilient post-Crisis elders.[44]

• Due to their location in history, such generations tend to be remembered for their adrift, alienated rising-adult years

and their midlife years of pragmatic leadership. Their main societal contributions are in the area of liberty,

survival and honor. Their best-known historical leaders include Nathaniel Bacon, William Stoughton, George

Washington, John Adams, Ulysses Grant, Grover Cleveland, Harry Truman, and Dwight Eisenhower. These were

shrewd realists who preferred individualistic, pragmatic solutions to problems. (Example among today’s living

generations: Generation X.[45])

http://en.wikipedia.org/wiki/Abraham_Lincoln


http://en.wikipedia.org/wiki/John_Winthrop



http://en.wikipedia.org/wiki/William_Berkeley_(governor)

http://en.wikipedia.org/wiki/Samuel_Adams

http://en.wikipedia.org/wiki/Benjamin_Franklin

http://en.wikipedia.org/wiki/James_Polk

http://en.wikipedia.org/wiki/James_Polk

http://en.wikipedia.org/wiki/Abraham_Lincoln

http://en.wikipedia.org/wiki/Herbert_Hoover

http://en.wikipedia.org/wiki/Franklin_Roosevelt

http://en.wikipedia.org/wiki/Baby_Boomer

http://en.wikipedia.org/wiki/Baby_Boomer

http://en.wikipedia.org/wiki/Social_alienation


http://en.wikipedia.org/wiki/Nathaniel_Bacon_(colonist)

http://en.wikipedia.org/wiki/William_Stoughton_(Massachusetts)

http://en.wikipedia.org/wiki/George_Washington

http://en.wikipedia.org/wiki/George_Washington

http://en.wikipedia.org/wiki/John_Adams

http://en.wikipedia.org/wiki/Ulysses_Grant

http://en.wikipedia.org/wiki/Grover_Cleveland

http://en.wikipedia.org/wiki/Harry_Truman

http://en.wikipedia.org/wiki/Dwight_Eisenhower

http://en.wikipedia.org/wiki/Realism_(international_relations)

http://en.wikipedia.org/wiki/Individualism

http://en.wikipedia.org/wiki/Pragmatism



Generation Wave Archetypes

Hero • Young adults fighting in World War II were born in the early part of the 20th century, like PT109 commander LTJG John F.

Kennedy (b. 1917). They are part of the G.I. Generation, which follows the Hero archetype.

• Hero generations are born after an Awakening, during an Unravelling, a time of individual pragmatism, self-reliance, and

laissez faire. Heroes grow up as increasingly protected post-Awakening children, come of age as team-oriented young

optimists during a Crisis, emerge as energetic, overly-confident midlifers, and age into politically powerful elders attacked

by another Awakening.

• Due to their location in history, such generations tend to be remembered for their collective military triumphs in young

adulthood and their political achievements as elders. Their main societal contributions are in the area of community,

affluence, and technology. Their best-known historical leaders include Cotton Mather, Thomas Jefferson, James

Madison, John F. Kennedy and Ronald Reagan. These have been vigorous and rational institution builders. In midlife, all

have been aggressive advocates of economic prosperity and public optimism, and all have maintained a reputation for

civic energy and competence in old age. (Examples among today’s living generations: G.I. Generation and Millennials.)

Artist • Artist generations are born after an Unraveling, during a Crisis, a time when great dangers cut down social and political

complexity in favour of public consensus, aggressive institutions, and an ethic of personal sacrifice. Artists grow up over-

protected by adults preoccupied with the Crisis, come of age as the socialized and conformist young adults of a post-

Crisis world, break out as process-oriented midlife leaders during an Awakening, and age into thoughtful post-Awakening

elders.

• Due to their location in history, such generations tend to be remembered for their quiet years of rising adulthood and their

midlife years of flexible, consensus-building leadership. Their main societal contributions are in the area of expertise and

due process. Their best-known historical leaders include William Shirley, Cadwallader Colden, John Quincy Adams,

Andrew Jackson, and Theodore Roosevelt. They have been complex social technicians and advocates for fairness and

inclusion. (Example among today’s living generations: Silent.)


http://en.wikipedia.org/wiki/PT109

http://en.wikipedia.org/wiki/Lieutenant_(junior_grade)

http://en.wikipedia.org/wiki/John_F._Kennedy


http://en.wikipedia.org/wiki/G.I._Generation

http://en.wikipedia.org/wiki/Cotton_Mather

http://en.wikipedia.org/wiki/Thomas_Jefferson

http://en.wikipedia.org/wiki/James_Madison

http://en.wikipedia.org/wiki/James_Madison


http://en.wikipedia.org/wiki/Ronald_Reagan

http://en.wikipedia.org/wiki/Greatest_Generation

http://en.wikipedia.org/wiki/Millennials

http://en.wikipedia.org/wiki/Due_process

http://en.wikipedia.org/wiki/William_Shirley

http://en.wikipedia.org/wiki/Cadwallader_Colden



http://en.wikipedia.org/wiki/John_Quincy_Adams

http://en.wikipedia.org/wiki/Andrew_Jackson

http://en.wikipedia.org/wiki/Theodore_Roosevelt

http://en.wikipedia.org/wiki/Silent_generation


Cycle


Secular Crisis

Strauss-Howe

Generation

Generation

Date / Type

1415 - 1514 Pre-Columbian Renaissance

(1517-1539) Retreat from France

Arthurian Generation 1433–1460) (H)

Wars of the Roses

(1455-1487) War of the Roses

Humanist Generation 1461–1482) (A)

High: Tudor Renaissance Reformation Generation 1483–1511) (P)

1515 - 1614 Columbian Reformation

(1517-1539) Protestant Reformation

Reprisal Generation 1512–1540) (N)

Spanish Armada

(1580-1588) Intolerance and Martyrdom

Elizabethan Generation 1541–1565) (H)

Crisis: Armada Crisis Parliamentary Generation 1566–1587) (A)

High: Merrie England Puritan Generation 1588–1617) (P)

1615 - 1714 Colonial Early Enlightenment

(1610-1640)

English Civil War

(1675-1704)

Cavalier Generation 1618–1647) (N)

Reaction and Restoration Glorious Generation 1648–1673) (H)

Crisis: King Philip's War,

Glorious Revolution Enlightenment Generation 1674–1700) (A)

1715 - 1814 Revolutionary Late Enlightenment

(1730-1760)

American Revolution

(1773-1794)

Awakening Generation 1701–1723) (P)

Great Awakening Liberty Generation 1724–1741) (N)

French and Indian War Republican Generation 1742–1766) (H)

Crisis: American Revolution Compromise Generation 1767–1791) (A)


















http://en.wikipedia.org/wiki/American_Revolution


Cycle

Spiritual Age Secular Crisis Strauss-Howe

Generation

Generation

Date / Type

Victorian Age -

(1815 – 1914)

Imperialism and

National Rivalry

Transcendental

(1800-1830) High: Era of Good Feeling

Transcendental Generation 1792–1821) (P)

Industry/Technology

Arms Races Transcendental Awakening

Gilded Generation 1822–1842) (N)

Unravelling: Slavery

abolished in British Empire Progressive Generation 1843–1859) (A)

Napoleonic Wars

(1860-1865)

Missionary Generation 1860–1882) (P)

Anglo-Chinese Opium

War - 1888

Lost Generation 1883–1900) (N)

Globalisation –

(1915 – 2014)

World Wars and

Loss of Empires

Missionary Awakening

(1890-1920)

The Great War –

(1914-1918)

G.I. Generation 1901–1924) (H)

WWI, Depression &

WWII (1929-1946)

The Second World War –

(1939-1945)

Silent Generation 1925–1942) (A)

Cold War Korean War - 1950 Baby Boom Generation 1943–1960) (P)

Regional Wars,

Terrorism, Insecurity

Baby Boom Awakening

(1960-1980)

Vietnam War - 1970

Russian War in

Afghanistan - 1980

Generation X (Generation X) 1961–1981) (N)

Millennial Conflicts 21st century Awakening

(2000 - 2020)

1st Gulf War –1990

Balkan Conflict – 2000

Millennial Generation (Gen Y) 1982–2004) (H)

Post-Millennial

Conflicts

“Arab Spring” Uprisings –

2010

Homeland Generation (Gen Z) 2005–2025 (A)

http://en.wikipedia.org/wiki/Era_of_Good_Feeling

http://en.wikipedia.org/wiki/Second_Great_Awakening









Cycle


Secular Crisis

Strauss-Howe

Generation

Generation

Date / Type

2015-2114 Post-Millennial 21st century Apocalypse

(2020 - 2040)

Global Food, Energy and

Water (FEW) Crisis

Apocalyptic Generation (Gen A) 2025–2050 (P)

Post-Apocalyptic

Realisation (2040 - 2060)



Post-Apocalyptic Generation

(Gen B)

2050–2070 (N)

Post-Apocalyptic Recovery

(2040 - 2060)



Recovery Generation (Gen C) 2070–2090) (H)

The current saeculum starts at the beginning of WWI in 1914, and so ends 100 years later in

2015 – which is the same time as the current 50-year Kondriatev Infrastructure Investment Wave

also ends. A new saeculum can mark the beginning of a either a period of unprecedented growth

and prosperity – or of global crisis. From 2015, a number of Economic Cycles rise together.

Strauss and Howe have based their definition of all of the saecula during the past 600 years on

Anglo-American history - from the start of the Protestant Reformation until the present day. In

common usage, the term saeculum is not usually allocated to any fixed period of time, but may

be of any duration from 80 up to 100 years. Saecula may be divided into four generations or

"seasons" varying between15-30 years each; Strauss and Howe represent these seasons as

youth, rising adulthood, midlife, and old age. This basis for Strauss and Howe generations and

saecula definition is somewhat arbitrary and debatable. McLaughlin, however, makes a much

better fist of his definition of generations and saecula.


• One example of the effect of succession changing generational membership is the

decline in community spirit across American society over the post-war decades. In

his intriguing book Bowling Alone, Robert Putnam proposes that the succession

of a civic-minded pre-war generation and gradual replacement with the markedly

more individualistic, self-confident and self-centric baby boomers is responsible for

about half of this decline. In a particularly striking figure, Putnam documents

downward trends in eight measures of Civic Engagement by year-of-birth.

• For seven of the eight measures, this decline either begins or accelerates in the

late 1920's to early 1930's period. People born after the early 1930's are less

active in their community than those born before, and this trend towards less

engagement accelerates with more recent birth years. Strauss and Howe would

explain the trends that Putnam describes as being the result of the succession of

generations having peer personalities characterized by decreasing levels of civic-

responsibility and public-orientation. Their GI generation (b 1901-24) has a peer

personality of a particularly civic-minded type - in stark contrast to the GI's are the

Baby Boomers Generation X and Generation Z – all of which have highly-focused

and individualistic peer personalities, as evidenced by the growth in Social Media.


• The peer personality of a particular generation is shaped by the generation's historical

location relative to a social moment. A social moment is an era, typically lasting about a

decade, when people perceive that historical events are radically altering their social

environment. Thus, a generation's peer personality (what makes it a particular kind of

generation) depends on when they were born relative to particularly eventful periods in

history. There are two types of social moments: secular crises, when society focuses on

reordering the outer world of institutions and public behaviour; and spiritual awakenings,

when society focuses on changing the inner world of values and private behaviour.

• What constitutes the saeculum is not the regularly repeating series of social moments –

but technology innovation. If succession moments occurred sporadically, a regular series

of generations would not be created and there would be no saeculum. Strauss and Howe

list six spiritual awakenings and five secular crises (Table 1) spaced 88 years apart on

average as their primary evidence for the existence of regularly spaced social moments.

• They propose generations reflect the experience of living through a social moment which

is triggered at a particular phase of life by economic wealth from innovation. The phases of

life are youth (age 0-21), rising adulthood (age 22-43), maturity (age 44-65) and elderhood

(age 66-87). They are 22 years in length and four of them comprise an 88-year saeculum,

which neatly dovetails with the average spacing of social moments of the same type.

Robert Putnam

• The effect of

succession-

changing

generational

membership on

trends in Social

Connectivity

• Have Smart

Apps and

Social Media

replaced Face-

2-face human

contact and

attending group

social events in

creating and

maintaining

Social Networks ?

BIBLOGRAPHY

Bohm-Bawerk, Eugen. 1891. The Positive Theory of Capital. London: Macmillan and Co.

Garrison, Roger. 2001. Time and Money: the Macroeconomics of Capital Structure. New York: Routledge.

Garrison, Roger. 2007. “Capital-Based Macroeconomics,” on-line slide show,

http://www.slideshare.net/fredypariapaza/capitalbased-macroeconomics, accessed 10/6/07.

Hayek, Friedrich A. [1931] 1966a. Prices and Production. New York: Augustus M. Kelley Publishers.

Hayek, Friedrich A. [1933] 1966b. Monetary Theory and the Trade Cycle. New York: Augustus M. Kelley.

Hayek, Friedrich A. 1995. Contra Keynes and Cambridge: Essays, Correspondence. Edited by Bruce Caldwell.

Chicago: University of Chicago.

Hoppe, Hans-Hermann. 1993. The Economics and Ethics of Private Property. Boston: Kluwer Academic.

Keynes, John M. 1931. “The Pure Theory of Money. A Reply to Dr. Hayek.” Economica (11) 34, 387-397.

Kurz, Heinz D. 1990. Capital, Distribution and Effective Demand: Studies in the “Classical Approach” to Economic

Theory. Cambridge, UK: Polity Press.

Kurz, Heinz and Salvadori, Neri. 1992. Theory of Production I. Milan: Instituto di ricera sulla Dinamica dei Sistemi

Economoci (IDSE).

Menger, Carl. [1871] 1950. Principles of Economics. Glencoe, IL: Free Press.

Mises, Ludwig. [1932] 1990. “The Non-Neutrality of Money”, in Money, Method and the Market Process, Richard M.

Ebeling, ed., from lecture given to New York City Economics Club. Norwell, MA: Kluwer Academic Publishers.

Mulligan, Robert F. 2006. “An Empirical Examination of Austrian Business Cycle Theory.” Quarterly Journal of Austrian

Economics 9 (2), 69-93.

Schumpeter, Joseph R. 1950. Capitalism, Socialism and Democracy. New York: Harper & Row.

http://www.slideshare.net/fredypariapaza/capitalbased-macroeconomics



Econometrics

How should the progress and development of a Sovereign Nation States

be measured and compared ?

Econometrics

• Econometrics is the application of statistical and mathematical techniques to

competing economic theories for the purpose of forecasting future trends and

risks. This takes economic models and time-series data sets through

statistical trials in order to test, verify and validate alternative hypotheses.

Collaboration between academia, government and the financial services

industry is improving the understanding of economic theory and econometric

modelling and advancing the homogeneity and integration of competing

economic theories and models, especially wherever suitable time-series data

sets exist to support model evaluation and benchmarking.

Competing theories and conflicting models underling fiscal policy analysis and

market risk evaluation are now being given a more sympathetic treatment and

satisfactory integration. Even now, attempts are being made to resolve those

factors of heterogeneity and conflict in economic modelling – an essential

condition for the development of a “standard economic theory” integrating

macro- and micro-economic views within a universally valid “standard

economic risk framework”.

Econometrics

• Economic theory makes statements or hypotheses that are mostly qualitative in nature. For example, microeconomic theory states that, other things remaining the same, a reduction in the price of a commodity is expected to increase the quantity demanded of that commodity. Thus, economic theory postulates a negative or inverse relationship between the price and quantity demanded of a commodity. But the theory itself does not provide any numerical measure of the relationship between the two; that is, it does not tell by how much the quantity will go up or down as a result of a certain change in the price of the commodity. It is the job of the econometrician to provide such numerical estimates. Stated differently, econometrics gives empirical content to most economic theory.

• The main concern of mathematical economics is to express economic theory modelled in mathematical form (equations) without regard to measurability or empirical verification of the theory. Econometrics, as noted previously, is mainly interested in the empirical verification of economic theory. As we shall see, the econometrician often uses the mathematical equations proposed by the mathematical economist but translates, transposes and transforms these equations in such a form that they lend themselves to empirical testing, verification and validation. This conversion of econometric theories and hypotheses into mathematical and statistical models and equations requires a great deal of ingenuity and practical skill.

Econometrics

• Economic statistics is mainly concerned with collecting, processing, and presenting highly aggregated and summarised economic data in the form of charts and tables. The low volume, highly summarised economic data collected by the Economic Statistician is not, therefore, the same in either structure or content as the very large volumes of raw atomic-level data required for econometric work.

• Whereas the economic statistician does not go beyond packaging, presenting and publishing their results – not being concerned with using the data collected to test the validity of economic theories – the econometrician analyses the statistical and mathematical equations proposed by the economic statistician and translates, transposes and transforms those equations in such a way as to lend themselves to empirical testing, verification and validation of econometric time-series data sets.

• There are several important implications for empirical modelling in econometrics. Firstly, in econometrics the modeller is, of course, faced with observational time-series historic data - as opposed to experimental data, Second, the modeller is required to master very different skill sets from those needed for manipulating experimental data in the physical sciences. Finally, the segregation of the roles of data collector and data analyst requires that the modeller becomes thoroughly familiar with the nature, structure and content of econometric time-series data sets.

Econometrics

• New time-series econometric techniques have been developed, based on

Econometric Data Science, which are being employed extensively in the areas

of macro-econometrics and finance. Non-linear econometric developments

are being used increasingly in the analysis cross-section (snap-shot) and time-

series (temporal) observations. Novel and emerging techniques in Econometric

Data Science Algorithms (such as clustering and wave-form analytics) and “Big

Data” in-memory computing technology are paving the way for establishing the

realisation of “real time econometrics” computing platforms and frameworks.

• Applications of Bayesian techniques to econometric problems have been given

new impetus - thanks largely to advances in computer power and econometric

techniques. The use of Bayesian and Non-linear econometric techniques are

beginning to provide researchers with a unified econometric framework where

the tasks of economic and risk forecasting, decision making, model evaluation

and benchmarking – and economic learning – can now be considered as

integral parts of the same interactive and iterative econometric process.

Econometrics

• When a governmental agency (e.g., the U.K. Office of Statistics or the U.S. Department of

Commerce) collects economic data, it does not necessarily have any economic theory in

mind as an explanation of the observed data. How does one know that the data really

supports a specific economic theory – such as the Keynesian theory of consumption ?

Is it because the Keynesian consumption function (i.e., the regression line) shown in

Figure I.3 is extremely close to the actual data points? Is it possible that another

consumption model (hypothesis, theory) might equally fit the observed data as well ?

• For example, Milton Friedman developed a model of consumption, called the permanent

income hypothesis. Robert Hall has also developed a model of consumption, called the

life-cycle permanent income hypothesis. Could one or both of these models also fit the

observed data ? In practice, the question facing an economic researcher is how to select

models of a given economic phenomenon among competing economic theories and

hypotheses, such as the nature of the relationship between consumption and income.

• As Miller contends:

• “Any encounter with data is step towards genuine confirmation whenever the hypothesis

does a better job of coping with the data than some natural rival. . . . What strengthens a

hypothesis is a victory which, at the same time, is a defeat for a plausible rival model.....”

Econometrics Methods

• How then does one choose among competing models or hypotheses? Here is the advice given

by Clive Granger. I would like to suggest that in the future, when you are presented with a new

piece of theory or empirical model, it is worth keeping in mind to ask these questions in relation

to the Problem / Opportunity Domain of comparing alternative theories or models: –

1. What is its purpose – which economic phenomena is it attempting to illuminate ?

2. What economic problem, opportunities, risks or decisions does it help to resolve?

3. What evidence is being presented that allows model quality / voracity evaluation ?

• We often come across several competing hypotheses when trying to explain various economic

phenomena. Economics students are familiar with the concept of the production function, which

is basically a relationship between production output and inputs (e.g. capital and labour). Two of

the best known production functions are the Cobb–Douglas function and the constant elasticity

of substitution function. Given data sets on production inputs and outputs, we will need to

discover which of the two production functions - if any – best supports the observed data.

• The multi-step classical econometric methodology discussed below is neutral in the sense that it

can be used to test any of these rival hypotheses. Is it possible to develop an econometric

framework that is comprehensive enough to include the testing, verification and validation of

competing economic theories or hypotheses? This is a much involved and controversial topic.


• There are two important implications for empirical modelling in econometrics. First,

the modeller is required to master very different skill sets from those needed for

analyzing experimental data in the physical sciences. Second, the separation of the

roles of data collector and the data analyst requires that the modeller to becomes

thoroughly familiar with the nature and structure of econometric time-series data sets.

PROBLEM DOMAIN ANALYSIS METHOD

1. Selection of the problem / opportunity domain

2. Description of economic theory or hypothesis.

3. Analysis of the underlying economic principles,

actors and econometric processes of the theory

4. Specification of the logical econometric model

5. Obtaining the time-series / cross-section data

6. Estimation of the scope of the economic theory

7. Hypothesis testing, validation and verification

8. Hypothesis refining and enhancement

9. Economic forecasting and prediction

10. Publishing and communicating the theory

PROBLEM DOMAIN MODELLING METHOD

1. Selection of the Model Framework

2. Definition of the theory or hypothesis.

3. Development of the data model and parameters

4. Design of the mathematical / statistical process

5. Construction of the econometric model functions

6. Data Loading and Model initiation

7. History Matching Runs

8. Model testing, validation and verification

9. Model Tuning Runs

10. Forecasting and Prediction Runs

11. Using the output for risk and policy decisions


• The following diagram outlines the relationship between Theoretical and Applied

Techniques and shows how Theoretical and Applied Techniques are mirrored: -

Linear Systems

Dynamic Systems

Phase Space

Non-linear Systems

CHAOS

Complex Systems

Theoretical Techniques Applied Techniques

1. Classical Economics

2. Scenarios

3. Probability, Voracity

4. Profiling

5. Business Cycles

6. Black Swan Events

1. Determinism – Human Actions

2. Monte Carlo Simulation

3. Bayesian Statistics

4. Clustering Algorithms

5. Wave-form Algorithms

6. Stochastic Events

Differential Equations

Adaptive Systems


• Bayesian statistics is a subset of the general field of statistics in which the evidence

about the true state of the world is expressed in terms of degrees of belief or, more

specifically, Bayesian statistics (Bayesian probabilities) - which are based on a

different philosophical approach and method for demonstrating proof of statistical

inference (measuring voracity) to statistical frequency (measuring occurrence) in

the field of general statistics. Wikipedia Exploration Topic: Statistical inference

• In Bayesian statistics, the posterior probability of a stochastic (random) event or

likelihood of the outcome of an uncertain proposition is the conditional degree of

voracity (belief or probability) that is assigned to it after all of the relevant evidence

has been taken into account. Wikipedia Exploration Topic: Posterior probability

• As with other branches of statistics, experimental design is explored using both

statistical frequency and Bayesian probability approaches: In evaluating various

statistical procedures like experimental designs, statistical frequency studies the

sampling distribution while Bayesian statistics updates the probability distribution

on the parameter space. Wikipedia Exploration Topic : Frequency inference

https://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=14&ved=0CIsBEJguMA0&url=http://en.wikipedia.org/wiki/Statistical_proof&ei=ofv4U-isEsTnavLlgLAC&usg=AFQjCNE049JJ2wvw0R82LsvifSPLD5eKOA

https://www.google.co.uk/search?biw=1280&bih=685&q=Statistical+inference&stick=H4sIAAAAAAAAAAEpANb_AHvTx-gAAAAMCAMiCC9tLzA2dnl5Rri6d6fZ2Vep6NScMLoqP7nwXtCr1pNxKQAAAA&sa=X&ei=ofv4U-isEsTnavLlgLAC&ved=0CIwBEPcxMA0

http://en.wikipedia.org/wiki/Posterior_probability

https://www.google.co.uk/search?biw=1280&bih=685&q=Posterior+probability&stick=H4sIAAAAAAAAAGOovnz8BQMDAy8HsxKnfq6-gWG8kVmVHkuFj7xo-2Zprh33tiQ2MZt94A8DAEQpcmMqAAAA&sa=X&ei=ofv4U-isEsTnavLlgLAC&ved=0CIQBEPcxMA0

http://en.wikipedia.org/wiki/Design_of_experiments

https://www.google.co.uk/search?biw=1280&bih=685&q=Frequentist+inference&stick=H4sIAAAAAAAAAGOovnz8BQMDAx8HsxKXfq6-QZK5aYGRoczeqVv-Sng4sxg8yD2SXvVKa-2UdQDwYD0FKwAAAA&sa=X&ei=ofv4U-isEsTnavLlgLAC&ved=0CIgBEPcxMA0


• The following diagram outlines the relationship between numerical and analytical

techniques, and shows how analytical and numerical methods can be integrated: -

Linear Systems

Dynamic Systems

Phase Space

Non-linear Systems

CHAOS

Complex Systems

Numerical Methods Analytical Methods

1. Profiling

2. Data Mining

3. Fortran 90

4. Arrays

5. do loop, if

6. Subroutines

7. Euler method

1. CHAID Analysis

2. 1−dim: sink, sources

3. 2−dim, linear equations,

saddles, nodes, spirals, centres

4. 2−dim, non-linear equations,

limit cycles

5. Poincare−Bendixon theorem

6. 3−dim, nonlinear equations

Differential Equations

Adaptive Systems

Human Agent-Based Modeling

Principle Tenets of Austrian Capital Theory and Agent-Based Modeling: -

Basic Model – top-down profiled Citizen Classes, Streams and Segments

1. Macro-economic (aggregated) National Model initiated with three ‘classes”, 1) rich

start with 2 capital units, earn investment returns only and are more risk seeking than

middleclass, 2) middleclass start with one capital unit, earn both wage income and

investment returns, and 3) poor only earn wages, draw benefits or commit crime.

2. Initial conditions assumes that all wages are spent – no savings. Model allows for

varying endowments, and risk preferences, within classes, streams and segments.

Austrian Capital Theory and Human Agent-Based Modeling

In the Basic Model - subjective unique risk preferences are generalized into three model

classes, with bounded, “sticky”, investment functions based on time lags for investment

to move from one stage of production to another. Unemployment based on investment

time-lags, with lay-offs beginning at higher stages of production. Economy operates

over-time showing results on accumulation, distribution, growth, employment, population.



Basic Model – top-down profiled Citizen Classes, Streams and Segments

3. Models are populated using income / expenditure from standard socio-economic /

demographic profile classes, streams and segments from Experian / Census Data.

4. Timing differences - interest rate change take three periods - moving from lower to

higher stages of production – before being fully integrated into investment decisions.

5. Rich upper-class agents use Working Capital as wages in order to hire employees.

Each capital worker unit is initially allocated in economy according to weight of stage

of production in the capital structure of economy.

6. Wealthy, middle-class agents move to rich after accumulating second capital unit.

7. Poor, working-class agents move to middle class after 20 periods of work; when poor

moves to middle-class, then another poor agent is born. Each agent lives 40 years.



Advanced Model – models bottom-up individual / household Census Data

1. Micro-economic Local Models can be developed, aggregated and summarized using

both Geographical (Postcode - in-code / out-code) and Geopolitical (Parish, District,

Town, County, Country) hierarchies to benchmark and validate National Models.

2. Models are designed using GIS Mapping and Spatial Analysis to handle standard

Geo-spatial Data Types – for people, property and places (locations and buildings).

3. Models are developed using standard Local and National Government Location and

Places Gazetteer (LLPG / NLPG) and Experian / Census Data for people and places.

4. Models are populated using actual individual / household personal data – age, ethnic

group, occupation, income and expenditure – from Experian / Census Data.

5. Models can be compared, benchmarked, verified and validated using standard socio-

economic / demographic profile streams and segments from Experian / Census Data.

The Austrian Vision

• The Austrian School of Real Economics was the forerunner of laissez-faire

unrestrained free market (libertarian) economics, and its central tenet or main

concept is that the coordination of human effort can be achieved only through

the combined decisions and judgments of individuals (human actions) - and

cannot be forced by an external agency such as a government. It emphasizes

complete freedom of association and sovereignty of individual property rights.

• Its other main tenets include (1) abolishment of central banks and return to the

gold standard, elimination of bank deposit insurance schemes so that bank

failures punish bad investments, (3) institution of an information system that

make real-time prices data available to everyone, abandonment of mathematical

models as too rigid and limited to be of any use. Most of its recommendations

are fiercely opposed by mainstream economists (both capitalist and socialist)

economists who call the Austrian School 'anarchist economics' – and barely

acknowledging its very existence. The Austrian School does not, however,

support unrestricted laissez-faire capitalism. Hayek went as far as to advocate

the re-distributing of wealth in the form of negative income tax

http://www.businessdictionary.com/definition/free-market.html

http://www.businessdictionary.com/definition/economics.html

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http://www.businessdictionary.com/definition/coordination.html

http://www.businessdictionary.com/definition/decision.html

http://www.businessdictionary.com/definition/judgment.html

http://www.businessdictionary.com/definition/individual.html

http://www.businessdictionary.com/definition/agency.html

http://www.businessdictionary.com/definition/government.html

http://www.businessdictionary.com/definition/freedom-of-association.html

http://www.businessdictionary.com/definition/sovereignty.html

http://www.businessdictionary.com/definition/property-rights.html

http://www.businessdictionary.com/definition/central-bank.html

http://www.businessdictionary.com/definition/return.html

http://www.businessdictionary.com/definition/gold-standard.html

http://www.businessdictionary.com/definition/bank.html

http://www.businessdictionary.com/definition/deposit-insurance.html

http://www.businessdictionary.com/definition/scheme.html

http://www.businessdictionary.com/definition/bank-failure.html

http://www.businessdictionary.com/definition/bank-failure.html

http://www.businessdictionary.com/definition/investment.html

http://www.businessdictionary.com/definition/institution.html

http://www.businessdictionary.com/definition/information-system.html

http://www.businessdictionary.com/definition/real-time.html



http://www.businessdictionary.com/definition/price.html

http://www.businessdictionary.com/definition/data.html

http://www.businessdictionary.com/definition/abandonment.html

http://www.businessdictionary.com/definition/mathematical-model.html

http://www.businessdictionary.com/definition/mathematical-model.html

http://www.businessdictionary.com/definition/limited.html

http://www.businessdictionary.com/definition/mainstream.html

http://www.businessdictionary.com/definition/Socialist.html

http://www.businessdictionary.com/definition/economist.html

The Austrian Vision

• The Austrian School of Real Economics body of thought was founded in 1871 in

Vienna by Carl Menger (1840-1921) who developed marginal utility theory of value

and carried on by Friedrich von Wiesner (1851-1926) who developed the concept

of opportunity cost. This was further elaborated by Eugen von Böhm-Bawerk

(1851-1914) who developed a capital and interest rate theory, Ludwig Edler von

Mises (1881-1973) and Joseph Schumpeter (1883-1950) who developed a

business cycle theory, and the 1974 Nobel laureate in economics, Friedrich August

von Hayek (1899-1992) who unified the vast body of works of his predecessors.

• This diverse mix of intellectual traditions in economic science is even more obvious

in contemporary Austrian school economists, who have been influenced by modern

figures in economics. These include ARMEN ALCHIAN, JAMES BUCHANAN, RONALD

COASE, Harold Demsetz, Axel Leijonhufvud, DOUGLASS NORTH, Mancur

Olson,VERNON SMITH, Gordon Tullock, Leland Yeager, and Oliver Williamson, as

well as Israel Kirzner and Murray Rothbard.

http://www.businessdictionary.com/definition/founded.html

http://www.businessdictionary.com/definition/developed.html

http://www.businessdictionary.com/definition/marginal-utility.html

http://www.businessdictionary.com/definition/theory.html

http://www.businessdictionary.com/definition/value.html

http://www.businessdictionary.com/definition/opportunity-cost.html

http://www.businessdictionary.com/definition/capital.html

http://www.businessdictionary.com/definition/and-interest.html

http://www.businessdictionary.com/definition/rate.html

http://www.businessdictionary.com/definition/business-cycle.html

http://www.businessdictionary.com/definition/work.html

http://www.econlib.org/library/Enc/bios/Alchian.html



http://www.econlib.org/library/Enc/bios/Buchanan.html



http://www.econlib.org/library/Enc/bios/Coase.html



http://www.econlib.org/library/Enc/bios/North.html



http://www.econlib.org/library/Enc/bios/SmithV.html



The Austrian Vision

• The Austrian Vision. The Austrian School of Real Economics very much owes its

uniqueness to its attention to the role of human actions in a free market and the

economy's capital structure. Theories of measuring the value of capital, about the

periodic business cycle influence on the structure of capital invested in production,

and about temporal market mechanisms that facilitate inter-cycle adjustments to

the capital structure has constituted a significant part of the research agenda for

the early as well as the modern Austrian school. Yet, fundamental differences in

economic standpoints and views emerged during the early developments in

Austrian capital theory – a dichotomy which even today is not yet fully resolved.

• Capital theory is beset with many perplexities and ambiguities. Most of the

theoretical difficulties stem from the fact that capital has a monetary unit of

measure corresponding to worker-hours of labour and acres of land. The "quantity

of capital," then, has no clear empiric meaning. If capital is accounted for in

physical terms, then gauging the total quantity of Liquid and Fixed Assets involves

an insurmountable aggregation problem; if it is reckoned in monetary value terms,

then the quantity of capital becomes dependent upon its own notional price. Similar

difficulties are associated with the measurement of production efficiency or the

degree of comparative efficiency of production processes.

The Austrian Vision

• If two processes are compared strictly in terms of their respective merits, it

may be unclear which of the two blueprints is the more robust; if capital

values are used in gauging the comparative degrees of efficiency, then the

comparison will depend in a critical way on the rate of interest used to

calculate the capital values. Attempts to spell out the precise relationship

between the rate of interest and the degree of sustainability of a process are

bound to run afoul of these difficulties—as was roundly demonstrated during

the controversies of the 1960s over "technique switching" and "capital

reversing."

• Such perplexities and ambiguities, however, are largely if not wholly

irrelevant to the early development of capital theory. What is important about

the theoretical developments over the final thirty years of the nineteenth

century is the new vision of capital and of a capital-using economy. Essential

to this new vision were the ideas that using capital takes time, that time, in

fact, is one of the dimensions of the economy's capital structure. Production

time, or the degree of efficiency, was recognized—and highlighted—as an

object of choice to be dealt with by economic theory.

The Austrian Vision

• Some treatment of the time element can be found in British economics,

particularly in Ricardo's discourse on machinery, and even in early French

writing such as that of Turgot. But the Austrian ideas about capital and time

constitute a significant break from Classical economic doctrine and from the

corresponding vision of capital. Dominated as it was by agricultural

production, Classical economics treated the time element in production

simply as a datum point along a temporal continuum (timeline).

• The very nature of agriculture dictated that the production period, the period

for which wages had to be "advanced" from capitalists to farm workers, was

one year. Formal economic theory was required to take this time constraint

into account, but it was not required to account for the timing difference itself.

The new vision required the treatment of time as a fundamental variable in

any theory of a capitalist economy. Characterizing it further requires that we

speak of visions and recognize the differences between the early visionaries -

particularly between Menger and Böhm-Bawerk.

The Austrian Vision

• Menger's and Böhm-Bawerk's contributions can be assessed in the light of the

distinction made by Ludwig Lachmann [1969, pp. 89-103 and 1978, pp. 8ff and

passim] between two opposing methods of economic analysis: subjectivism

(qualitative, narrative) and formalism (structured, technical, quantitative). For

subjectivists, economic phenomena can be made intelligible only in terms of the

intentions and plans of market participants; for formalists, economic measures

(econometrics), such as inputs, outputs, and production time, can be related to

one another without specific reference to the plans and actions of individuals.

• Menger's harsh assessment of Böhm-Bawerk's contribution is well reported in

modern literature: "[T]he time will come when people will realize that Böhm-

Bawerk's theory is one of the greatest errors ever committed" [Schumpeter,

1954, 847, n. 8]. Schumpter was well know for vigorously attacking his rivals

(e.g. Nicolai Kondriatev) and dismissing both their standpoints and views.

Although the context in which this statement was made remains a matter for

conjecture, a prevalent—and plausible—interpretation of this comment is that

Böhm-Bawerk may have strayed too far from the subjective value theory outlined

by Menger [Endres, 1987, p. 291 and passim, Kirzner, 1976, pp. 54-58, von

Mises, 1966, pp. 479ff, and Streissler and Weber, 1973, p. 232].

The Austrian Vision

• One of the reasons for the “Austrian School of Economics” loss of prominence during

the 1930s is that Austrian School macroeconomic theory could not be adequately

formalized within a mathematical framework - as was John Maynard Keynes's

General Theory of Economics.

• Lachmann's distinction between the two approaches of subjectivism and formalism,

yields some insights into understanding the development and history of Austrian

capital theory: -

– Menger was a thoroughgoing subjectivist

– Böhm-Bawerk straddled the fence between subjectivism and formalism

– Pawel Ciompa, Ragnar Frisch and Joseph Schumpter favoured formalism

• Böhm-Bawerk‘s formalism underlies what Menger saw as one of the greatest

errors; his subjectivism allows for a new mathematical interpretation of economic

theory which is, nonetheless, still thoroughly consistent with the subjectivism of

Menger's own work.

The Austrian Vision

• The present temporal interpretation considers Menger's judgment as it applies to the

treatment of the time element in the structure of capital. It is now argued that the

subsequent development of Austrian capital theory along quantitative and objective lines

(e.g. by Wicksell) - rather than along qualitative and subjective lines (e.g. by von

Mises) provides some small justification for Menger's use of the superlative: "one of the

greatest errors."

• F.A. Hayek won the Nobel Prize in 1973 for his work in unifying Austrian School

Economics by integrating these competing economic theories – a foundation step

towards a future Standard Economic Model. Economic Research today into the

Austrian School Theory of Capital and Business Cycles is a validation and continuation

of this resurgence of interest in the Austrian School.

• There are several related, but not perfectly synonymous methodological contrast evident

between the two opposing methods of economic analysis: subjectivism (narrative,

qualitative analysis) versus formalism (structured, technical, quantitative analysis): -

– causal-layer analysis (CLA) versus simultaneous determinacy (human actions)

– market-process analysis versus equilibrium theory

– microeconomic analysis versus macroeconomic modelling.

The Austrian Vision

• The Austrian School can be seen as an economic methodology or approach

which is wary of the unintended consequences of government intervention and its

effect on the price system - which is seen as the coordinating and regulating

factor in a society’s economy.

• The Austrian School methodology prioritizes subjectivism (logical reasoning) over

formalism (empirical analysis) because it assumes the economy is too complex to

model causality, and, that many institutions exist because they evolved as society

developed - and thus belonged in society for a reason

• The Austrian School considers the individual as entrepreneur as basis for

analytical approach and the subjectivity of decision-making. It is thus skeptical of

the validity of other economic schools of thought, especially those using

generalized aggregations.

• Hayek later in life lost faith in general equilibrium theory, thus in recent years

agent-based modeling has taken front and centre stage as a valid method for

empirical evaluation of many of the concepts and theories of the Austrian School.

Human Actions

• Human Action is the execution of purposeful behaviour in order to

achieve a more satisfactory state of affairs in an effort to improve a

less satisfactory situation. The history of the life of man is simply an

incessant sequence of Human Actions accumulated over a lifetime.

Human Actions

• There is in modern Chinese folklore, an urban legend about a fast food peddler who set

up his shop at the gate of the Chinese stock exchange and ended up making a killing on

both stocks and shares and his own food products, come rain or shine in any market

conditions. When pressed about his secret of success, he said,

• “Well, it's simple. When my stand gets really crowded with traders, I know that stock

market volume is falling, and so is probably heading towards a price adjustment - so I

sell my stocks. When there is barely any food sales for a long time, I know market

volume is rising, driving prices up - so it's time to buy“.....

•

What does this fable tell us? While the exact scenarios of the ebbs and flows of each

business cycle may vary: from the gold rush of yesteryears to the sub prime crisis, but

one thing is certain that we as a species always go overboard when it comes to greed

and fear - the masses' "maniac" index. Digital technology is now being used to measure

and forecast changes in market sentiment.

• When there is unmistakable over-exuberance in the air, ring all the alarm bells.....

The Nature of Randomness

Classical Mechanics (Newtonian Physics)

– governs the behaviour of everyday objects

– any apparent randomness is as a result of Unknown Forces, either internal or external,

acting upon a System.

Quantum Mechanics

– governs the behaviour of unimaginably small objects (such as sub-atomic particles)

– all events are truly and intrinsically both symmetrical and random (Hawking Paradox).

Relativity Theory

– governs the behaviour of impossibly super-massive cosmic structures

– any apparent randomness or asymmetry is as a result of Unknown Forces acting early

in the history of Time-space

Wave Mechanics (String Theory)

– integrates the behaviour of every size and type of object

– any apparent randomness or asymmetry is as a result of Unknown Dimensions acting

in the Membrane or in Hyperspace

Economics - Understanding Human Actions

Classical Economists

Four of the most important founding Classical Economists were Adam

Smith, Thomas Malthus, David Ricardo and John Stuart Mill

Each was a highly original thinker, each discovered fundamental

economic principles and each developed important economic theories

that transformed global economic systems over many generations.....

Classical Economists

Human Population – Thomas Malthus Adam Smith – the Invisible Hand

Free Market Economy – David Ricardo Utilitarianism – John Stuart Mill

The Classical Theory of Economics

• Of the great classical economists - Adam Smith, Thomas Malthus, David Ricardo

and John Stuart Mill are widely recognised as being the most gifted and influential

founding fathers of Classical Economic Theory.

• In the late eighteenth and early nineteenth centuries – Adam Smith was the first

philosopher to establish the Classical Theory of Economics, Thomas Malthus

published his theory of population dynamics and its relationship with the availability

of scarce resources, David Ricardo is credited as being the first to rationalise,

standardise and systemise the study of economic science - whilst John Stewart Mill

published a large number of books on philosophy and economics, which include: – A

System of Logic (1843), Principles of Political Economy (1848), On Liberty (1859),

Considerations on Representative Government (1861) and Utilitarianism (1861).

• These noted economists have all proposed important economic theories that have

advanced both the scientific theory and applied practice of economics – as well as

contributing towards building the body of academic knowledge in economics. In this

section, we will examine the history of classical economics, important principles and

theories and their impact within the context of our exploration of business and

economic waves, cycles, patterns and trends.

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The Classical Theory of Economics • The fundamental principle of The Classical Theory of Economics is that

the economy is a self‐regulating system – guided only by the invisible hand of the free market. Classical economists maintain that the economy is always capable of achieving the natural level of real GDP or output, which is the level of real GDP that is obtained when the economy's resources are fully employed.

• While circumstances arise from time to time that cause the economy to fall below or to exceed the natural level of real GDP self-adjustment mechanisms exist within the market system that work to bring the economy back to the natural equilibrium level of real GDP. The classical doctrine - which is that the free market economy is always at or near the natural level of real GDP - is based on two firmly held beliefs: Say's Law and the belief that prices, wages, and interest rates are flexible.

• Say's Law. According to Say's Law, when an economy produces a certain level of real GDP, it also generates the income needed to purchase that level of real GDP. The economy is thus always capable of demanding all of the output that its workers and firms choose to produce - hence, the economy is always capable of achieving the natural level of real GDP.


• The achievement of the natural level of real GDP is not as simple as Say's

Law would seem to suggest. While it is true that the income obtained from

producing a certain level of real GDP must be sufficient to purchase that level

of real GDP, there is no guarantee that all of this income will be spent. Some

of this income will be saved. Income that is saved is not used to purchase

consumption goods and services, implying that the demand for these goods

and services will be less than the supply.

• If the level of aggregate demand falls below aggregate supply due to

aggregate saving, suppliers will cut back on their production and reduce the

number of resources that they employ. When employment of the economy's

resources falls below the full employment level, the equilibrium level of real

GDP also falls below its natural level. Consequently, the economy may not

achieve the natural level of real GDP if there is aggregate saving.

• The classical theorists' response is that the funds from aggregate saving are

eventually borrowed and turned into investment expenditures, which are a

component of real GDP. Hence, aggregate saving need not lead to a

reduction in real GDP.


• Consider, however, what happens when the funds from aggregate saving

exceed the needs of all borrowers in the economy. In this situation, real GDP

will fall below its natural level because investment expenditures will be less

than the level of aggregate saving. This situation is illustrated in Figure 1.



• Aggregate saving, represented by the curve S, is an upward‐sloping function of the interest rate; as the interest rate rises, the economy tends to save more. Aggregate investment, represented by the curve I, is a downward‐sloping function of the interest rate; as the interest rate rises, the cost of borrowing increases and investment expenditures decline. Initially, aggregate saving and investment are equivalent at the interest rate, i. If aggregate saving were to increase, causing the Scurve to shift to the right to S′, then at the same interest rate i, a gap emerges between investment and savings. Aggregate investment will be lower than aggregate saving, implying that equilibrium real GDP will be below its natural level

• Flexible interest rates, wages, and prices. Classical economists believe that under these circumstances, the interest rate will fall, causing investors to demand more of the available savings. In fact, the interest rate will fall far enough—from i toi′ in Figure —to make the supply of funds from aggregate saving equal to the demand for funds by all investors. Hence, an increase in savings will lead to an increase in investment expenditures through a reduction of the interest rate, and the economy will always return to the natural level of real GDP. The flexibility of the interest rate as well as other prices is the self‐adjusting mechanism of the classical theory that ensures that real GDP is always at its natural level. The flexibility of the interest rate keeps the money market, or the market for Credit (loan funds), in equilibrium all the time and thus prevents real GDP from falling below its natural level.


• Graphical illustration of the classical theory as it relates to a

decrease in aggregate demand. Figure considers a decrease in

aggregate demand from AD 1to AD 2.


• Similarly, flexibility of the wage rate keeps the labour market, or the market for

workers, in equilibrium all the time. If the supply of workers exceeds firms' demand

for workers, then wages paid to workers will fall so as to ensure that the work force is

fully employed. Classical economists believe that any unemployment that occurs in

the labour market or in other resource markets should be considered voluntary

unemployment. Voluntarily unemployed workers are unemployed because they

refuse to accept lower wages. If they would only accept lower wages, firms would be

eager to employ them.

• The immediate, short‐term effect is that the economy moves down along the SAS

curve labelled SAS 1, causing the equilibrium price level to fall from P 1 to P 2, and

equilibrium real GDP to fall below its natural level of Y 1 to Y 2. If real GDP falls below

its natural level, the economy's workers and resources are not being fully employed.

• When there are unemployed resources, the classical theory predicts that the wages

paid to these resources will fall. With the fall in wages, suppliers will be able to

supply more goods at lower cost, causing the SAS curve to shift to the right from

SAS 1 to SAS 2. The end result is that the equilibrium price level falls to P 3, but the

economy returns to the natural level of real GDP.

Free Market Economics – the Invisible Hand

[The rich] consume little more than the poor, and in spite of their natural

selfishness and rapacity…they divide with the poor the produce of all their

improvements. They are led by an invisible hand to make nearly the same

distribution of the necessaries of life, which would have been made, had the

earth been divided into equal portions among all its inhabitants, and thus

without intending it, without knowing it, advance the interest of the society,

and afford means to the multiplication of the species.

• The Wealth of Nations • Adam Smith •

Adam Smith

Adam Smith and the Invisible Hand of the Free Market

• Adam Smith, who lived from about 1723 to 1790, was an Economist. a Philosopher

and a Scot. He is considered to be the founder of modern economics. Smith, who's

exact date of birth is unknown, was baptised on 5 June 1723. His father, a customs

officer in Kirkcaldy, died before he was born. Adam Smith studied at Glasgow and

Oxford Universities. He returned to Kircaldy in 1746 and two years later was asked

to give a series of public lectures in Edinburgh - which established his reputation.

• In 1751, Smith was appointed professor of logic at Glasgow University and a year

later became professor of moral philosophy. He became a member of a brilliant

intellectual circle that included David Hume, John Home, Lord Hailes and William

Robertson. During 1764, Smith left Glasgow to travel to the Continent as a tutor to

Henry, the future Duke of Buccleuch. While travelling, Smith met a number of leading

European intellectuals and Philosophers, including Voltaire, Rousseau and Quesnay.

• In 1776, Smith moved to London where he published a volume which he intended to

be the first part of a complete theory of society, covering theology, ethics, politics and

law. This volume, 'Inquiry into the Nature and Causes of the Wealth of Nations',

was the first major work in the science of political, social and geographic economics.

Adam Smith

Adam Smith and the Invisible Hand of the Free Market • At the time of Adam Smith, the Enlightenment, philosophy was a study of the human

condition and the circumstances under which man lived - an all-encompassing inquiry into the nature and meaning of existence. A deep examination of the affairs of the world of commerce led Smith to the conclusion that collectively individuals in society - each acting in his or her own self-interest - managed to purchase the raw materials and produce the goods and services that collectively society requires.

• Smith called the mechanism by which this self-regulation occurs “the invisible hand” of the free market in his groundbreaking book, The Wealth of Nations, published in 1776 - the same year as America's Declaration of Independence. Smith argued forcefully against Government regulation of commerce and trade, and wrote “if all people were set free to better themselves, it would encourage greater economic prosperity for all”. Surely the 13 Colonies would have remained British.....

• While Smith couldn't demonstrate directly the empiric existence of the “invisible hand” of market forces, he presented many instances and examples of its influence in society. Essentially, the butcher, the baker, and the candlestick maker individually go about their business. Each produces the amount of meat, bread, and candlesticks he judges to be correct. Each buys the amount of meat, bread, and candlesticks that his household needs. All of this happens without their consulting one another - and without all the king's men telling them how much to produce and when to produce it .

Adam Smith

Adam Smith and the Invisible Hand of the Free Market • In discovering the “invisible hand” of the free market as a self‐regulating system

Smith founded of Classical Economics – both as a Human Philosophy and as an important Economic Principle –- as the guiding principle and fundamental premise of the science of The Classical Theory of Economics This important guiding principle was to be revisited and expanded upon by future generations of economists.

• The key doctrine of classical economics is the philosophy of a “light hand” of minimal intervention by central government - laissez-faire - so the “invisible hand” of free market economics will guide market participants in their economic endeavours, in order to create the greatest economic good for the greatest number of people, and foster economic growth. Smith also explored the dynamics of the labour market, wealth accumulation, and productivity growth. His work gave later generations of economists much to think about, debate and expand upon – not least members and acolytes of the Austrian School of Real Economics - Joseph Schumpter, Ludwig von Mises and Friedrich Hayek.

• In 1778, Smith was appointed commissioner of customs in Edinburgh. In 1783, he became a founding member of the Royal Society of Edinburgh. Adam Smith died in the city of Edinburgh on 17 July 1790.

Production and Exchange of Value

• Adam Smith opens The Wealth of Nations by explaining that the production and exchange of

value (wealth), and that the contribution of value production and exchange to national income.

Using the example of a pin factory, Smith shows how specialisation – breaking down the

production process into small tasks which can be performed repetitively by one person in one

place at one time - can enormously boost overall manufacturing capacity and productivity.

• Through specialisation, Labourers can optimise the return on their efforts by reputedly

performing a task based on an acquire skill - in order to maximise their earnings over any given

period of time. Factory owners may also employ labour-saving machinery to increase production

capacity – and thus increase the capacity to create wealth. Specialist products manufactured in

this way may then be sold or exchanged for money or bartered for other goods - thus spreading

the benefits of specialisation of labour and machinery across the wider population as a whole.

• How far and how fast the benefit of wealth creation spreads through society depends on how

widespread and efficient is the market. Market participants may try to artificially influence market

conditions - and call upon governments to sanction restrictive practices in order in order to help

them “rig markets” –even lobbying governments to pass protectionist legislation to better serve

their own selfish interests – such as imposing an import tax on goods originating from foreign

competitors. The best interests of all the participants in a free market are served if policymakers

avoid such restrictive market interventions - and promote fair and open competition.

Capital - the Accumulation of Wealth

• Smith goes on to identify that the accumulation of wealth (building up capital) - is an essential condition for economic progress. The acquisition of wealth – by saving some of the value that is produced instead of consuming all of it immediately – allows, over time, the investment of that capital in different ways. Thus capital investment might allow us to design and build new, dedicated, labour-saving machinery in order to improve manufacturing process – or to combine existing resources in novel and innovative ways in order to produce new products and services. As we increase our capital investment in manufacturing, we might expect our total unit production output (capacity) to soar dramatically – and in doing so production process become cheaper and more efficient per unit of production.

• Thanks to this growth of capital, prosperity becomes an expanding pie: everyone becomes richer. It is a virtuous circle. - but capital can also be lost, through mistakes and errors of judgement, fraud, theft, as a result of war, acts of terrorism or civil disorder - or via taxation and profligate government spending. Governments should aim to allow people to build up capital in the confidence that they will enjoy its fruits, and should be aware that their own taxation and spending will eat into the nation’s productive capital.

Economic Policy and the role of the Free Market

• Just as individuals gain from specialisation, says Smith, so do nations. There is no point in

trying to grow grapes to produce wine in Scotland - when grapes grow in abundance in

the warmer climes of France. Wheat and Oates and Barley grow plentifully in the cooler,

wetter Scottish climate. Scottish Farmers sell Barley to Whiskey producers – who ferment

the Barley to create worst and distil the worst into Whisky. Scottish merchants can then

transport Whiskey to France and sell it to French Consumers – and are then able to buy

French Wine - which they transport back home and sell to wine merchants in Scotland.

• Trading Economies should do what they are best at – which is to manufacture and trade

their specialised goods for transport to those Markets in those Countries where there is a

strong demand for them. Restrictions on international trade inevitably make both Countries

poorer. Legislators think too much of themselves if they believe that by their intervention in

the free market process - they can direct economic production better than market forces.

Economic Policy and the role of Government

• Smith is critical of government and officialdom - but is no champion of laissez-faire. He

believes that the market economy he has described can function and deliver its benefits

only when its rules are observed – when property is secure and contracts are honoured.

The maintenance of justice and the rule of law is therefore vital. So is defence - if property

can be stolen by raiders or looted by a foreign power, we are no better off than if our own

neighbours make off with it. Adam Smith sees a role for education and public works too, in

as much that as these collective projects make it easier for trade and markets to operate.

• Where tax has to be raised for these purposes, it should be levied in proportion to the

people’s ability to pay, and it should be at set rat fixed rates rather than arbitrary, it should

be easy to pay, and it should aim to have minimal side effects. Governments should avoid

taxing capital, which is essential to the nation’s productivity. Most Government spending is

for current-year consumption – so Governments should also avoid building up large fiscal

debts, which draws a nations capital away from future production – thus impoverishing it.

Human Population – Thomas Malthus

Population, when unchecked, goes on doubling itself every 25 years or

increases in a geometrical ratio.

• An Essay on the Principle of Population • Thomas Malthus •


• Few economists have had such controversial ideas, and generated a debate on such a

scale as Thomas Malthus. In “An Essay on the Principle of Population”, published in

1798, the English economist made public his theory on population dynamics and its

relationship with the availability of scarce resources. This essay was the result of his

scepticism towards positivist theorists, praising the perfectibility of man and greeting the

advances and diffusion of human knowledge as a source of welfare and freedom for future

generations. Disagreeing with such Utopian perspectives, Malthus maintained that the

development of mankind was severely limited by the pressure that population growth

exerted on the availability of scarce resources – Food, Energy and Water (FEW).

• The foundation of Malthus' theory relies on two assumptions that he views as fixed,

namely that food and passion between sexes are both essential for human's existence.

Malthus believed that the world's population tends to increase at a faster rate than does its

food supply. Whereas population grows at a geometric (exponential) rate, the production

capacity only grows at a linear rate (arithmetically). Therefore, in the absence of consistent

checks on population growth, Malthus made the gloomy prediction that in a short period of

time, scarce resources will have to be shared among an increasing number of individuals.

However, such checks that ease the pressure of population explosion do exist, and

Malthus distinguishes between two categories, the preventive check and the positive

check. The preventive check consists of voluntary limitations of population growth. .


• The positive check consists of limitations to population growth by war,

famine and disease. Malthus distinguishes between two categories, the

preventive check and the positive one. The preventive check consists of

voluntary limitations of population growth. Individuals, before getting

married and building a family, make rational decisions based on the

income they expect to earn and the quality of life they anticipate to

maintain in the future for themselves and their families. The positive check

to population is a direct consequence of the lack of a preventive check.

• When society does not limit population growth voluntarily, then diseases,

famines and wars act to reduce population size and establish the

necessary balance of population with resources. According to Malthus, the

positive check acts more intensively in lower classes, where infant

mortality rates are higher and unhealthy conditions are more common.

• The preventive and positive checks, by controlling population growth,

eventually close the mismatch between the level of population and the

availability of resources, but the latter at a cost of creating misery and

wretchedness that are unavoidable and are beyond the control of man.


• Under this perspective, technological improvements that contribute to the

increase in agricultural yields will only produce a temporary increase in living

standards, but will be offset in the long run by a correspondent increase in

population size that will cancel the temporary relief. Migrations could alleviate

the effects of the positive check, but Malthus considers this possibility

unfeasible, as general conditions were too harsh in possible receiving countries

• Malthus was strongly opposed to monetary transfers from richer to poorer

individuals. According to him, increasing the welfare of the poor by giving them

more money would eventually worsen their living conditions, as they would

mistakenly be lead to think that they can support a bigger family, which would in

turn depress the preventive check and generate higher population growth. At

the end of this process, the same amount of resources has to be split between

a larger population, triggering the work of the positive check to populations.

Moreover, immediately after such a transfer, people can afford buying more

food, bidding its price up and decreasing real wages, which hurt poor

individuals whose main income comes from their labour.


• For these reasons, Malthus, together with other distinguished economists like David Ricardo, were opposed the English Poor Laws - legislation that gave relief to poor and unemployed people, and played a central role in the Poor Laws reform in 1834. He held that it is better for a family to foresee its lack of ability to support children before having them – than having to deal with subsequent famine, diseases and infant mortality. In other words, taking for granted that checks on populations are unavoidable, it is better to use the preventive check rather than the positive check.

• Malthus realised that it was implicit in his model that if real wages were determined by the free market – they would always be pinned down to the subsistence level. If real wages were above this level, population would begin to grow, inducing a decline in nominal wages as a result of firms having a larger supply of labour available. Moreover, the larger population would result in an increase in the demand for goods, which would force prices to go up and real wages to decrease to their subsistence level.

• This concept was known as the Iron Law of Wages, and, although first conceptually formalized by Ricardo in 1817, it was a theme constantly present in Malthus's work. We can still see this Iron Law of Wages in operation in Western Society today – where wages have not risen in real terms in the USA for over Forty years – and have been static for over Twenty years in the UK

Free Market Economy – David Ricardo

"The proportions, too, in which the capital that is to support labour, and the capital that is invested in tools, machinery and buildings, may

be variously combined."

• Principles of Political Economy and Taxation • David Ricardo •

David Ricardo

• David Ricardo (1772-1823) was a British political economist and one of the most

influential of the classical economists who has often been credited with rationalising,

standardising and systemising the theory of economic science - along with Thomas

Malthus, Adam Smith, and John Stuart Mill David Ricardo was also a member of

Parliament, Businessman, financier and speculator, who amassed a considerable

personal fortune. Perhaps his most important contribution to the economics science

was the theory of comparative advantage - a fundamental argument in favour of

both free trade among countries and of specialisation of labour among individuals.

• Ricardo was born in London on 19 April 1772, the third son of a Dutch Jew who had

made a fortune on the London Stock Exchange. At the age of just 14, Ricardo joined

his father's business and quickly demonstrated a strong grasp of economic principles

in business affairs. In 1793 Ricardo married Priscilla Anne Wilkinson - a Quaker -

and Ricardo converted to Christianity to become a Unitarian. This caused a breach

with his father and obliged Ricardo to establish business on his own - continuing as a

member of the stock exchange, where his ability won him the support of an eminent

banking house. Ricardo prospered to such an extent that in a few years he acquired

a substantial fortune. Financial independence enabled him to pursue his interests in

literature and science - particularly in mathematics, chemistry, and geology.

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David Ricardo

• In 1799 David Ricardo read Adam Smith's Wealth of Nations and for the next ten years he studied economics. His first pamphlet was published in 1810: entitled The High Price of Bullion, a Proof of the Depreciation of Bank Notes, as an extension of the letters that Ricardo had published in the Morning Chronicle in 1809. Ricardo argued in favour of a sterling paper currency backed by the gold standard – thus providing a fresh stimulus to the controversy around the fiscal policies of the Bank of England. The Money Supply crisis created by Wars with France (1792-1815) has in 1797 caused Pitt's government to suspend the annual cash interest payments by the Bank of England to Government Bond holders. Consequently, there had been an increase in the volume of lending and the printing of paper currency. This created a climate of inflation. Ricardo said that inflation affected foreign exchange rates as well as the flow of gold bullion.

• In 1814, at the age of 42, Ricardo retired from business and took up residence at Gatcombe Park in Gloucestershire, where he had extensive landholdings. In 1819 he became MP for Portarlington. He did not speak often, but his free-trade views were received with respect - although they opposed the economic thinking of the day. Parliament was made up mostly of wealthy landowners who wished to maintain the Corn Laws in order to protect the income from their estates.

http://www.victorianweb.org/economics/smith.html

http://www.victorianweb.org/history/cornlaws1.html

David Ricardo

• David Ricardo became friends with a number of eminent intellectuals, among

whom were the philosopher and economist James Mill (father of John Stuart

Mill), the Utilitarian philosopher Jeremy Bentham and Thomas Malthus, who

was best known for his pamphlet, Principles of Population published in 1798.

Ricardo accepted Malthus' ideas on population growth. In 1815 another

controversy arose over the Corn Laws, when the government passed new

legislation that was intended to raise further the duties on imported wheat.

• In 1815 Ricardo responded to the Corn Laws by publishing his Essay on the

Influence of a Low Price of Corn on the Profits of Stock, in which he

argued that raising the duties on imported grain had the effect of increasing

the price of corn and hence increasing the income of landowners and the

aristocracy at the expense of the ability of the rising industrial working classes

to afford Bread. Ricardo said that the abolition of the Corn Laws would help to

distribute the national income towards the most productive groups in society.

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David Ricardo

• In 1817, Ricardo published Principles of Political Economy and Taxation in

which he analysed the distribution of money among the landlords, workers, and

owners of capital. He found the relative domestic values of commodities were

dominated by the quantities of labour required in their production, rent being

eliminated from the costs of production. He concluded that profits vary inversely

with wages, which move with the cost of necessaries, and that rent tends to

increase as population grows, rising as the costs of cultivation rise. He was

concerned about the population growing too rapidly, in case it depressed wages

to the subsistence level, reduce profits and checked capital formation.

• The Bullion Committee was appointed by the House of Commons in 1819: it

confirmed Ricardo's views and recommended the repeal of the Bank Restriction

Act. In 1814, at the age of 42, Ricardo retired from business and took up

residence at Gatcombe Park in Gloucestershire, where he had extensive

landholdings. In 1819 he became MP for Portarlington. He did not speak often

but his free-trade views were received with respect, although they opposed the

economic thinking of the day. Parliament was made up of landowners who

wished to maintain the Corn Laws in order to protect their profits.

http://www.victorianweb.org/history/bullion.html


David Ricardo

• David Ricardo discovered and formulated the law of comparative advantage -

probably somewhere around the first two weeks of October 1816. The date itself is

not significant, but his letters at the time reveal how Ricardo’s mind was working

when he postulated the law. These letters show how his mind ranged over much of

the terrain of trade and market theory - from factor price equalisation conditions to

the Ricardian Economic Model . We may also conjecture that the hardest part of

his discovery may well have been defining the key assumption of Factor Immobility.

• Ricardo postulated that there is a mutual benefit from trade (or exchange) - even if

one party (e.g. a resource-rich country, in a high-technology, free market economy

with a highly skilled artisan workforce) is more productive in every possible way than

its polar opposite trading counterparty (e.g. a resource-poor country, with a relatively

low technology base and a Government-controlled centrally-planned and regulated

market economy featuring a largely unskilled labour force) – just as long as each

trading counterparty concentrates on exploiting those resources and manufacturing

activities where it has obtained a relative productivity advantage.

David Ricardo

• The Ricardian Economic Model refers to the economic theories of David Ricardo,

an English political economist who was born in 1772 and made a fortune as a banker

loan broker and stockbroker. At the age of 27, Ricardo read An Inquiry into the

Nature and Causes of Wealth of Nations by Adam Smith and was fascinated by

Smiths theories of economics. Ricardo's’ main economic theories are outlined in his

work On the Principles of Political Economy and Taxation (1817). This sets out a

series of social and economic theories which would later become the underpinnings

of Marx's Das Kapital and Marshallian economics - including the theory of rent, the

labour theory of value and above all the theory of comparative advantage.

• Ricardo wrote his first economic article ten years after reading Adam Smith and

ultimately, the "bullion controversy " gave him fame in the economic community for

his theory on inflation in 19th-century England. This economic principle became

known as monetarism - the economic theory that an excess of currency (Money

Supply) leads to inflation. Ricardo was also a founding father in creating and

formalising the principles of classical economics – and as such, he advocated a free

market economy - free trade and free competition - without the burden of government

interference of enforcing market economy restrictions. or protective trade laws


http://en.wikipedia.org/wiki/Political_economist

http://en.wikipedia.org/wiki/Stockbroker

http://en.wikipedia.org/wiki/The_Wealth_of_Nations

http://en.wikipedia.org/wiki/The_Wealth_of_Nations


http://en.wikipedia.org/wiki/On_the_Principles_of_Political_Economy_and_Taxation

http://en.wikipedia.org/wiki/Das_Kapital

http://en.wikipedia.org/wiki/Das_Kapital

http://en.wikipedia.org/wiki/Alfred_Marshall

http://en.wikipedia.org/wiki/Comparative_advantage

http://en.wikipedia.org/wiki/Precious_metal

http://en.wikipedia.org/wiki/Inflation

http://en.wikipedia.org/wiki/Monetarism

http://en.wikipedia.org/wiki/Currency


http://en.wikipedia.org/wiki/Free_trade

http://en.wikipedia.org/wiki/Competition

http://en.wikipedia.org/wiki/Trade_barrier

Utilitarianism – John Stuart Mill

“It is better to be a human being dissatisfied than a pig satisfied;

better to be Socrates dissatisfied than a fool satisfied. And if the

fool, or the pig, are of a different opinion, it is only because they

know only their own side of the question.”

• Utilitarianism • John Stuart Mill •

John Stuart Mill

• John Stuart Mill, the eldest son of the philosopher, James Mill and Harriet

Barrow (whose influence on Mill was vastly overshadowed by that of his

father), was born in London on 20th May, 1806. Educated a home by his

father, the young John Stuart Mill had studied the works of Aristotle, Plato,

Jeremy Bentham, Thomas Hobbes, David Ricardo and Adam Smith by the

time he had reached the age of twelve.

• James Mill, a struggling man of letters, wrote a definitive History of British

India (1818), and the work landed him a coveted position in the East India

Company, where he rose to the post of chief examiner. When not carrying out

his administrative duties, James Mill spent considerable time educating his son

John, who began to learn Greek at age three and Latin at age eight. By the

age of 14, John was extremely well versed in the Greek and Latin classics;

had studied world history, logic and mathematics; and had mastered the basics

of economic theory, all of which was part of his father’s plan to make John

Stuart Mill a young proponent of the views of the philosophical radicals.


http://www.spartacus.schoolnet.co.uk/ITlondon.htm

http://en.wikipedia.org/wiki/Aristotle

http://en.wikipedia.org/wiki/Plato

http://www.spartacus.schoolnet.co.uk/PRbentham.htm

http://en.wikipedia.org/wiki/Thomas_Hobbes


http://www.spartacus.schoolnet.co.uk/PRsmithA.htm

http://www.biography.com/people/james-mill-9408191

John Stuart Mill

• Under the tutelage of his imposing father, himself a historian and economist, John

Stuart Mill began his intellectual journey at an early age, starting his study of Greek

at the age of three and Latin at eight. Mill’s father was a proponent of Jeremy

Bentham’s philosophy of utilitarianism, and John Stuart Mill began embracing it

himself in his middle teens. Later, he started to believe that his rigorous analytical

training had weakened his capacity for emotion, that his intellect had been nurtured

but his feelings had not. This perhaps led to his expansion of Bentham’s utilitarian

thought, his development of the “harm theory,” and his writings in the defence of the

rights of women, all of which cemented his reputation as a major thinker of his day.

• Mill was especially impressed by the work of Jeremy Bentham. He agreed with

Bentham when he argued in Introduction to the Principles of Morals and Legislation

(1789), that the proper objective of all conduct and legislation is "the greatest

happiness of the greatest number". Mill became a Utilitarian and at the age of

seventeen formed a discussion group called the Utilitarian Society.


http://www.amazon.co.uk/s/ref=nb_sb_noss?url=search-alias=stripbooks&field-keywords=Jeremy+Bentham&x=20&y=14

http://www.spartacus.schoolnet.co.uk/PRunitarian.htm

http://www.spartacus.schoolnet.co.uk/PRunitarian.htm

John Stuart Mill

• By his late teens, Mill spent many hours editing Jeremy Bentham’s

manuscripts, and he threw himself into the work of the philosophic radicals (still

guided by his father). He also founded a number of intellectual societies and

began to contribute to periodicals, including the Westminster Review (which

was founded by Jeremy Bentham and James Mill). In 1823, his father secured

him a junior position in the East India Company, and he, like his father before

him, rose in the ranks, eventually taking his father's position of chief examiner.

• Mill also began having articles published in the Westminster Review, a journal

founded by Jeremy Bentham and James Mill to propagate Radical views. John

Stuart Mill also wrote for other newspapers and journals including the Morning

Chronicle and Parliamentary History & Review. Jeremy Bentham took an

active role in the campaign for parliamentary reform, and was one of the first to

suggest that women should have the same political rights as men.

http://www.spartacus.schoolnet.co.uk/Jwestminster.htm



http://www.spartacus.schoolnet.co.uk/Jchronicle.htm

http://www.spartacus.schoolnet.co.uk/Jchronicle.htm

John Stuart Mill

• Mill wrote a large number of books on philosophy and economics. This includes: A System

of Logic (1843),Principles of Political Economy (1848), On Liberty (1859), Considerations

on Representative Government (1861) and Utilitarianism (1861). “It is far better to be a

human being dissatisfied than a pig satisfied; better to be Socrates dissatisfied than a fool

satisfied. And if the fool, or the pig, are of a different opinion, it is only because they know

their own side of the question.” ― John Stuart Mill, Utilitarianism

• In the 1865 General Election John Stuart Mill was invited to stand as the Radical candidate

for the Westminster seat in Parliament. Barbara Bodichon, Emily Davies and Bessie

Rayner Parkes were enthusiastic supporters of his campaign as he spoke in favour of

women having the vote. One politician campaigning against Mill claimed that "if any man

but Mr Mill had put forward that opinion he would have been ridiculed and hooted by the

press; but the press had not dared to do so with him.“

• John Stuart Mill won the seat. The Times commented: "The very circumstances that this

eminent writer declared his most controversial opinions in his address, and subsequent

speeches, makes his return the more significant. Hundreds who voted for Mr Mill probably

disagreed with him philosophically, and a still greater number politically. But it is creditable

to the electors, and a hopeful sign for the metropolitan boroughs, that Westminster people

will rather have a man who thinks for himself, even though his conclusions may be fidder

from their own."








http://www.goodreads.com/author/show/57651.John_Stuart_Mill

http://www.goodreads.com/work/quotes/1086777

http://www.spartacus.schoolnet.co.uk/GE1865.htm

http://www.spartacus.schoolnet.co.uk/PRwestminster.htm

http://www.spartacus.schoolnet.co.uk/Wbodichon.htm

http://www.spartacus.schoolnet.co.uk/Wbodichon.htm

http://www.spartacus.schoolnet.co.uk/Wdavies.htm

http://www.spartacus.schoolnet.co.uk/Wbelloc.htm




http://www.spartacus.schoolnet.co.uk/PRtimes.htm

John Stuart Mill

• Frances Power Cobbe commented that Mill's attitude towards Helen Taylor was "beautiful

to witness, and a fine exemplification on his own theories of the rightful position of

women". As well as helping Mill with his books and articles, Helen Taylor was active in

the women's suffrage campaign. In December 1868, Mill and his step-daughter, resigned

from the Manchester National Society in protest against the leadership of Lydia Becker.

• Mill retained his interest in women's suffrage and on 7th October 1869, he wrote: "The

cause has now reached a point at which it has become extremely desirable that the ladies

who lead the movement should make themselves visible to the public, their very

appearance being a refutation of the vulgar nonsense talked about women's rights

women.“

• Although he was in favour of universal suffrage he was against it being mixed with

women's suffrage. He wrote to Charles Dilke on 28th May 1870: "Women's suffrage has

quite enemies enough, without adding to the number all the enemies of universal suffrage.

To combine the two questions would practically suspend the fight for women's equality,

since universal suffrage is sure to be discussed almost solely as a working men's question:

and when at last victory, comes, there is sure to be a compromise, by which the working

men would be enfranchised without the women."

http://www.spartacus.schoolnet.co.uk/Wcobbe.htm

http://www.spartacus.schoolnet.co.uk/Wcobbe.htm

http://www.spartacus.schoolnet.co.uk/WtaylorH.htm

http://www.spartacus.schoolnet.co.uk/women.htm

http://www.spartacus.schoolnet.co.uk/Wbecker.htm

http://en.wikipedia.org/wiki/Sir_Charles_Dilke,_2nd_Baronet

http://en.wikipedia.org/wiki/Sir_Charles_Dilke,_2nd_Baronet

Early 20th Century Economists

Karl Marx, John Maynard Keynes (later Lord Keynes), John

Kenneth Galbraith and Milton Friedman are widely recognized as

being the foremost and most influential of the early 20th century

Economists

Each was a gifted academic, and each developed competing

economic theories that transformed the world's economic systems

during the 20th Century.....

Economic Overview – 20th Century

• Many noted economists have proposed important economic theories which have advanced the science and practice of economics - well as the contributing towards the academic body of economic knowledge. In this section, we will examine some of the important early 20th century economists and their economic theories as they impact upon our exploration of business and economic cycles, patterns and trends.

• Karl Marx, John Maynard Keynes (later Lord Keynes), John Kenneth Galbraith and Milton Friedman are all widely recognized as being amongst the most influential Economists in the early 20th century – Karl Marx because he challenged capitalism and had such a forceful impact on the relationship between economics, society and politics - and John Maynard Keynes because he introduced new economic theories and policies in relation to the Money Supply – in doing so, prompted the adoption of new Government Policies in the pursuit of Economic Development. Keynes also played a key role in the founding of the International Monetary Fund and in other political and economic measures introduced at the end of World War II.

Karl Marx

Karl Marx: Capitalism is Exploitation!

• Karl Marx, a German economist and political scientist who lived from 1818 to 1883,

looked at capitalism from a more pessimistic and revolutionary viewpoint. Where Adam

Smith saw harmony and growth, Marx saw instability, class struggle, and decline. Marx

believed that once the capitalist (the guy with the money, the organisational skills and his

name over the factory door) has set up the means of production, then any value created

is via the labour involved in manufacturing the goods produced in that factory. In Marx's

view, presented in his 1867 tome Das Kapital (Capital), a capitalist's profits come from

exploiting labour- that is, from underpaying workers for the value that they are actually

creating. For this reason alone, Marx couldn't subscribe to a profit-oriented organisation.

• This situation of management exploiting labour underlies the class struggle that Marx saw

at the heart of capitalism, and he predicted that that struggle would ultimately bring an

end to the capitalist system. To Marx, class struggle is not only inherent in the system -

because of the tension between capitalists and workers - but also intensifies over time.

The struggle intensifies as businesses eventually become larger and larger, due to the

inherent efficiency of large outfits and their ability to withstand the cyclical crises that

plague the system. Ultimately, in Marx's view, Capitalist society moves to a two-class

system of a few wealthy capitalists and a mass of underpaid, underprivileged workers.

Karl Marx

Karl Marx: Capitalism is Exploitation!

• Marx predicted the fall of capitalism and movement of society toward communism, in

which “the people” (that is, the workers) own the means of production and thus have no

need to exploit labour for profit. Clearly, Marx's thinking had a tremendous impact on many

societies, particularly the USSR (Union of Soviet Socialist Republics) in the 20th century.

• In practice, however, two historical trends have undermined Marx's theories. Firstly,

socialist, centrally planned economies have proven far less efficient at producing and

delivering goods and services - that is, at creating the greatest good for the greatest

number of people - than have capitalist systems. Secondly, right up until the 1970’s,

workers' incomes in the West had risen over time, which challenges the theory that labour

is exploited in the name of profit. If workers' incomes are rising, they are clearly sharing in

the growth of the economy – so in a very real sense, they are sharing in the profits.

• That being so, real Labour Wages in the USA have remained static for the last forty years

– the massive wealth created over this period has mostly been retained by the owners of

the Capital - entrepreneurs and shareholders. Has Marx’s warnings and predictions about

capitalism come true - ultimately, has society in the USA become a two-class system - few

wealthy capitalists – the one percent “super-rich” – amongst a mass of poorly educated,

underpaid, underprivileged workers unable to pay for pensions, healthcare or education ?

Karl Marx

Karl Marx: Capitalism is Exploitation! • Marx even has something to say about weaknesses in capitalistic systems such

as monopolistic economies. While Marx's theories have been largely discredited, they are still fascinating and worth knowing about – not least because although Marx criticised Capitalism, he failed to propose or suggest any viable alternative.

• Large companies enjoy certain advantages over smaller ones and are able to manipulate market conditions in order to undercut or absorb their smaller rivals, as demonstrated by examples such as Standard Oil (now ExxonMobil) and General Motors, ConAgra and Dole in agriculture. and more recently Microsoft and IBM, in high technology, In addition to this, the distribution of wealth in the U.S.-style capitalism, which is a less regulated form of capitalism, more liable to market manipulation and corruption than that in Europe - tends to create a two-tier class system of “have's” and “have not‘s” by allowing wealth to be retained by the owners of Capital - in the hands of entrepreneurs and shareholders

• Labour Wages in the USA – as measured by consumer spending power – have remained stagnant for the last forty years (for over twenty years in the UK) as the massive wealth created through technology innovation over the previous four decades has been largely retained by Capitalists themselves - entrepreneurs and shareholders. Have Marx's warnings and predictions of capitalism creating under-privileged workers and an over-privileged “super-rich” come true at last?

John Maynard Keynes

John Maynard Keynes: The role of Government intervention in the Economy

• A new economic theory - Neo-liberal Keynesianism - appeared with the publication of

John Maynard Keynes’ “The General Theory of Employment, Interest and Money”.

Keynes had embraced a radically different set of economic assumptions, which lead to the

startling possibility of a strikingly new and different economic equilibrium - where the

economy could get stuck in a deep trough (stagnate) of simultaneous high unemployment

and high inflation – an economic condition from which it was very difficult to escape. Neo-

classical Economic theory presumed that this economic equilibrium – a stark alternative

economic condition compared with the norm, where the economy spiralled into a deep

state of inefficient economic equilibrium or stagnation – was implausible, and impossible .

• Keynes believed that there was only one way out of stagnation - for the government to

stimulate the economy by boosting Public Spending in order to increase money supply

which would flow into the into the private-sector and thus drive up demand for goods and

services. President Franklin D. Roosevelt lent this theory credibility when he launched his

“New Deal”, a massive public works programme to kick-start a stagnant economy. This

experiment was interrupted by the entry of the United States into World War II - creating a

war effort which simultaneously took millions of men out of the dole queue and into the

armed forces - as well as creating a host of new manufacturing jobs at extremely high levels of economic production for weapons, ammunition, ships and trucks and planes.

John Maynard Keynes

John Maynard Keynes: The role of Government intervention in the Economy

• John Maynard Keynes was a brilliant British economist who lived from 1883 to 1946, He

examined capitalism and came up with some extremely influential and persuasive insights

- quite different, however, from those of Karl Marx and, for that matter, Adam Smith. In

1936, Keynes published his General Theory of Employment, Interest and Money.

Keynes's theories mainly involved the public propensity to spend or save their disposable

income as their earnings rise - and the effects of this increased spending on the economy

as a whole.. The validity and desirability of Keynes's prescription for a sluggish economy -

using government spending to prime the pump—are still debated today.

• The significance of Keynes's work lies in the views he held about the role of Central

Government in a capitalist economy. During the Great Depression, when Keynes was

writing his General Theory of Employment, Interest and Money, unemployment in the

United Kingdom had reached about 25 percent and millions of workers had lost their jobs

as well as their life savings. There was no clear way out of the stagnation, so the

Government boosted spending on Public Works to kick-start the economy by increasing

the Money Supply - leading to serious political questions as to whether Smith's invisible

hand was still guiding the economy. Could this unprecedented Government intervention

cause the collapse of the free-market economy and the end of the Capitalist System?

Keynesian Economic Theory

• At the onset of the Great Depression 1927-29, many economists believed that : -

“left alone, markets were self-correcting and would return to an ‘equilibrium’ that efficiently

utilised capital, workers and natural resources… this was the inviolate and core axiom of

‘scientific economics’ itself…

• A month after the Great Crash, economists at Harvard University, had made a statement (from

Richard Parker - John Kenneth Galbraith: his life, politics and economics, 2005, p.12). that : -

“a severe depression like that of 1920-21 is outside the range of probability.”

• They could not have been more wrong. In a new theory, Neo-liberal Keynesianism, which

emerged with the publication of John Maynard Keynes’ “The General Theory of Employment,

Interest and Money.” - Keynes had made use of a radically different set of assumptions, which

could lead to a startling new possibility of an alternative and frightening economic equilibrium

consisting of simultaneous high unemployment and low income – a stark and different reality

where the economy could be forced into a deep state of inefficient economic equilibrium - or

stagnation. Such an economy would stagnate (get stuck in a deep trough) – a condition from

which it was very difficult to escape. In Neo-classical Economic theory – this economic condition

was thought to be both theoretically implausible and practically impossible.

Austrian School Economists

Joseph Schumpter, Ludwig von Mises and Frederich Hayek are

amongst the most important of the Austrian School Economists

Each was a “Master of Money” - a highly original thinker, each of whom

developed competing economic theories that transformed the world's

economic systems, and each attracted a strong following amongst

politicians and economic planners right up until the present day.....

Economic Overview – Austrian School

• Many noted economists have proposed important economic theories

which have advanced the science and practice of economics well as

developing and enriching the academic body of economic knowledge.

• In this section, we will examine the significance of the Austrian School

of Real Economics and the roles of the important economic heroes of

the movement – the “Masters of Money” – as they feature in context with

our exploration of business and economic cycles, patterns and trends.

• Of the Austrian School Economists – Joseph Schumpter was the first

to rationalise, standardise and integrate the theory of Business Cycles,

Ludwig von Mises published his theory of the principle of Human

Actions, and Frederich Hayek is credited as driving the last major

attempt to rationalise, standardise and integrate the body of knowledge

of modern economic science into the lucid Economic Theories of today.

Economics - Human Actions

• In his foreword to Human Action: A Treatise on Economics, the great Austrian School

Economist, Ludwig von Mises, explains that Complex Market Phenomena are simply: -

"the outcomes of endless conscious, purposeful human actions, made by countless

individuals exercising personal choices and preferences - each of whom is trying as

best they can to optimise their circumstances in order to achieve various needs and

desires. Individuals, through economic activity strive to attain their preferred

outcomes - whilst at the same time attempting to avoid unintended consequences

leading to unforeseen outcomes."

• Thus von Mises lucidly presents the basis of economics as the science of observing,

analysing, understanding and predicting intimate human behaviour (human actions –

micro-economics) – which when aggregated together in a Market creates the flow of

goods, services, people and capital (market phenomena – macro-economy).

All human actions – “are simple individual choices in response to subjective personal

value judgments, which ultimately determine all Market Phenomena – patterns of

innovation and investment, supply and demand, production and consumption, costs

and prices, levels of profits and losses, and ultimately real (Austrian) Gross

Domestic Production (rGDP) .....”

Economics - Human Actions

All human actions – “are simple individual choices in response to

subjective personal value judgments, which ultimately determine

all Market Phenomena – patterns of innovation and investment,

supply and demand, production and consumption, costs and

prices, levels of profits and losses, and ultimately real (Austrian)

Gross Domestic Production (rGDP) .....”

• Human Action: A Treatise on Economics • Ludwig von Mises •

• In his foreword to Human Action: A Treatise on Economics, the great Austrian School Economist, Ludwig von Mises, explains that complex market phenomena are simply "the outcomes of endless conscious, purposeful individual actions, by countless individuals exercising personal choices and preferences - each of whom is trying as best they can to optimise their circumstances in order to achieve various needs and desires. Individuals, through economic activity strive to attain their preferred outcomes - whilst at the same time attempting to avoid any unwanted outcomes leading to unintended consequences."

• Thus von Mises lucidly presents the basis of economics as the science of observing, analysing, understanding and predicting intimate human behaviour (human actions – or micro-economics) – which when aggregated creates the flow of goods, services, people and capital (market phenomena - or the macro-economy). Individual choices in response to subjective personal value judgments ultimately determine all market phenomena - patterns of supply and demand, production and consumption, costs and prices, and even profits and losses. Although commodity prices may appear to be set by economic planners in central banks under strict government control - it is, in fact, the actions of individual consumers living in communities and participating in their local economy who actually determine what the Real Economic value of commodity prices really are. As a result of the individual choices and collective actions exercised by producers and consumers through competitive bidding in markets for capital and labour, goods and materials, products and services throughout all global markets – ultimately the global economy is both driven by, and is the product of - the sum of all individual human actions.

Austrian School of Real Economics

Value Creation vs. Value Consumption

• We live in a natural world which once, at the birth of civilisation, was brimming to the full with innumerable and diverse natural resources. It is important to realise that Wealth was never bestowed on us “for free“ - simply as a bonanza of that abundant feedstock of natural resources.

• Throughout History, Wealth has always been extracted or created through Human Actions – the result of countless men executing primary Value Creation Processes throughout the last 200,000 years - Hunting and Gathering, Fishing and Forestry, Agriculture and Livestock, Mining and Quarrying, Refining and Manufacturing. Those Secondary Added Value Processes - such as Transport and Trading, Shipping and Mercantilism – serve only to Add Value to primary Wealth which was originally created by the labour of others executing primary Value Chain Processes.

• The Economic Wealth that we enjoy today as an advanced globalised society is not generated “magically” through discovery, intellectual effort or technology innovation - nor through market phenomena created by the efforts of brokers and traders - or even by monetarist intervention from economic planners or central bankers. Economic Wealth is as a result of the effort of man - Human Actions and primary Value Chain Processes generating Utility or Exchange Value

• Vast amounts of Wealth can also be created (and destroyed.....) via Market Phenomena - the “Boom” and “Bust” Business Cycles of Economic Growth and Recession which act to influence the Demand / Supply Models and Price Curves of Commodities, Bonds, Stocks and Shares in Global Markets. Market Phenomena are simply the sum of all Human Actions – the aggregated activity of Traders and Brokers, Buyers and Sellers participating in that particular marketplace.

Value Creation in Business

• As an introduction to this special topic of the Value Chain - we have defined value

creation in terms of: “Utility Value” which is contrasted with “Exchange Value” -

1. “Utility Value” – skills, learning, know-how, intellectual property and acquired knowledge

2. “Exchange Value” – land, property, capital, goods, traded instruments, commodities and

accumulated wealth.

• Some of the key issues related to the study of Value are discussed - including the

topics of value creation, capture and consumption. All Utility and Exchange Value is

derived from fundamental Human Actions. Although this definition of value creation is

common across multiple levels of activity and analysis, the process of value creation

will differ based on its origination or source - whether that economic value is created

by an individual, a community, an enterprise - or due to Market Phenomena.

• We explore the concepts of Human Actions, competition for scarce resources and

market isolating mechanisms which drive Business Cycles and Market Phenomena in

the Global Economy - using Value Chain analysis in order to explain how value may

be created, exchanged and captured – or consumed, dissipated and lost – as a result

of different activities using different processes at various levels within the Value Chain


• In order to develop a theory of value creation by enterprises, it is useful to first characterise the value creation process. In the next two sections of this document we develop a framework that builds upon Schumpeter's arguments to show: -

1. In any economy, the Creation of Value is solely as a consequence of Human Actions

2. As a result of Human Actions, Value may be created, captured, stockpiled or consumed

3. Also, in any economy, every Individual and Organisation competes with each other for the sole use of scare resources – land, property, capital, labour, machinery, traded instruments and commodities – which may be either raw materials or finished goods

4. New and innovative combinations of resources gives the potential to create new value

5. Mercantilism – shipping, transport, sales, trading, battering and exchange of these new combinations of resources - accounts for the actual realization of this potential value

• In other words - resource combination and exchange lie at the heart of the value creation process and in sections II and III we both describe how this process functions - and also identify the conditions that facilitate and encourage, or slow down and impede, each of these five elements of the Value Creation process.


• This framework establishes the theoretical infrastructure for the analysis of the roles firms play in this value creation process and of how both firms and markets collectively influence the process of economic development – which is derived from Human Actions: -

1. Value Creation – primary Wealth Creation Processes

2. Value Capture – the Acquisition of Wealth by means other than Value Creation

3. Value Stockpiling – the Accumulation of Wealth

4. Value-added Services – Mercantilism, shipping, transport, sales, trading, battering , exchange

5. Value Consumption – the depletion of Resources or the exhaustion of Wealth

• As our analysis of the requirements for effective resource combination and exchange reveals, global market phenomena alone are able to create only a very small fraction of the total value that can be created out of the stock of resources available in economies. The very different institutional nature and context of enterprises, operating in a state of creative tension within global markets, substantially enhance the fraction of the total potential value that can be obtained out of nature’s resources. We describe this process of value creation by firms and, in section V, we integrate the firm's role with that of markets to explain why both firms and markets are needed to ensure that economies develop and progress in a way that achieves what Douglass North (1990) has described as "adaptive efficiency."'

Value Creation vs. Value Consumption

• There are five major roles for people in society: those who create wealth – Primary Value

Creators (Agriculture and Manufacturing) , those who Capture Value from others (through

Taxation, War, Plunder or Theft) those who stockpile Wealth (Savers) and those who

merely consume the wealth generated by others – Value Consumers.. Somewhere in the

middle are the Added Value Providers – those who create secondary value by executing

value-added processes to commodities and goods created by primary Value Creators.

1. Value Creators – primary Wealth Creators working in Agriculture and Manufacturing

2. Value Acquirers – those who capture Wealth generated by others e.g. via Inheritance, Taxation by City, State and Federal Government , or through war, plunder and theft

3. Value Accumulators – those who aggregate, stockpile and hoard Wealth e.g. Savers

4. Value-adders – Secondary Wealth Creators who add value to basic commodities through the human actions of mercantilism, shipping, transport, sales, trading, and retailing

5. Value Consumers – Everyone consumes resources and depletes wealth to some degree by spending their earnings on Food, Housing, Utilities, Clothes, Entertainment and so on.

• About half of society – Children, Students, Invalid and Sick, Unemployed and Government

Workers – consume much of the wealth generated by Primary and Secondary Wealth

Creators – offsetting only little of their depletion of Resources or consumption of Wealth.

Friedrich Hayek

• You may be forgiven for thinking that the current financial crisis was caused by

allowing markets - especially global financial markets – far too much freedom.

Followers of the Austrian economist Friedrich Hayek would say exactly the opposite.

In their view, the crisis happened because the markets weren't free enough.

• Friedrich Hayek was one of the greatest free-market thinkers, who in the 1930s

famously debated with Keynes over the role of government intervention in the

economy. Although Hayek did not share Keynes' charismatic powers of argument

and persuasion (his thick Austrian accent didn't help) – what Hayek did have in

abundance was the intellectual firepower to take on Keynes on the debating floor.

• Keynes constantly and persistently articulated to politicians that intervention by

economic policymakers could, and would, improver adverse economic conditions -

whereas Hayek maintained that Government intervention, in the long term, could,

and would - only make things worse. Hayek was frequently exasperated by the

inconsistencies in Keynes' body of academic work and his tendency to change his

mind - something that the Cambridge economist did quite regularly, and not only "when the facts changed". In the end, this factor made all the difference.

Friedrich Hayek

• Friedrich Hayek wrote The Road to Serfdom, shortly after World War II - a best-

selling polemic railing against centralised economic planning. In it, he warned that

the dead hand of the bureaucrat could threaten the future of a free society almost as

much as the most feared “man of steel “ - Stalin. After that, Hayek suffered many

years in the intellectual wilderness, while Keynesian Economics bestrode the post-

war world. There was at last, a great burst of fame and influence in the 1970s, when

Hayek was awarded a Nobel Prize for economics and feted by free-market politicians

on both sides of the Atlantic. Lord Patten reports how Margaret Thatcher would pull

from out of her handbag – her favourite Hayek quotations at key moments during

cabinet meetings. So far, so good - but what can Hayek say to us right now?

Market Complexity

• There are modern monetarists who have interesting things to say about the current

crisis. Milton Friedman has been profiled and lauded many times over the years.

Hayek, like Keynes - and unlike Milton Friedman – had focused on the great

complexity of markets and their inherent unpredictability. And why choose Hayek for

nomination as a Master of Money, and not the other great free-market economist,

Milton Friedman - who almost certainly wielded more influence than Hayek ?

Friedrich Hayek

• Many Politicians shared some sympathy with this view in the 1930s, when Hayek's arguments on market complexity and inherent unpredictability often enjoyed a better reception than those of Keynes'. This lesson, however, has often been lost on post-war Politicians – even those who claimed to hold free-market economic values. Politicians might pay lip service to liberalising the economy and setting markets free, but in practice it has been difficult for them to truly relinquish the urge to meddle in economic affairs - even when they are privately convinced of the intellectual case and economic benefits of doing so.

• Hayek, unlike Keynes or Friedman, did not believe that Central Bankers and Economic Policymakers could master Market Complexity sufficiently to steer the economy in the right – or even any - direction. Hayek said that more often than not, political intervention in the economy, in the long term, would only make matters worse – for example, the decision of the US Government to rescue people who had invested in Mexican bonds in the "Tequila Crisis" of 1994.

• The free-market economist and Federal Reserve Chairman, Alan Greenspan, supported a massive US-IMF rescue package for Mexico, even though he had warned previously that the cost of protecting speculators from the unintended consequences of their own actions would - by encouraging investors and institutions to continue taking excessive risks – build up problems in the future.

Friedrich Hayek

• We only have to consider an earlier example - the "Tequila Crisis" of 1994 – to find a precedent for the Financial Crisis of 2008 and the decision of the US and UK Government's to rescue in the failed Banks and Insurance companies and bail out investors. The financial system might have seemed free, these critics argue, but in reality it was a dangerous hybrid. The banks were free to do just about anything that they wanted – in the certain knowledge that Governments would not allow them to fail and endanger investors in large numbers.

• This encouraged Bankers to take on Sub-prime Mortgage Products, which were some pretty risky bets, and it all ended up costing us all very, very dear. So, lest there be any doubt about it, lets make it it be very, very clear,– the safety net of Government intervention was always there - as witnessed by the massive bailouts of 2008. All of which explains why Hayek and some other Austrian economists have now acquired a new generation of followers - including the Governor of California and next Republican presidential candidate Paul Rubio. They find both a convincing explanation of the financial crisis and a bracing solution to future Financial Markets misadventures in Hayek's theories.

• In seeking examples of interventionist Government - witness today the current difficulties that Conservative-Liberal coalition ministers in the British Parliament have in letting go of day-to-day power over the National Health Service or the BBC – lat alone devolving authority to de-centralised Government.

Friedrich Hayek

Leave well alone? • The Hayek Austrian School explanation for the Financial Crisis of 2008 is that it

is all down to Government interference – tampering with free market risk and reward, the very worst possible kind of government meddling – where economic policymakers fail to grant financial markets the freedom of control of action and consequence. Hayek thought that this policy originated in the government's grim determination to control the price of money and the money supply – by fixing interest rates and controlling the availability of credit (money) in the marketplace.

• In the Austrian view, the US Federal Reserve and other central banks helped cause the financial crisis, by persistently cutting interest rates whenever the economy showed any signs of faltering; for example, after the bursting of the dotcom bubble in 2000. That might have staved off a more serious downturn - but only at the cost of encouraging people to take on debts they couldn't afford (through excess of money supply) - and granting banks an insurance policy to take excessive risks.

• This, in effect, is the same argument that Hayek made against Keynes in the late 1920s and 1930s. He maintained that the Federal Reserve caused the crash, by keeping interest rates too low and encouraging a lot of "malinvestment" - investment in those projects or assets which made no business sense – in that they were neither financially viable nor economically worthwhile.

Friedrich Hayek

• Hayek commented that greater efforts to stimulate the economy would only make

economic conditions worse - especially if those measures required further borrowing by

the government The difference between Milton Friedman and John Maynard Keynes -

much exaggerated in the historical record - was that Friedman advocated an increase in

the Money Supply (Quantitative Easing), whilst Keynes saw a major role for fiscal policy

too - in increased Public Spending - particularly in the aftermath of financial crises.

• When it comes to the 1930s, economic history has not always looked kindly on Hayek's

arguments. The neo-classical viewpoint of the depression by Milton Friedman and Anna

Schwartz, decades later, made a convincing case that it was caused by the US central

bank pumping too little money into the economy, rather than too much. This is a bit like

the NRA saying that the culture of violence in the USA is due to there not being enough

guns in civilian hands – rather than too many.....

• What is most interesting to note is that the Monetary Theories of both Milton Friedman

and John Maynard Keynes are on the same side of the argument - both the neo-classic

and neo-liberal viewpoints are united against the non-interventionist standpoint of

Friedrich Hayek. Given the experience of an economic downturn, Friedman and Keynes

each thought that economic policymakers could come to the rescue. Both men thought

that, in normal times, monetary policy was the best way to deal with a recession.

Friedrich Hayek

• What makes Hayek a radically different kind of free-market economist is the

distrusted that he harbours for both sets of economic policy machinery - monetary

and fiscal - for guiding the economy. Hayek's view has great resonance for anyone

who feels uneasy about governments bailing out bankers and central banks pumping

hundreds of billions of dollars into the economy.

• In the 1920s, Hayek had lived through hyperinflation as a young adult in Austria, and

as a result he simply would not believe that governments should or could iron out the

peaks and troughs in the economic cycle. The only government power he had any

confidence in was the power of economic intervention to make things worse - by

devaluing the currency through Quantitative Easing.

• We can easily understand why so many are turning to Austrian School economists

like Hayek for a radical and different kind of solution to today's economic problems .

Whether any government or mainstream politician is really prepared to step back,

and let the system "heal itself" - whatever the short-term consequences - is quite

another matter. None were able to in 2008.

Monetary and Fiscal Policy

Difference Between Monetary and Fiscal Policy Tejvan Pettinger on September 16, 2011 in economics

• Monetary Policy and Fiscal Policy are both used as tools to pursue dual

economic policies of controlling economic growth and managing inflation. Monetary Policy features varying the interest rate and influencing the availability of credit – the Money Supply – whereas Fiscal Policy involves the government changing tax rates and manipulating levels of government public spending in order to influence aggregate demand in the economy.

Monetary Policy • Monetary policy is usually conducted by Economic Planners in Central

Banks and their Political Masters in Treasury Authorities, and involves: -

– Setting interest base rates (e.g. LIBOR and Bank of England in the UK and the Federal Reserve in USA)

– Influencing the Money Supply (availability and flow of Credit) e.g. Policy of quantitative easing to increase the supply of money .

http://www.economicshelp.org/blog/author/tejvan/



http://www.economicshelp.org/blog/category/economics/

http://www.economicshelp.org/blog/1047/economics/quantitative-easing/

http://www.economicshelp.org/blog/1047/economics/quantitative-easing/


How Monetary Policy Works

• The Central Bank may have an inflation target of, say, 2%. If they feel that inflation is going to go above the inflation target, due to economic growth being too quick, then they will increase interest rates. Higher interest rates increase borrowing costs and reduce consumer spending and investment - leading to lower aggregate demand and lower inflation. If the economy lurches into recession, the Central Bank would cut interest rates (see Cutting interest rates).

Fiscal Policy

• Fiscal Policy is carried out by the government and involves changing: -

– Level of government spending

– Levels of taxation

• To increase demand and economic growth - the government will cut tax and increase spending (leading to an increase in budget deficit)

• To reduce demand and reduce inflation - the government can increase tax rates and cut spending (leading to a decrease in budget deficit)

http://www.economicshelp.org/blog/3417/interest-rates/effect-of-lower-interest-rates/


Example of Expansionary Fiscal Policy

• In a recession, the government may decide to increase borrowing and spend

more on infrastructure spending. The idea of this is to increase in government

spending – which creates an injection of money into the economy and helps

to create jobs. There may also be a multiplier effect, where the initial injection

into the economy causes a further round of higher spending. This increase in

total aggregate demand can kick-start the economy to get out of recession.

• Increased borrowing to fund public expenditure on infrastructure projects is

an inflationary fiscal policy (as is lowering the general level of taxation so as

to increase consumer spending) - which may cause the economy to become

over-heated – and in turn lead to an increase in the average rate of inflation.

• Should f the government felt that inflation was a problem, then they could

pursue a deflationary fiscal policy (higher tax and lower spending) in order to

reduce the rate of economic growth. See more at: Expansionary fiscal policy

http://www.economicshelp.org/blog/1948/economics/the-multiplier-effect/

http://www.economicshelp.org/blog/617/economics/impact-of-expansionary-fiscal-policy/


Which is More Effective Monetary or Fiscal Policy?

In recent decades, monetary policy has become more popular because: -

• Monetary policy is set by Central Banks, and therefore reduces political influence (e.g.

politicians may cut interest rates in order to boost the economy before a general election)

• Fiscal Policy can have more supply side effects on the wider economy. E.g. to reduce

inflation – higher tax and lower spending would not be popular and the government may

be reluctant to purse this. Also lower spending could lead to reduced public services and

the higher income tax could create disincentives to work.

• Monetarists argue expansionary fiscal policy (larger budget deficit) is likely to cause

crowding out – higher government spending reduces private sector spending, and higher

government borrowing pushes up interest rates. (However, this analysis is disputed)

• Expansionary fiscal policy (e.g. more government spending) may lead to special interest

groups pushing for spending which isn’t really helpful and then proves difficult to reduce

when recession is over.

• Monetary policy is quicker to implement. Interest rates can be set every month. A decision

to increase government spending may take time to decide where to spend the money.

http://www.economicshelp.org/blog/1013/economics/crowding-out/


• The current recession demonstrates that Monetary Policy has too many limitations.

– Targeting inflation alone is far too narrow. This meant Central Banks ignored an

unsustainable boom in the housing market and bank lending.

– Liquidity Trap. In a recession, cutting interest rates may prove insufficient to

boost demand because banks don’t want to lend and consumers are too nervous

to spend. Interest rates were cut from 5% to 0.5% in March 2009 - but this didn’t

solve recession in UK – as Banks could place Deposits in Asia earning over 5%

– Even Quantitative Easing – creating money may be ineffective if the banks just

want to keep the extra money in their balance sheets – especially if at the dame

time, Regulators are forcing Banks to increase Liquidity (Capital Adequacy).

– Government spending directly creates demand in the economy and can provide

a kick-start to get the economy out of recession. In a deep recession, reliance on

monetary policy alone, will be insufficient to restore equilibrium in the economy.

– In a Liquidity Trap, any expansionary fiscal policy will not cause crowding out

because the government is making use of surplus savings to inject demand into

the economy.

– In a deep recession, expansionary fiscal policy may be important for confidence

– but only if monetary policy has proved to be ineffective – or a complete failure.


• Whether it's the European Central Bank lending trillions to European national

banks, or a third bout of quantitative easing by the Federal Reserve Bank –

currently running at $85 billion per month – it just feels that too many financial

institutions are simply deferring the moment of truth - rather than dealing directly

with core structural economic problems. This includes the International Monetary

Fund, the European Central Bank and the Federal Reserve Bank.

Stochastic Processes –

Random Events

The Nature of Uncertainty – Randomness

Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects – any apparent randomness is as a result of Unknown Forces

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics

Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object – apparent randomness and asymmetry is as a result of Quantum and Unknown Forces

The Nature of Randomness

Classical Mechanics (Newtonian Physics)

– governs the behaviour of everyday objects

– any apparent randomness is as a result of Unknown Forces, either internal or external,

acting upon a System.

Quantum Mechanics

– governs the behaviour of unimaginably small objects (such as sub-atomic particles)

– all events are truly and intrinsically both symmetrical and random (Hawking Paradox).

Relativity Theory

– governs the behaviour of impossibly super-massive cosmic structures

– any apparent randomness or asymmetry is as a result of Unknown Forces acting early

in the history of Time-space

Wave Mechanics (String Theory)

– integrates the behaviour of every size and type of object

– any apparent randomness or asymmetry is as a result of Unknown Dimensions acting

in the Membrane or in Hyperspace

Randomness

Stochastic Processes – Random Events

• A tradition that begins with the classical Greek natural philosophers (circa 600 -

200 BC) and continues through contemporary science - holds that change and

the order of nature are the result of natural forces. What is the role of random,

stochastic processes in a universe that exhibits such order? When we examine

the heavens there seems to be a great deal of order to the appearance and

movement of the celestial bodies - galaxies, stars, planets, asteroids, etc.

• Since the dawn of our species, humans have speculated on how these bodies

were formed and on the meaning of their movements. Most observations of

natural phenomena support the contention that nature is ordered. The force

that brought about this order differs depending upon the source of the historic

explanation of how this order came to be. For most of human history, super-

natural forces were credited with the imposition of order on nature.

Randomness

Stochastic Processes

• Stochastic is a term which means that certain natural phenomena, such as: -

– the history of an object

– the outcome of an event

– the execution of a process

• - involves random processes - or chance. In stochastic processes randomness is the sole

governing factor controlling the outcome – over time, there is no identifiable pattern or

trend in outcomes, there is no detectable distribution, grouping or clusters in the results.

• The elliptic orbits of the planets are examples of non- stochastic processes – because they

have predictable outcomes through following an identifiable pattern or design. The pattern

of the elliptic orbit is a result of various gravitational forces operating on the motion of the

bodies – rather than random or chance events. If we assume that we can follow the path

of a moving object – and that every time the object moves along the path it repeats the

same pattern – we could conclude that this pattern of motion results from some non-

stochastic process. Once we have analysed and modelled the pattern of movement - we

can predict the next location of the moving object with some degree of accuracy.

Randomness

• If the movement of an object resulted from the operation of stochastic

processes, a repeating pattern of motion would not occur - and we would not

be able to predict with any accuracy the next location of the object as it move

down its path. Examples of stochastic processes include: - the translational

motion of atomic or molecular substances, such as the hydrogen ions in the

core of the sun; the outcomes from flipping a coin; etc. Stochastic processes

govern the outcome of games of chance – unless those games are “fixed”.

• Disruptive Future paradigms in Future Studies, when considered along with

Wave (String) Theory in Physics – alert us to the possibility of chaotic and

radically disruptive Random Events that generate ripples which propagate

outwards from the causal event like a wave – to flow across Space-Time.

Different waves might travel through the Time-Space continuum at slightly

different speeds due to the “viscosity” (granularity) in the substance of the

Space-Time Continuum (dark energy and dark matter).

Randomness

• Some types of Wave may thus be able to travel faster than others – either

because those types of Wave can propagate through Time-Space more rapidly

than other Wave types – or because certain types of Wave form can take

advantage of a “short cut” across a “warp” in the Time-Space continuum.

• A “warp” brings two discrete points from different Hyperspace Planes close

enough together to allow a Hyperspace Jump. Over any given time interval -

multiple Hyperspace Planes stack up on top of each other to create a time-line

which extends along the temporal axis of the Minkowski Space-Time Continuum.

• As we have discussed previously - Space (position) and Time (history) flow

inextricably together in a single direction – towards the future. In order to

demonstrate the principle properties of the Minkowski Space-Time continuum,

any type of Spatial and Temporal coupling in a Model or System must be able to

show over time that the History of a particle or the Transformation of a

process are fully and totally dependent on both its Spatial (positional) and

Temporal (historic) components acting together in unison.

Randomness

• Neither data-driven nor model-driven representations of the future are capable

alone, and by themselves, of dealing with the effects of chaos (uncertainty). We

therefore need to consider and factor in further novel and disruptive system

modelling approaches in order to help us to understand how Natural Systems

(Cosmology, Climate) and Human Activity Systems (Economics, Sociology)

perform. Random, Chaotic and Disruptive Wild Card or Black Swan events

may thus be factored into our System Models in order to account for uncertainty.

• Horizon Scanning, Tracking and Monitoring techniques offer us the possibility to

manage uncertainty by searching for, detecting and identifying Weak Signals –

which are messages from Random Events coming towards us from the future.

Faint seismic disturbances warn us of coming of Earth-quakes and Tsunamis.

Weak Signals (seismic disturbances) may often be followed by Strong Signals

(changes in topology), Wild Card (volcanic eruptions) or Black Swan (pyroclastic

cloud and ocean wave events), Horizon Scanning may help us to use Systems

Modelling to predict Natural Events like Earth-quakes and Tsunamis – as well as

Biological processes such as the future of Ecosystems, and Human Processes

such as the cyclic rise and fall of Commodity, Stocks and Shares market prices.

The Nature of Randomness – Uncertainty

• Randomness makes any precise prediction of future outcomes impossible.

We are unable to predict any future outcome with any significant degree of

confidence or accuracy – due to the inherent presence of uncertainty

associated with Complex Systems. Randomness in Complex Systems

introduces chaos and disorder – causing disruption. Events no longer continue

to unfold along a smooth, predictable linear course leading towards an

inevitable outcome – instead, we experience surprises.

• What we can do, however, is to identify the degree of uncertainty present in

those Systems, based on known, objective measures of System Order and

Complexity - the number and nature of elements present in the system, and the

number and nature of relationships which exist between those System

elements. This in turn enables us to describe the risk associated with possible,

probable and alternative Scenarios, and thus equips us to be able to forecast

risk and the probability of each of those future Scenarios materialising.

Complex Systems and Chaos Theory

Complex Systems and Chaos Theory has been used extensively in the field of Futures Studies, Strategic

Management, Natural Sciences and Behavioural Science. It is applied in these domains to understand

how individuals within populations, societies, economies and states act as a collection of loosely

coupled interacting systems which adapt to changing environmental factors and random events – bio-ecological, socio-economic or geo-political.....

Linear and Non-linear Systems

Linear Systems – all system outputs are directly and proportionally related to system inputs

• Types of linear algebraic function behaviours; examples of Simple Systems include: -

– Game Theory and Lanchester Theory

– Civilisations and SIM City Games

– Drake Equation (SETI) for Galactic Civilisations

Non-linear Systems – system outputs are asymmetric and not proportional or related to inputs

• Types of non-linear algebraic function behaviours: examples of Complex / Chaotic Systems are: -

– Complex Systems – large numbers of elements with both symmetric and asymmetric relationships

– Complex Adaptive Systems (CAS) – co-dependency and co-evolution with external systems

– Multi-stability – alternates between multiple exclusive states.(lift status = going up, down, static)

– Chaotic Systems

• Classical chaos – the behaviour of a chaotic system cannot be predicted.

• A-periodic oscillations – functions that do not repeat values after a certain period (# of cycles)

– Solitons – self-reinforcing solitary waves - due to feedback by forces within the same system

– Amplitude death – any oscillations present in the system cease after a certain period (# of cycles)

due to feedback by forces in the same system - or some kind of interaction with external systems.

– Navis-Stokes Equation for the motion of a fluid: -

• Weather Forecasting

• Plate Tectonics and Continental Drift

http://en.wikipedia.org/wiki/Classical_chaos

http://en.wikipedia.org/wiki/Aperiodic



http://en.wikipedia.org/wiki/Soliton

http://en.wikipedia.org/wiki/Amplitude_death

Complexity Paradigms

• System Complexity is typically characterised and measured by the number of elements in a

system, the number of interactions between elements and the nature (type) of interactions.

• One of the problems in addressing complexity issues has always been distinguishing between

the large number of elements (components) and relationships (interactions) evident in chaotic

(unconstrained) systems - Chaos Theory - and the still large, but significantly smaller number

of both and elements and interactions found in ordered (constrained) Complex Systems.

• Orderly System Frameworks tend to dramatically reduce the total number of elements and

interactions – with fewer and smaller classes of more uniform elements – and with reduced,

sparser regimes of more restricted relationships featuring more highly-ordered, better internally

correlated and constrained interactions – as compared with Disorderly System Frameworks.

Complexity Simplicity

Simplexity Ordered

Complexity Disordered Complexity

Complex Adaptive Systems (CAS)

Linear Systems

(element and interaction density)

Chaos Order

System Complexity

• System Complexity is typically characterised by the number of elements in a system,

the number of interactions between those elements and the nature (type) of interactions.

One of the problems in addressing complexity issues has always been distinguishing

between the large number of elements and relationships, or interactions evident in

chaotic (disruptive, unconstrained) systems - and the still large, but significantly smaller

number of elements and interactions found in ordered (constrained) systems.

• Orderly (constrained) System Frameworks tend to have both a restricted number of

uniform elements with simple (linear, proportional, symmetric) interactions with just a few

element and interaction classes of small size, featuring explicit interaction rules which

govern more highly-ordered, internally correlated and constrained interactions – and

therefore tend to exhibit predictable system behaviour with smooth, linear outcomes.

• Disorderly (unconstrained) System Frameworks – tend to have both a very large total

number of non-uniform elements featuring complex (non-linear, asymmetric) interactions

which may be organised into many classes and regimes. Disorderly (unconstrained)

System Frameworks – feature a greater number of more disordered, uncorrelated and

unconstrained element interactions with implicit or random rules – which tend to exhibit

unpredictable, random, chaotic and disruptive system behaviour – and creates surprises.

Complexity Map


• A system may be defined as simple or linear whenever its evolution sensitively is fully

independent of its initial conditions – and may also be described as deterministic

whenever the behaviour of a simple (linear) systems can be accurately predicted and

when all of the observable system outputs are directly and proportionally related to

system inputs. We can expect smooth, linear, highly predictable outcomes to simple

systems which are driven by linear algebraic functions.

• A system may be described as chaotic whenever the system evolution sensitively is

fully dependant upon its initial conditions – and may also be defined as probabilistic –

whenever the behaviour of that stochastic system cannot be predicted. This property

of dependency on initial conditions in chaotic systems implies that from any two invisibly

different starting points or variations in starting conditions – then their trajectories begin

to diverge – and the degree of separation between the two trajectories increases

exponentially over the course of time. In this way, over numerous System Cycles –

invisibly small differences in initial conditions are amplified until they become radically

divergent, eventually producing totally unexpected results with unpredictable outcomes.

Instead of smooth, linear outcomes – we experience surprises. This is why complex,

chaotic systems such as weather and the economy – are impossible to accurately

predict. What we can do, however, is to describe possible, probable and alternative

future scenarios – and calculate the probability of each of those scenarios materialising.


• Chaos Theory has been used extensively in the fields of Futures Studies, Natural

Sciences, Behavioural Science, Strategic Management, Threat Analysis and Risk

Management. The requirements for a stochastic system to become chaotic, are that the

system must be non-linear and multi-dimensional – that is, the system posses at least

three dimensions. The Space-Time Continuum is already multi-dimensional – so any

complex (non-linear) and time-variant system which exists over time in three-dimensional

space - meets all of these criteria.

• The Control of Chaos refers to a process where a tiny external system influence is

applied to a chaotic system, so as to slightly vary system conditions – in order to achieve

a desirable and predictable (periodic or stationary) outcome. To synchronise and resolve

chaotic system behaviour we may invoke external procedures for stabilizing chaos which

interact with symbolic sequences of an embedded chaotic attractor - thus influencing

chaotic trajectories. The major concepts involved in the Control of Chaos, are described

by two methods – the Ott-Grebogi-Yorke (OGY) Method and the Adaptive Method.

• The Adaptive Method for the resolution of Complex, Chaotic Systems introduces multiple

relatively simple and loosely coupled interacting systems in an attempt to model over time

the behaviour of a single, large Complex and Chaotic System - which may still be subject

to undetermined external influences – thus creating random system effects.....

Complex Adaptive Systems Adaption and Evolution

When Systems demonstrate properties of Complex

Adaptive Systems (CAS) - often defined as a

collection or set of relatively simple and loosely

connected interacting systems exhibiting co-adapting

and co-evolving behaviour - then those systems are

much more likely to adapt successfully to their

environment and, thus better survive the impact of both

gradual change and of sudden random events.

Complex Adaptive Systems

• Complex Adaptive Systems (CAS) and Chaos Theory has also been

used extensively in the field of Futures Studies, Strategic Management,

Natural Sciences and Behavioural Science. It is applied in these domains

to understand how individuals within populations, societies, economies and

states act as a collection of loosely coupled interacting systems which

adapt to changing environmental factors and random events – biological,

ecological, socio-economic or geo-political.

• Complex Adaptive Systems (CAS) and Chaos Theory treats individuals,

crowds and populations as a collective of pervasive social structures which

may be influenced by random individual behaviours – such as flocks of

birds moving together in flight to avoid collision, shoals of fish forming a

“bait ball” in response to predation, or groups of individuals coordinating

their behaviour in order to respond to external stimuli – the threat of

predation or aggression – or in order to exploit novel and unexpected

opportunities which have been discovered or presented to them.


• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is

often defined as a collection or set of relatively simple and loosely connected interacting

systems exhibiting co-adapting and co-evolving behaviour (sub-systems or components

changing together in response to the same external stimuli) - then those systems are

much more likely to adapt successfully to their environment and, thus better survive the

impact of both gradual change and of sudden random events. Complexity Theory

thinking has been present in biological, strategic and organisational system studies since

the first inception of Complex Adaptive Systems (CAS) as an academic discipline.

• Complex Adaptive Systems are further contrasted compared with other ordered and

chaotic systems by the relationship that exists between the system and the agents and

catalysts of change which act upon it. In an ordered system the level of constraint means

that all agent behaviour is limited to the rules of the system. In a chaotic system these

agents are unconstrained and are capable of random events, uncertainty and disruption.

In a CAS, both the system and the agents co-evolve together; the system acting to

lightly constrain the agents behaviour - the agents of change, however, modify the

system by their interaction. CAS approaches to behavioural science seek to understand

both the nature of system constraints and change agent interactions and generally takes

an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.




in nature to biological behaviour in an population than to truly Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable long-term



shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards) – by

the ability of Financial markets to rapidly absorb and recover from these events.

• Unexpected and surprising Cycle Pattern changes have historically occurred during

regional and global conflicts being fuelled by technology innovation-driven arms

races - and also during US Republican administrations (Reagan and Bush - why?).

Just as advances in electron microscopy have revolutionised the science of biology

- non-stationary time series wave-form analysis has opened up a new space for


Crowd Behaviour – the Swarm

• An example of Random Clustering is a Crowd or Swarm. There are a various forces

which contribute towards Crowd Behaviour – or Swarming. In any crowd of human

beings or a swarm of animals, individuals in the crowd or swarm are closely connected

so that they share the same mood and emotions (fear, greed, rage) and demonstrate

the same or very similar behaviour (fight, flee or feeding frenzy). Only the initial few

individuals exposed to the Random Event or incident may at first respond strongly and

directly to the initial “trigger” stimulus, causal event or incident (opportunity or threat –

such as external predation, aggression or discovery of a novel or unexpected

opportunity to satisfy a basic need – such as feeding, reproduction or territorialism).

• Those individuals who have been directly exposed to the initial “trigger” event or incident -

the system input or causal event that initiated a specific outbreak of behaviour in a crowd or

swarm – quickly communicate and propagate their swarm response mechanism and share

with all the other individuals – those members of the Crowd immediately next to them – so

that modified Crowd behaviour quickly spreads from the periphery or edge of the Crowd.

• Peripheral Crowd members in turn adopt Crowd response behaviour without having been

directly exposed to the “trigger”. Members of the crowd or swarm may be oblivious to the

initial source or nature of the trigger stimulus - nonetheless, the common Crowd behaviour

response quickly spreads to all of the individuals in or around that core crowd or swarm.

Crowd Behaviour – the Swarm

• One of the dangers posed by human crowd behaviour is that of “de-individualisation” in a

crowd, where a group of random individuals aggregate together and begin acting in concert

- adopting common behaviour, aims and objectives – and may begin to exhibit uninhibited

crowd responses to external information and stimuli. Crowd participants in this state begin

to respond without the usual constraints of their normal social, ethical, moral, religious and

behavioural rules. These are the set of circumstances which led to events such as the Arab

Spring and London Riots - which spread rapidly through deprived communities across the

country, both urban and rural. This type of collective group behaviour – such as a “feeding

frenzy” – has been observed in primates and carnivores - and even in rodents and fish.....

• Crowd behaviour is not just the domain of Demonstrators and Protesters - it can also be

seen in failing economies with the actions of Economic Planners in Central Banks - along

with their Political Masters – who also behave as a group of individuals acting together in

concert without the usual constraints – and thus, under extreme psychological stress as

systems such as the economy begins to collapse unpredictably – start to demonstrate "de-

individualisation" - collective uninhibited responses to external information and stimuli,

without the constraints of their normal political, economic, social, ethical, moral and

behavioural rules. These circumstances may lead to further panic and crowd behaviour

across Towns and Cities, Banks and Financial Institutions, ultimately Municipal, State and

Federal government departments - causing the failure of Global Markets or the fall of

Governments – as was recently witnessed in both the Arab Spring and the Euro Crisis.

Wave-form Analytics in Cycles

• Wave-form Analytics is a powerful new analytical tool “borrowed” from spectral

wave frequency analysis in Physics – which is based on Time-frequency analysis –

a technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of natural and human activity waves,

and in the field of economic cycles, business cycles, market patterns and trends.

• Trend-cycle decomposition is a critical technique for testing the validity of multiple

(compound) dynamic wave-form models competing in a complex array of

interacting and inter-dependant cyclic systems in the study of complex cyclic

phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.

In order to study complex periodic economic phenomena there are a number of

competing analytic paradigms – which are driven by either deterministic methods

(goal-seeking - testing the validity of a range of explicit / pre-determined / pre-

selected cycle periodicity value) and stochastic (random / probabilistic / implicit -

testing every possible wave periodicity value - or by identifying actual wave

periodicity values from the “noise” – harmonic resonance and interference patterns).


• A fundamental challenge found everywhere in business cycle theory is how to

interpret very large scale / long period compound-wave (polyphonic) time series data

sets which are dynamic (non-stationary) in nature. Wave-form Analytics is a new

analytical too based on Time-frequency analysis – a technique which exploits the

wave frequency and time symmetry principle. The role of time scale and preferred

reference from economic observation are fundamental constraints for Friedman's

rational arbitrageurs - and will be re-examined from the viewpoint of information

ambiguity and dynamic instability.

• The Wigner-Gabor-Qian (WGQ) spectrogram demonstrates a distinct capability for

revealing multiple and complex superimposed cycles or waves within dynamic, noisy

and chaotic time-series data sets. A variety of competing deterministic and

stochastic methods, including the first difference (FD) and Hodrick-Prescott (HP)

filter - may be deployed with the multiple-frequency mixed case of overlaid cycles

and system noise. The FD filter does not produce a clear picture of business cycles

– however, the HP filter provides us with strong results for pattern recognition of

multiple co-impacting business cycles. The existence of stable characteristic

frequencies in large economic data aggregations (“Big Data”) provides us with strong

evidence and valuable information about the structure of Business Cycles.


Wave-form Analytics in Natural Cycles

• Solar, Oceanic and Atmospheric Climate Forcing systems demonstrate Complex Adaptive

System (CAS) behaviour – behaviour which is more similar to an organism than that of

random and chaotic “Stochastic” systems. The remarkable long-term stability and

sustainability of cyclic climatic systems contrasted with random and chaotic short-term

weather systems are demonstrated by the metronomic regularity of climate pattern

changes driven by Milankovich Solar Cycles along with 1470-year Dansgaard-Oeschger

and Bond Cycles – regular and predictable and Oceanic Forcing Climate Sub-systems.

Wave-form Analytics in Human Activity Cycles

• Economic systems also demonstrate Complex Adaptive System (CAS) behaviour - more

similar to an ecology than chaotic “Random” systems. The capacity of market economies

for cyclic “boom and bust” – financial crashes and recovery - can be seen from the impact

of Black Swan Events causing stock market crashes - such as the failure of sovereign

states (Portugal, Ireland, Greece, Iceland, Italy and Spain) and market participants

(Lehman Brothers) due to oil price shocks, money supply shocks and credit crises.

Surprising pattern changes occurred during wars, arm races, and during the Reagan

administration. Like microscopy for biology, non-stationary time series analysis opens up

a new space for business cycle studies and policy diagnostics.

The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Natural Cycle Types

• Cosmic Processes – ultra long-term Astronomic changes (e.g. galactic and solar system events)

• Geological Processes – very long-term global change e.g. Mountain Building, Volcanic Activity

• Biological Processes – Evolution (terra-forming effects) Carbon, Nitrogen, Oxygen and Sulphur Cycles

• Solar Forcing – long-term periodic change in Insolation (solar radiation) due to Milankovitch Cycles

• Oceanic Forcing – Oceanic Cycles - currents and climate systems – temperature, salinity, oscillation,

• Atmospheric Forcing – rapid change in air temperature - weather systems and Ice / melt-water Cycles

• Human Processes – Human Activity (agriculture, industrialisation) and impact on Climate Change

• Atomic / Sub-atomic Processes –

– Classical Mechanics (Newtonian Physics) – governs the behaviour of all everyday objects

– Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles

– Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures

– Wave Mechanics (String Theory) – integrates the behaviour of every size & type of object

Wave Theory – Natural Cycles

Milankovitch Solar Orbit Climate Cycles • Milankovitch Cycles are a Composite Harmonic Wave Series built up from individual wave-forms with

periodicity of 20-100 thousand years - exhibiting multiple wave harmonics, resonance and interference

patterns. Over very long periods of astronomic time Milankovitch Cycles and Sub-cycles have been

beating out precise periodic waves, acting in concert together, like a vast celestial metronome.

• From the numerous geological examples found in Nature including ice-cores, marine sediments and

calcite deposits, we know that Composite Wave Models such as Milankovitch Cycles behave as a

Composite Wave Series with automatic, self-regulating control mechanisms - and demonstrate

Harmonic. Resonance and Interference Patters with extraordinary stability in periodicity through

many system cycles over durations measured in tens of millions of years.

• Climatic Change and the fundamental astronomical and climatic cyclic variation frequencies are

coherent, strongly aligned and phase-locked with the predictable orbital variation of 20-100 k.y

Milankovitch Climatic Cycles – which have been modeled and measured for many iterations, over a

prolonged period of time, and across many levels of temporal tiers - each tier hosting different types of

geological processes, which in turn influence different layers of Human Activity.

• Milankovitch Cycles - are precise astronomical cycles with periodicities of 22, 41, 100 and 400 k.y

– Precession (Polar Wandering) - 22,000 year cycle

– Eccentricity (Orbital Ellipse) 100,000 and 400,000 year cycles

– Obliquity (Axial Tilt) - 41,000-year cycle

Natural Cycle Types

Milankovitch Climatic Cycles - astronomical cycles with periodicities of 22, 41, 70, 100, 400 k.y

• Precession (Polar Wandering) - 22,000 year cycle

• Inclination as the Earth's orbit drifts up and down with a cycle period of about 70,000 years. Note: Passing through the Orbital Plane, more dust and objects fall to earth – Milankovitch did not study this three-dimensional aspect of Earth’s orbit as it has no direct insolation effect

• Eccentricity (Orbital Ellipse) 100,000 and 400,000 year cycles

• Obliquity (Axial Tilt) - 41,000-year cycle

• The Solar System and planet Earth orbit our parent Galaxy, the Milky Way, every 250m years

• Note: by passing through the Galactic Plane, dust and larger objects may enter the Solar system and fall to earth – Milankovitch did not study this three-dimensional aspect of the Solar Systems’ Galactic orbit as it has no direct or obvious insolation effect on earth.

Quaternary Sub-Milankovitch Climatic Cycles – Harmonic Resonance / Interference Wave Series

• Semi-precession cycles with a periodicity of around half a precession cycle (10-50 k.y)

• Pleistocene Sub-Milankovitch Climatic Cycles Dansgaard-Oeschger Cycles.

– Major D/O Events at 1470 years (and at circa1800 / 900 /450 /125 years ?)

– Minor Sub-Milankovitch Climatic Cycles at 152, 114, 83 and 11 years

• Holocene Sub-Milankovitch Climatic Cycles Bond Cycles

– Major Bond Events at 1470 years (and at circa 2600 / 1650 / 1000 / 500 / 200 years ?)

– Minor Sub-Milankovitch Climatic Cycles at 117, 64, 57 and 11 years

Natural Cycle Types

STELLIUM – Major Multi-planetary Conjunctions – 40 years

• Planetary orbital periods (in earth Years)

– Mercury – 0.24 Years

– Venus - 0.52

– Earth - 1

– Mars - 1.88

– Jupiter – 11.86

– Saturn – 29.46

– Uranus – 84.01

– Neptune – 184.80

Natural Cycles in Astronomy – the Solar System

• Solar Activity Cycle – Sunspot Cycles – 11 years

• Southern Oscillation - El Nino / La Nina (Warm / Cold Water Periodicity in the Pacific @ 3, 5 & 7 years)

• Natural Seasonal Cycles – Diurnal to Annual (1 day to 1 year)

– Tidal – Diurnal (twice daily), 12 hours (Earth Rotation + Moon Orbit)

– Day-Night Cycle – Daily, 24 hours (Earth Rotation)

– Lunar Month – Monthly, 28 Days (Moon Orbit of the Earth)

– Solar Year - Seasonal Cycle – Annual, 1 Year (Earth Orbit of the Sun)

Wave Theory – Natural Cycles

Sub-Milankovitch Climatic Cycles • Sub-Milankovitch Climatic Cycles are less well understood – varying from Sun Cycles of 11 years

to Climatic Variation Trends of up to 1470 years intervals, may also impact on Human Activity –

short-term Economic Patterns, Cycles and Innovation Trends – to long-term Technology Waves and

the rise and fall of Civilizations. A possible explanation might be found in Resonance Harmonics of

Milankovitch-Cycles 20-100 ky / sub-Cycle Periodicity - resulting in Interference Phenomenon from

periodic waves being re-enforced and cancelled. Dansgaard-Oeschger (D/O) events – with precise

1470 years intervals - occurred repeatedly throughout much of the late Quaternary Period.

Dansgaard-Oeschger (D/O) events were first reported in Greenland ice cores by scientists Willi

Dansgaard and Hans Oeschger. Each of the 25 observed D/O events in the Quaternary Glaciation

Time Series consist of an abrupt warming to near-interglacial conditions that occurred in a matter of

decades - followed by a long period of gradual cooling down again over thousands of years

• Sub-Milankovitch Climatic Cycles - Harmonic. Resonance and Interference Wave Series

– Solar Forcing Climatic Cycle at 300-Year, 36 and 11 years

• Grand Solar Cycle at 300 years with 36 and 11 year Harmonics

• Sunspot Cycle at 11years

– Oceanic Forcing Climatic Cycles at 1470 years (and at 490 / 735 / 980 years ?)

• Dansgaard-Oeschger Cycles – Quaternary

• Bond Cycles - Pleistocene

– Atmospheric Forcing Climatic Cycles at 117, 64, 57 and 11 years

• North Atlantic Climate Anomalies

• Southern Oscillation - El Nino / La Nina

Climate Cycles

• Climate oscillations have various hypothesized

and multiple observed time-scales - twenty-six

iterations of Dansgaard–Oeschger and Bond

Cycles have major periodicities of 1470 years.

• They include the following: -

– Ice ages

– Atlantic Multi-decadal Oscillation

– El Niño Southern Oscillation

– Pacific decadal oscillation

– Inter-decadal Pacific Oscillation

– Arctic oscillation

– North Atlantic Oscillation

– North Pacific Oscillation

– Hale cycle (may be discernible in climate

records; see solar variation)

– 60-year climate cycle recorded in tree rings,

stalagmites and many ancient calendars -

as per Dr. Nicola Scafetta (2010)

http://en.wikipedia.org/wiki/Ice_age

http://en.wikipedia.org/wiki/Atlantic_Multidecadal_Oscillation



http://en.wikipedia.org/wiki/El_Ni%C3%B1o_Southern_Oscillation

http://en.wikipedia.org/wiki/Pacific_decadal_oscillation

http://en.wikipedia.org/wiki/Interdecadal_Pacific_Oscillation



http://en.wikipedia.org/wiki/Arctic_oscillation

http://en.wikipedia.org/wiki/North_Atlantic_Oscillation

http://en.wikipedia.org/wiki/North_Pacific_Oscillation

http://en.wikipedia.org/wiki/Hale_cycle

http://en.wikipedia.org/wiki/Solar_variation

Natural Cycles and Human Activity

Dr. Nicola Scafetta - solar-lunar cycle climate forecast -v- global temperature

• Dr. Nicola Scafetta has developed novel statistical techniques for studying the scaling

exponents of time series-analysis and their fractal/multi-fractal scaling properties. For

example, the Diffusion Entropy Analysis, when used together with more traditional

variance-based methodologies, allows the discrimination among fractal noises generated

by alternative dynamics such as fractal Brownian motion and Levy-walk signals.

• In his recent publications, Dr. Nicola Scafetta has proposed a harmonic wave model to

explain recent observed changes in the global climate - comprised of four major decadal

and multi-decadal cycles (periodicity 9.1, 10.4, 20 and 60 years) - which are not only

consistent with the four major solar / lunar / astronomical cycles – including a corrected

anthropogenic net warming contribution – plus they demonstrate surprising approximate

coincidence with Business Cycles from Joseph Schumpters Economic Wave Series

• The model was not only able to reconstruct the historic decadal patterns of the

temperature since 1850 better than any general circulation model (GCM) adopted by the

IPCC in 2007, but it is apparently able to better forecast the actual temperature pattern

observed since 2000. Note that since 2000 the proposed model is a full forecast. Will the

forecast hold, or is the proposed model is just another failed attempt to forecast climate

change? Only time will tell.....

Scafetta on his latest paper: Harmonic climate model versus the IPCC general circulation climate models

http://wattsupwiththat.com/2012/01/09/scaffeta-on-his-latest-paper-harmonic-climate-model-versus-the-ipcc-general-circulation-climate-models/



NATURAL CYCLES and HUMAN ACTIVITY

• Infinitesimally small differences may be imperceptible to the point of invisibility - how tiny can

influences be to have any effect ? Such influences may take time to manifest themselves –

perhaps not appearing as a measurable effect until many system cycle iterations have been

completed – such is the nature of the "strange attractor." effect. This phenomenon is captured in

the Climate Change “butterfly scenario” example, which is described elsewhere.

• Climate change is not uniform – some areas of the globe (Arctic and Antarctica) have seen a

dramatic rise in average annual temperature whilst other areas have seen lower temperature

gains. The original published temperature record for Climate Change is in red, while the updated

version is in blue. The black curve is the proposed harmonic component plus the proposed

corrected anthropogenic warming trend. The figure shows in yellow the harmonic component

alone made of the four cycles, which may be interpreted as a lower boundary limit for the natural

variability. The green area represents the range of the IPCC 2007 GCM projections.

• The astronomical / harmonic model forecast since 2000 looks in good agreement with the data

gathered up to now, whilst the IPCC model projection is not in agreement with the steady

temperature observed since 2000. This may be due to other effects, such as cooling due to

increased water evaporation (humidity has increased about 4% since measurements began in the

18th centaury) or cloud seeded by jet aircraft condensation trails – which reduce solar forcing by

reflecting energy back into space. Both short-term solar-lunar cycle climate forecasting and

long-term Milankovitch solar forcing cycles point towards a natural cyclic phase of gradual

cooling - which partially off-sets those Climate Change factors (Co2 etc.) due to Human Actions.

Scafetta on his latest paper: Harmonic climate model versus the IPCC general circulation climate models

http://syzygyastro.hubpages.com/hub/Defining-the-Fractal




Climate Cycles

• It also appears that many Human Activity Waves - Business, Social, Political, Economic, Historic and

Pre-historic (Archaeology) Cycles - may be compatible with, and map onto, the twenty-six iterations of

Dansgaard–Oeschger and Bond Cycles with major periodicity 1470 years (and 800 to 1000 years): -

• Oceanic Climate Forcing Cycles: – Duration of Civilisations: -

– Bronze Age City States (2100 – 900 BC)

– Iron Age Mercantile City States – Armies and Empires – (Iron Age Cold Epoch - 900 BC to about 300 BC)

– Western Roman Empire (300 BC – 500 AD)

– Eastern Roman Empire (500 – 1300 AD)

– Islamic Empire – (800 - 1300 AD)

– Vikings and Normans - Nordic Ascendency (700-1500 AD – Medieval Climate Anomaly or “mini Ice Age”)

– The British Empire - Anglo-French Rivalry – Norman Conquest to Entente Cordial (1066 -1911)

– The America s- Mayan, Inca and Aztec Civilisations and Pueblo Indians (Anastasia) – drought in South-Western USA

– Asia - Chinese, Indus Valley and Khmer Civilisation (Amkor)

– Pacific -Polynesian Expansion- Hawaii to Easter Island and New Zealand

• Solar Climate Forcing - Milankovitch Cycles – Solar Insolation driving Quaternary Ice Age Cycles: –

• Pleistocene and Holocene Ice Age Cycles – long, gradual cooling (Ice-ages - Pluvials) followed by rapid Climate Warming

(inter-pluvials) - causing the extinction of Mammoths, Mastodons – with Clovis, Soloutrean and Neanderthal Cultures

• Major Geological Extinction-level Events - Global Kill Moments

– Pre-Cambrian and Cambrian Extinction Events – 1000-542 million years ago

– Permian-Triassic Boundary (PTB) Event – 251.4 million years ago

– Cretaceous – Tertiary Boundary Event – 65 million years ago

– Global Massive Change – Impact of Human Activity – 20,000 years ago to present day (ongoing)

Climate Cycles


1. Stone – Tools for hunting, crafting artefacts and making fire

2. Fire – Combustion for warmth, for cooking and for managing the environment

3. Agriculture – Neolithic Age Human Settlements

4. Bronze – Bronze Age Cities and Urbanisation

5. Ship Building – Communication, Culture ,Trade

6. Iron – Iron Age Empires, Armies and Warfare

7. Gun-powder – Global Imperialism, Colonisation

8. Coal – Mining, Manufacturing and Mercantilism

9. Engineering – Bridges, Boats and Buildings

10. Steam Power – Industrialisation and Transport

11. Industrialisation – Mills, Factories, Foundries

12. Transport – Canals, Railways and Roads

13. Chemistry – Dyestuff, Drugs, Explosives, Petrochemicals and and Agrochemicals

14. Electricity – Generation and Distribution

15. Internal Combustion – Fossil Fuel dependency

16. Aviation – Powered Flight – Airships, Aeroplanes

17. Physics – Relativity Theory, Quantum Mechanics

18. Nuclear Fission – Abundant Energy & Cold War

19. Electronics – Television, Radio and Radar

20. Jet Propulsion – Global Travel and Tourism

21. Global Markets – Globalisation and Urbanisation

22. Aerospace – Rockets, Satellites, GPS, Space Technology and Inter-planetary Exploration

23. Digital Communications – Communication Age -Computers, Telecommunications and the Internet

24. Smart Devices / Smart Apps – Information Age

25. Smart Cities of the Future – The Smart Grid – Pervasive Smart Devices - The Internet of Things

26. The Energy Revolution – The Solar Age – Renewable Energy and Sustainable Societies

27. Hydrogen Economy – The Hydrogen Age – fuel cells, inter-planetary and deep space exploration

28. Nuclear Fusion – The Fusion Age – Unlimited Energy - Inter-planetary Human Settlements

29. Space-craft Building – The Exploration Age - Inter-stellar Cities and Galactic Urbanisation

Kill Moments – Major Natural and Human Activity catastrophes – War, Famine, Disease, Natural Disasters

Culture Moments – Major Human Activity achievements - Technology Development, Culture and History

Industrial Cycles – the phases of evolution for any given industry at a specific location / time (variable)

Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Digital and Information Ages: -


About 8,000 BC:

• The end of the last Ice Age - the last Ice age ended when the great ice sheets finally retreated from Scandinavia and the last glaciers disappeared in Scotland.

• Plants, Animals and People from the south now invaded the new ecosystem after snow and ice had disappeared from the surface of the land. Part of the North Sea basin remains dry to allow continued contact with continental Europe.

• Many of the surviving ice-age mega-fauna – Sabre-toothed Tiger, Dire Wolf, Giant Cave Bear, Mammoth, Mastodon, Giant Elk, Woolly Rhinoceros – started to fall in numbers and become extinct


8,000 - 7000 BC:

• Age of the Hunter Gatherers. The European climaye, environment and ecology became transformed: the boreal forests (coniferous forests) were pushed back to Scandinavia, tundra and steppe were all but removed from the landscape. The dominant vegetation type was now mixed deciduous forest - covering over 80% of the land bordering the North Sea. Humans followed the northward migration of temperate vegetation – along with the animals which browsed upon it – to re-colonise northern Europe.

Technology Shock Wave - Pottery


7,500 BC:

• The melting of the ice sheets resulted in the flooding of the North Sea

basin and the disappearance of the land bridge connecting Britain to the

continent by 8000 years ago. This prevented many tree and plant

species from entering Britain and explains, for example, why we have

only three native species of conifer – the Juniper, Yew and Scots Pine.

6,000 - 2,500 BC:

• Holocene Climate Optimum - the Sea level reached a slightly higher

level than today coinciding with the warmest period during the past

10,000 years - with temperatures about 2 degrees C higher than today.

This was followed by the Iron Age Climate anomaly – over-grazing and

gradual cooling drove man and beast off the high fells, peaks and moors.


Impact of Mesolithic peoples - 8,000-5.000 BC • Mesolithic1 Europeans altered the landscape through fire more thoroughly than their

predecessors. By doing so they created a more predictable environment for themselves.

• Burning grasses helped rejuvenate their environments over a period of five to six years, attracting game, especially if open areas were maintained near water sources. It probably through the use of fire and other land management techniques that created large open areas which is probably most important environmental legacy of the Mesolithic peoples.

• Europeans learned to manipulate their environments and created a mosaic of woodlands and open land that they so favoured for food gathering and hunting. Manipulation could be extreme: it was Mesolithic hunter-gatherers who first deforested the western Isles of Scotland. By 3000 years ago there was no tree left on these isles

Technology Shock Wave - Forestry

http://www.eh-resources.org/timeline/timeline_prehistory.html


Arrival of agriculture, ca 5000-4000 BC Farming, including crops like emmer and einkorn along with domesticated animals, reached north-western Europe via south-eastern and central Europe by ca. 4,800 BC during the Neolithic2 period.

• It is likely that the aboriginal European peoples were not replaced and pushed into the extreme North and West by immigrant farming populations, rather observed and adapted to the new way of life: agriculture. Immigrants would have set examples and pushed hunter-gatherers into agriculture. That must not have been hard since many hunter-gatherers had managed wild life and plant resources in a way that can be described as proto-agriculture. It is also likely that agriculture sprang up independently in some locations and was later supplemented by the grains and animals arriving from the Middle East.

Technology Shock Wave - Agriculture

Technology Shock Wave - Farming

http://www.eh-resources.org/timeline/timeline_prehistory.html


Bronze and Iron Ages, ca. 2100 BC – 1 AD

• By about 1 AD the countryside in many parts of western Europe was already owned, managed and planned. This had been the case for most of the Bronze and Iron Age. Little wildwood remains and the land resource was well planned with field systems in rotation, pasture and coppiced woodland. Hill forts became common and acted as local centres of administration, power and refuge.

Farming systems

• Farming typically revolved around small hamlets and farmsteads with enclosed rectilinear fields - each having areas of pasture, arable and wood. Ploughing became more efficient with the arrival of the iron share (plough point) and a two field rotation was introduced; crops one year followed by a fallow that was grazed by livestock. This lead to surprisingly high yields and fuelled population growth, even though retreat from the uplands had been necessary because of climate deterioration.

Technology Shock Wave – Metalworking

Business Cycles, Patterns and Trends in “Big Data”

Complex Market Phenomena are simply: - "the outcomes of endless conscious, purposeful human actions, by countless

individuals exercising personal choices and preferences - each of whom is trying as best they can to optimise their

circumstances in order to achieve various needs and desires. Individuals, through economic activity strive to attain their

preferred outcomes - whilst at the same time attempting to avoid any unintended consequences leading to unforeseen

outcomes.....”


The Temporal Wave


of Geospatial “Big Data” simultaneously within a Time (history) and Space (location)


temporal Information in today's data-rich landscape – are becoming increasingly difficult to



attribute–space analysis common in GIS Mapping and Spatial Analysis - but now the


of Visual Exploration for Geospatial (location) data within a Temporal (historic) context.

• This time-visualisation approach integrates data visualisation techniques with spatial an

temporal data, thus improving accessibility, exploration and analysis of the huge amounts

of geo-spatial data used to support geo-visual analytics. The temporal wave combines the

strengths of both linear timeline and cyclical wave-form analysis – and is able to represent

both Time (history) and Space (location) data simultaneously, even at different levels of

granularity. Both linear and cyclic trends in space-time data may be represented in

combination with other graphic representations typical for location–space and attribute–

space data-types. The temporal wave can be used in its role as time–space data reference

system, as a time–space continuum representation tool, and as time–space interaction

tool.

Wave-form Analytics in “Big Data”

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of human activity waves, and in the

field of economic cycles business cycles, patterns and trends.












• Complex Adaptive Systems (CAS) and Chaos Theory has also been

used extensively in the field of Futures Studies, Strategic Management,

Natural Sciences and Behavioural Science. It is applied in these domains

to understand how individuals within populations, societies, economies and

states act as a collection of loosely coupled interacting systems which

adapt to changing environmental factors and random events – bio-

ecological, socio-economic or geo-political.

• Complex Adaptive Systems (CAS) and Chaos Theory treats individuals,

crowds and populations as a collective of pervasive social structures which

may be influenced by random individual behaviours – such as flocks of

birds moving together in flight to avoid collision, shoals of fish forming a

“bait ball” in response to predation, or groups of individuals coordinating

their behaviour in order to respond to external stimuli – the threat of

predation or aggression – or in order to exploit novel and unexpected

opportunities which have been discovered or presented to them.


• When Systems demonstrate properties of Complex Adaptive Systems (CAS) - which is

often defined as a collection or set of relatively simple and loosely connected interacting

systems exhibiting co-adapting and co-evolving behaviour (sub-systems or components

changing together in response to the same external stimuli) - then those systems are

much more likely to adapt successfully to their environment and, thus better survive the

impact of both gradual change and of sudden random events. Complexity Theory

thinking has been present in biological, strategic and organisational system studies since

the first inception of Complex Adaptive Systems (CAS) as an academic discipline.

• Complex Adaptive Systems are further contrasted compared with other ordered and

chaotic systems by the relationship that exists between the system and the agents and

catalysts of change which act upon it. In an ordered system the level of constraint means

that all agent behaviour is limited to the rules of the system. In a chaotic system these

agents are unconstrained and are capable of random events, uncertainty and disruption.

In a CAS, both the system and the agents co-evolve together; the system acting to

lightly constrain the agents behaviour - the agents of change, however, modify the

system by their interaction. CAS approaches to behavioural science seek to understand

both the nature of system constraints and change agent interactions and generally takes

an evolutionary or naturalistic approach to crowd scenario planning and impact analysis.




in nature to biological behaviour in an population than to truly Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable long-term



shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards) – by

the ability of Financial markets to rapidly absorb and recover from these events.

• Unexpected and surprising Cycle Pattern changes have historically occurred during

regional and global conflicts being fuelled by technology innovation-driven arms

races - and also during US Republican administrations (Reagan and Bush - why?).

Just as advances in electron microscopy have revolutionised the science of biology

- non-stationary time series wave-form analysis has opened up a new space for


Composite Economic Wave Series

• Economic systems tend to demonstrate Complex Adaptive System (CAS) behaviour – rather than a simple series of chaotic “Random Events” – very similar to the behaviour of living organisms. The remarkable long-term stability and resilience of market economies is demonstrated by the impact and subsequent recovery from Wild Card and Black Swan Events. Surprising pattern changes occur during wars, arm races, and during Republican administrations, causing unexpected stock market crashes - such as oil price shocks and credit crises. Wave-form Analytics for non-stationary time series analysis opens up a new and remarkable opportunity for business cycle studies and economic policy diagnostics.

• The role of time scale and preferred reference from economic observation is explored in detail. For example - fundamental constraints for Friedman's rational arbitrageurs are re examined from the view of information ambiguity and dynamic instability. Alongside Joseph Schumpter’s Economic Wave Series and Strauss and Howe’s Generation Waves, we also discuss Robert Bronson's SMECT Forecasting Model - which integrates both Business and multiple Stock-Market Cycles into its structure.....

• Composite Economic Wave Series

– Saeculum - Century Waves

– Generation Waves (Strauss and Howe)

– Joseph Schumpter’s Economic Wave Series

– Robert Bronson’s SMECT Forecasting Model

Weak Signals, Wild Cards and

Black Swan Event Scenarios • In this section, we examine empiric evidence from global “Big Data” on how shock waves

to geo-political economic and business systems impact on business cycles, patterns and

trends. We first review Gail's work (1999), which uses long-running restrictions to identify

shock waves, and examine whether the identified shocks can be plausibly interpreted: -

• Wild card and Black Swan Event Types



– Political, Economic and Social Change

– Global Conflict – War, Terrorism, and Insecurity

– Natural Disasters and Catastrophes – Global Massive Change Events

• We do this in three ways. Firstly, we derive additional long-run restrictions and use them as

identification tests. Secondly, we compare the qualitative implications from the model with the

impulse responses of variables such as production, wages and consumption. Third, we test

whether some standard .exogenous. variables predicate the shock events. We discovered that

that Weak Signals may predicate coming technology shock waves, oil price shocks, and military

conflict. We then show ways in which a standard DGE model can be modified to fit Gail's finding

that a positive technology shock may lead to lower labour input. Finally, we re-examine the

properties of the other key shocks to the economic system and demonstrate the impact of oil

price shocks and military conflict .

Price Index Inflation

Waves, Cycles, Patterns and Trends

• Business Cycles were once thought to be an economic phenomenon due to periodic fluctuations in economic activity. These mid-term economic cycle fluctuations are usually measured using Real (Austrian) Gross Domestic Product (rGDP). Business Cycles take place against a long-term background trend in Economic Output – growth, stagnation or recession – which affects Money Supply as well as the relative availability and consumption (Demand v. Supply and Value v. Price) of other Economic Commodities. Any excess of Money Supply may lead to an economic expansion or “boom”, conversely shortage of Money Supply (Money Supply shocks – the Liquidity Trap) may lead to economic contraction or “bust”. Business Cycles are recurring, fluctuating levels of economic activity experiences in an economy over a significant timeline (decades or centuries).

• The five stages of Business Cycles are growth (expansion), peak, recession (contraction), trough and recovery. Business Cycles were once widely thought to be extremely regular, with predictable durations, but today’s Global Market Business Cycles are now thought to be unstable and appear to behave in irregular, random and even chaotic patterns – varying in frequency, range, magnitude and duration. Many leading economists now also suspect that Business Cycles may be influenced by fiscal policy as much as market phenomena - even that Global Economic “Wild Card” and “Black Swan” events are actually triggered by Economic Planners in Government Treasury Departments and in Central Banks as a result of manipulating the Money Supply under the interventionist Fiscal Policies adopted by some Western Nations.

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what

they can do with it - is largely unknown, undisclosed and un-policed.....

Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and

who we see is now watched and recorded

“Big Data” Global Content Analysis

• “Big Data” refers to those aggregated datasets whose size and scope is beyond the capability of conventional transactional Database Management Systems and Enterprise Software Tools to capture, store, analyse and manage. This definition of “Big Data” is of necessity subjective and qualitative – “Big Data” is defined as a large collection of unstructured information, which, when initially captured, contains sparse or undiscovered internal references, links or data relationships.

• Data Set Mashing or “Big Data” Global Content Analysis – supports Strategic Foresight Techniques such as Horizon Scanning, Monitoring and Tracking by taking numerous, apparently un-related RSS and other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH Structures and Unstructured Databases and Document Management Systems for interrogating using Data Mining and Real-time Analytics – that is, searching for and identifying possible signs of hidden data relationships (Facts/Events) – in order to discover and interpret previously unknown “Weak Signals” indicating emerging and developing Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative transformations, catalysts and agents of change which may develop and unfold as future “Wild Card” or “Black Swan” events.

“Big Data” Global Content Analysis



in nature to biological behaviour in an organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable



shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for



• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. This is

introduced here for the first time in the study of human activity waves, and in the

field of economic cycles business cycles, patterns and trends.











“Big Data”

“Big Data”

Normal, daily routine activities from our everyday life generates vast amounts of data. Who owns this data, who has access to it, and what they can do with it - is largely unknown, undisclosed and un-policed..... Little-by-little, more and more aspects of our daily life are being monitored - meaning intimate details of what we do, where we go, and who we see is now watched and recorded.

“Big Data” Definitions

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

“Big Data” Applications • Science and Technology

– Pattern, Cycle and Trend Analysis

– Horizon Scanning, Monitoring and Tracking

– Weak Signals, Wild Cards, Black Swan Events

• Multi-channel Retail Analytics – Customer Profiling and Segmentation

– Human Behaviour / Predictive Analytics

• Global Internet Content Management

– Social Media Analytics

– Market Data Management

– Global Internet Content Management

• Smart Devices and Smart Apps

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Broadband / Home Entertainment

– Call Details Records

– Internet Content Browsing

– Media / Channel Selections

– Movies, Video Games and Playlists

• Smart Metering / Home Energy

– Energy Consumption Details Records

• Civil and Military Intelligence Digital Battlefields of the Future – Data Gathering

Future Combat Systems - Intelligence Database

Person of Interest Database – Criminal Enterprise,

Political organisations and Terrorist Cell networks

Remote Warfare - Threat Viewing / Monitoring /

Identification / Tracking / Targeting / Elimination

HDCCTV Automatic Character/Facial Recognition

• Security Security Event Management - HDCCTV, Proximity

and Intrusion Detection, Motion and Fire Sensors

Emergency Incident Management - Response

Services Command, Control and Co-ordination

• Biomedical Data Streaming Care in the Community

Assisted Living at Home

Smart Hospitals and Clinics

• SCADA Operational Technology SCADA Remote Sensing, Monitoring and Control

Smart Grid Data (machine generated data)

Vehicle Telemetry Management

Intelligent Building Management

Smart Homes Automation

Exploitation – “Big Data”

• There has been much speculation about how industries will cash in on “Big Data” In a nutshell “Big Data” occurs in volumes or structures that exceeds the functionality / capacity of conventional hardware, database platforms and analytical tools.

• Social media and search are leading the way with big data applications. As “Big Data” tools and methods enter the mainstream we will see businesses make use of the "data exhaust" that today doesn't get exploited To put it bluntly, most companies are failing to leverage their data assets by failing to realise the benefits of the huge volumes of data they are already generating.

Big Data Analytics Goes Big Time

Big Data Analytics Goes Big Time • Organizations around the globe and across

industries have learned that the smartest business decisions are based on fact, not gut feel. That means they're based on analysis of data, and it goes way beyond the historical information held in internal transaction systems. Internet click-streams, sensor data, log files, mobile data rich with geospatial information, and social-network comments are among the many forms of information now pushing information stores into the big-data league above 10 terabytes.

• Trouble is, conventional data warehousing deployments can't scale to crunch terabytes of data or support advanced in-database analytics. Over the last decade, massively parallel processing (MPP) platforms and column-store databases have started a revolution in data analysis. But technology keeps moving, and we're starting to see upgrades that are blurring the boundaries of known architectures. What's more, a whole movement has emerged around NoSQL (not only SQL) platforms that take on semi-structured and unstructured information.

This info-graph presents from 2011 to 2013 update on what's available, with options including ExtremeData xdb, EMC's Greenplum appliance, Hadoop and MapReduce, HP's recently acquired the Autonomy and Vertica platforms, IBM's separate DB2-based Smart Analytic System and Netezza offerings, and Microsoft's Parallel Data Warehouse. Smaller, niche database players include Infobright, Kognitio and ParAccel. Teradata reigns at the top of the market, picking off high-end defectors from industry giant Oracle. SAP's Sybase unit continues to evolve Sybase IQ, the original column-store database. In short, there's a platform for every scale level and analytic focus

Big Data Partnership

Training - For more information on Big Data Partnership’s training offerings, please visit the Training page. Feel free to Contact Us directly to discuss your specific needs.

http://bigdatapartnership.com/our-services/training/

http://bigdatapartnership.com/contact-us/

Big Data Partnership 3D Approach

• Discovery - As enterprises move into this new age for data analytics, companies can often struggle to identify where in their large data architecture, big data software and techniques can be utilised. Big Data Partnership can help those organisations understand where those use cases are through short workshop engagements. These are typically 2-5 days long and will help not only identify where Big Data Analytics could help drive more customer insight and ROI but also educate on what tools are in the eco-system.

• Develop - Even with solid use cases and a good understanding of how Big Data software and techniques could help businesses, it is not always easy to prove the model and commit to the necessary investment to really make the positive transformation in an organisation. One way of doing this is to take a single use case and develop a Proof of Concept to prove the expected ROI and business benefit and also validate the technology. This level of engagement can typically be a month long and can help businesses not only take the big step towards big data but also help them understand whether the expected ROI is there.

• Deliver - Big Data Partnership are able to assist enterprises in fully realising their Big Data initiatives through offering fixed price and day-based consultancy to help deliver full data analytics projects. We understand that each customer has differing needs, therefore we tailor our approach specific to each client. Effective big data is not just about predetermined buckets or templates for business intelligence; it is about meaningful analysis and processing of information in a way that is highly relevant to the business. We have highly skilled Data Scientists as well as deep rooted Big Data Engineers who can help you fully make the most of your implementations and ensure success of your Big Data projects.

“Big Data”

• Put yourself in the Big Data driver’s seat.

• Today, companies are generating massive amounts of data—everything from web clicks, to customer transactions, to routine business events—and attempting to mine that data for trends that can inform better business decisions.

• Quantivo enables a new analytics experience that is bound only by imagination of the user - it’s a full stack for turning raw data into intelligence. The Quantivo platform features patented, pattern-based technology that efficiently integrates event data across multiple sources, in hours not weeks. Your query quest starts here.

“Big Data” Analytics

Quantivo sifts through mountains of data—and spots the patterns that matter.

• When faced with overwhelming amounts of data, looking for the big “aha” can be next to impossible. That is, unless you’ve got Quantivo on your side. Unlike the other vendors that overpromise and under-deliver, Quantivo hits the mark with pattern-based analytics that brings Big Data down to size by tracking relationships between attributes and ignoring redundancies. With easy-to-use tools, users can zero in on predictive and repeatable patterns and trends—without losing any of the original data. In addition, Quantivo pattern-based analytics: -

– Creates behavioural segments derived from a combination of contextually specific attributes and online/offline detailed event data

– Uncovers buried relationships that link attributes to behaviours

– Tracks how behaviors change over time—and identifies the trigger for these changes

– Helps you “learn what you don’t know” by intelligently auto-compiling lists of patterns existing in your data

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

Mobile Enterprise (MEAPs)

“Big Data” – Analysing and Informing

• “Big Data” is now a torrent raging through every aspect of the global economy – both the public sector and private industry. Global enterprises generate enormous volumes of transactional data – capturing trillions of bytes of information from the external supply chain – global markets, customers and suppliers – and from their own internal business operations.

– SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN?

– GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE?

– INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW?

– SERVICE LAYER – Real-time Analytics – WHY?

– COMMUNICATION LAYER – Mobile Enterprise Platforms

– INFRASTRUCTURE LAYER – Cloud Service Platforms


• SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN? – Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices

– Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV

– Remote Monitoring, Command and Control – SCADA

• GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE? – Person and Social Network Directories - Personal and Social Media Data

– Location and Property Gazetteers - Building Information Models (BIM)

– Mapping and Spatial Analysis - Topology, Landscape, Global Positioning Data

• INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW? – Content – Structured and Unstructured Data and Content

– Information – Atomic Data, Aggregated, Ordered and Ranked Information

– Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks


• SERVICE LAYER – Real-time Analytics – WHY? – Global Mapping and Spatial Analysis

– Service Aggregation, Intelligent Agents and Alerts

– Data Analysis, Data Mining and Statistical Analysis

– Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis

• COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid – Connectivity - Smart Devices, Smart Apps, Smart Grid

– Integration - Mobile Enterprise Application Platforms (MEAPs)

– Backbone – Wireless and Optical Next Generation Network (NGE) Architectures

• INFRASTRUCTURE LAYER – Cloud Service Platforms – Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models

– Infrastructure – Network, Storage and Servers

– Applications – COTS Software, Utilities, Enterprise Services

– Security – Principles, Policies, Users, Profiles and Directories, Data Protection

What Google Searches about the Future tell us about the Present...

• Internet Research published recently demonstrates how Internet Searches about future topics have a significant link to the economic success of the native country of the Search Requester.

• Google (GOOG) search data have become a statistical gold mine for academics, scientists, and number crunchers, who have used it for everything from predicting flu outbreaks to determining to what extent racial prejudice robbed Barack Obama of otherwise certain votes.

• Two academics in the U.K., Warwick Business School associate professor Tobias Preis and Dr. Helen Susannah Moat of University College London, analyzed more than 45 billion Google searches performed during 2012 and calculated the national ratio between searches that included “2013” and those that included “2011” for the native country of the Search Requester,

• They found that countries where “Internet users … search for more information about the future tend to have a higher per-capita GDP,” says Preis, who created a stir in 2010 when he used a similar data-crunching approach to quantify and model stock price fluctuations of companies on the Standard & Poor’s 500 index. “The more a country is looking to the future using Internet Searches, then the more successful economically the country is.”

• The rational is, when the economy is humming along nicely, it is easier to be optimistic—to plan vacations, buy season tickets, investigate investment opportunities, etc.

• Of all nations, the Germans are the most forward-looking, knocking Britons from the top spot. Preis explained that the U.K. scored so highly a year earlier because of the high national expectation around the forthcoming 2012 London Olympic Games. This year, the Germans are looking forward to a pivotal federal election. Preis, a German national, declined to comment on whether Germany’s exuberance for the future bodes well for incumbent Angela Merkel.

• Interestingly, the U.S. ranks 11th, up from 15th a year earlier. The 2012 findings showed that entering an election year, more Americans were looking backward to 2010. Preis says that this year, Americans as a whole are more optimistic about 2013 than they were a year earlier,.

• Economic laggards Pakistan, Vietnam, and Kazakhstan round out the bottom of the list.

http://investing.businessweek.com/research/stocks/snapshot/snapshot.asp?ticker=GOOG

http://www.businessweek.com/articles/2013-01-16/5-social-media-tools-to-fight-the-flu

http://campaignstops.blogs.nytimes.com/2012/06/09/how-racist-are-we-ask-google/

Clustering in “Big Data” “A Cluster is a grouping of the same, similar and equivalent, data

elements containing values which are closely distributed – or

aggregated – together”

Clustering is a technique used to explore content and understand

information in every business and scientific field that collects and

processes verify large volumes of data

Clustering is an essential tool for any “Big Data” problem

Clustering Phenomena in “Big Data”

• “Big Data” refers to vast aggregations (super sets) consisting of numerous individual

datasets (structured and unstructured) - whose size and scope is beyond the capability of

conventional transactional (OLTP) or analytics (OLAP) Database Management Systems

and Enterprise Software Tools to capture, store, analyse and manage. Examples of “Big

Data” include the vast and ever changing amounts of data generated in social networks

where we maintain Blogs and have conversations with each other, news data streams,

geo-demographic data, internet search and browser logs, as well as the ever-growing

amount of machine data generated by pervasive smart devices - monitors, sensors and

detectors in the environment – captured via the Smart Grid, then processed in the Cloud –

and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.

• Data Set Mashing and “Big Data” Global Content Analysis – drives Horizon Scanning,

Monitoring and Tracking processes by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS)

DWH Structures and Document Management Systems for Real-time Analytics – searching

for and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown Data Relationships driven by hidden Clustering

Forces – revealed via “Weak Signals” indicating emerging and developing Application

Scenarios, Patterns and Trends - in turn predicating possible, probable and alternative

global transformations which may unfold as future “Wild Card” or “Black Swan” events.

“Big Data”

“Big Data”

• The profiling and analysis of very large aggregated datasets in order to determine a

‘natural’ or implicit structure of data relationships or groupings – in order to discover

hidden data relationships driven by unknown factors where no prior assumptions

are made concerning the number or type of groups discovered or Cluster / Group

relationships, hierarchies or internal data structures – is a critically important starting

point – and forms the basis of many statistical and analytic applications.

• The subsequent explicit Cluster Analysis of discovered data relationships is an

important and critical technique which attempts to explain the nature, cause and

effect of unknown clustering forces driving implicit profile similarities, mathematical

or geographic distributions. Geo-demographic techniques are frequently used in

order to profile and segment Demographic and Spatial data by ‘natural’ groupings –

including common behavioural traits, Clinical Trial, Morbidity or Actuarial outcomes –

along with numerous other shared characteristics and common factors Cluster

Analysis attempt to understand and explain those natural group affinities and

geographical distributions using methods such as Causal Layer Analysis (CLA).....

Clustering in “Big Data”

Clustering in “Big Data”

“A Cluster is a group of profiled data similarities aggregated closely together”

• Clustering is an essential tool for any “Big Data” problem. Cluster Analysis of both

explicit (given) or implicit (discovered) data relationships in “Big Data” is a critical

technique which attempts to explain the nature, cause and effect of the forces which drive

clustering. Any observed profiled data similarities – geographic or temporal aggregations,

mathematical or statistical distributions – may be explained through Causal Layer Analysis.

• Cluster Analysis is a technique used to explore content and information in order to

understand very large volumes of data in every business and scientific field that collects

and processes vast quantities of machine generated (automatic) data

– Choice of clustering algorithm and parameters are processes and data dependent

– Approximate Kernel K-means provides a good trade-off between clustering accuracy

and data volumes, throughput, performance and scalability

– Challenges include homogeneous and heterogeneous data (structured versus

unstructured data), data quality, streaming, scalability, cluster cardinality and validity

Cluster Types Deep Space Galactic Clusters

Hadoop Cluster – “Big Data” Servers

Molecular Clusters

Geo-Demographic Clusters

Crystal Clusters

Cluster Types DISCIPLINE CLUSTER TYPE CLUSTERS DIMENSIONS DATA TYPE DATA SOURCE CLUSTERING

FACTORS / FORCES

Astrophysics Distribution of Matter through the Universe across Space and Time

Star Systems Stellar Clusters Galaxies Galactic Clusters

Mass / Energy Space / Time

Astronomy Images Optical Telescope Infrared Telescope Radio Telescope X-ray Telescope

Gravity Dark Matter Dark Energy

Climate Change Temperature Changes Precipitation Changes Ice-mass Changes

Hot / Cold Dry / Wet More / Less ice

Temperature Precipitation Sea / Land Ice

Average Temperature Average Precipitation Greenhouse Gases %

Weather Station Data Ice Core Data Tree-ring Data

Solar Forcing Oceanic Forcing Atmospheric Forcing

Actuarial Science Morbidity Epidemiology

Place / Date of birth Place / Date of death Cause of Death

Birth / Death Longevity Cause of Death

Medical Events Geography Time

Biomedical Data Demographic Data Geographic data

Register of Births Register of Deaths Medical Records

Health Wealth Demographics

Price Curves Economic Modelling Long-range Forecasting

Economic growth Economic recession

Bull markets Bear markets

Monetary Value Geography Time

Real (Austrian) GDP Foreign Exchange Rates Interest Rates Price movements Daily Closing Prices

Government Central Banks Money Markets Stock Exchange Commodity Exchange

Business Cycles Economic Trends Market Sentiment Fear and Greed Supply / Demand

Business Clusters Retail Parks Digital / Fin Tech Leisure / Tourism Creative / Academic

Retail Technology Resorts Arts / Sciences

Company / SIC Geography Time

Entrepreneurs Start-ups Mergers Acquisitions

Investors NGAs Government Academic Bodies

Capital / Finance Political policy Economic policy Social policy

Elite Team Sports Performance Science

Winners Loosens

Team / Athlete Sport / Club League Tables Medal Tables

Sporting Events Team / Athlete Sport / Club Geography Time

Performance Data Biomedical Data

Sports Governing Bodies RSS News Feeds Social Media Hawk-Eye Pro-Zone

Technique Application Form / Fitness Ability / Attitude Training / Coaching Speed / Endurance3

Future Management Human Activity Natural Events

Random Events Waves, Cycles, Patterns, Trends

Random Events Geography Time

Weak Signals Wild Card Events Black Swan Events

Global Internet Content / Big Data Analytics - Horizon Scanning, Tracking and Monitoring

Random Events Waves, Cycles, Patterns, Trends, Extrapolations

GIS MAPPING and SPATIAL DATA ANALYSIS

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.....

GIS MAPPING and SPATIAL DATA ANALYSIS

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes.

• Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).

World-wide Visitor Count – GIS Mapping

Geo-Demographic Profile Data GEODEMOGRAPHIC INFORMATION – PEOPLE and PLACES

Age Dwelling Location / Postcode

Income Dwelling Owner / Occupier Status

Education Dwelling Number-of-rooms

Social Status Dwelling Type

Marital Status Financial Status

Gender / Sexual Preference Politically Active Indicator

Vulnerable / At Risk Indicator Security / Threat Indicator

Physical / Mental Health Status Security Vetting / Criminal Record Indicator

Immigration Status Profession / Occupation

Home / First language Professional Training / Qualifications

Race / ethnicity / country of origin Employment Status

Household structure and family members Employer SIC

Leisure Activities / Destinations Place of work / commuting journey

Mode of travel to / from Leisure Activities Mode of travel to / from work

BTSA Induction Cluster Map

Geo-Demographic Profile Clusters

Star Clusters

• New and

improved

understanding

of star cluster

physics brings

us within reach

of answering a

number of

fundamental

questions in

astrophysics,

ranging from

the formation

and evolution

of galaxies –

to intimate

details of the

star formation

process itself.

Hertzsprung Russell

• The Hertzsprung

Russell diagram is a

scatter plot Cluster

Diagram which shows

the Main Sequence

Stellar Lifecycles.

• A Hertzsprung Russell

diagram is a scatter

plot Stellar Cluster

Diagram which

demonstrates the

relationship between a

stars temperature and

luminosity over time –

using red to blue colour

to indicate the mean

temperature at the

surface of the star.

Star

Clusters • The Physics of star

clustering leads us

to new questions

related to the

make-up of stellar

clusters and

galaxies, stellar

populations in

different types of

galaxy, and the

relationships

between high-

stellar populations

and local clusters –

overall, resolved

and unresolved –

the implications

for their relative

formation times

and galactic star-

formation histories.

Cluster Analysis

• Data Representation – Metadata - identifying common Data Objects, Types and Formats

• Data Taxonomy and Classification – Similarity Matrix (labelled data)

– Grouping of explicit data relationships

• Data Audit - given any collection of labelled objects..... – Identifying relationships between discrete data items

– Identifying common data features - values and ranges

– Identifying unusual data features - outliers and exceptions

• Data Profiling and Clustering - given any collection of unlabeled objects..... – Pattern Matrix (unlabelled data)

– Discover implicit data relationships

– Find meaningful groupings (Clusters)

– Predictive Analytics – Event Forecasting

– Wave-form Analytics – Periodicity, Cycles and Trends

– Explore hidden relationships between discrete data features

Many big data problems feature unlabeled objects

Distributed Clustering Models

Number of processors

Speedup Factor - K-means

Speedup Factor - Kernel K-means

2 1.1 1.3

3 2.4 1.5

4 3.1 1.6

5 3.0 3.8

6 3.1 1.9

7 3.3 1.5

8 1.2 1.5

K-means

Kernel K -means

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core

Intel Xeon processors, with 8GB memory in intel07 cluster

Network communication cost increases with the no. of processors

Cluster Analysis

Clustering Algorithms

Hundreds of spatial, mathematical and statistical clustering algorithms are available –

many clustering algorithms are “admissible” – but no single algorithm alone is “optimal”

• K-means

• Gaussian mixture models

• Kernel K-means

• Spectral Clustering

• Nearest neighbour

• Latent Dirichlet Allocation

Challenges in “Big Data” Clustering

• Data quality

• Volume – number of data items

• Cardinality – number of clusters

• Synergy – measures of similarity

• Values – outliers and exceptions

• Cluster accuracy - validity and verification

• Homogeneous versus heterogeneous data (structured and unstructured data)

k-means/Gaussian-Mixture Clustering of Audio Segments

Distributed Clustering Model Performance

Clustering 100,000 2-D points with 2 clusters on 2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster Network communication cost increases with the no. of processors

K-means Kernel K -means

HPCC Clustering Models

High Performance / High Concurrence Real-time Delivery (HPCC)

Distributed Clustering Models

Distributed Clustering Model Performance

Distributed Approximate Kernel K-means

2-D data set with 2 concentric circles

2.3 GHz quad-core Intel Xeon processors, with 8GB memory in intel07 cluster

Run-time

Size of dataset (no. of Records)

Benchmark Performance (Speedup Factor )

10K 3.8

100K 4.8

1M 3.8

10M 6.4

Historic Economic Trends in “Big Data”

Sixty years ago, in the year 1950…

In 1950, nobody was taking China seriously as a global economic power – China was way off our economic “radar” - and remained so for a very long time indeed !

The United States was by far the largest economy in the world, in terms of GDP, challenged only by the former Soviet Union (USSR)

China was emerging as a much smaller economy - comparable to France in size (GDP), accounting for less than 5% of global economic activity

Created by the Forecasting Net www.forecastingnet.com October 2011

http://www.forecastingnet.com/

Asia - 1985

1985: Asia, North America, and Western

Europe are almost equal in “size”

Created by the Forecasting Net

www.forecastingnet.com

October 2011


How regions perform….. The rise of Asia…..

19

85

Sixty years on, in the year 2015…

Chinas’ economy has grown continuously – and is now challenging the leading economic position of the USA

China is the undisputed champion of world economic achievement - even during the crisis era, post 2008

China is by far the largest shareholder of U.S. debt

Asia has been substituting the West, in terms of percentage contribution to global GDP, for many decades, initiating a shift of the international balance of power

It all started with Japan’s economic miracle but it really took off with China’s and, to a lesser degree, India’s growth frenzy over the last few decades

This is not a temporary but a long term trend that we could have easily identified as early as the 1980s, if only we looked…



How countries perform…..

C

hin

a U

SA


www.forecastingnet.com October 2011


Asia - Today The rise of Asia…..

19

85


www.forecastingnet.com October 2011


The rise of the BRICs

China and to a lesser degree India are the long term winners of the economic growth “race”, in terms of percentage contribution to global GDP per year

The continuous long term decrease of the relative economic power-expressed as the percentage contribution to global GDP-of the United States and the largest Western European countries, Germany, United Kingdom, and France, is obvious after 1960

Japan’s end of the economic miracle in the early 1990s resulted to the sharp decline of its contribution to global GDP, after a growth frenzy that lasted many decades

The gradual deterioration of the relative economic power of the USSR-after 1960-that led to the collapse of the Soviet Union and the subsequent rise of the Russian Federation, is apparent

Brazil’s contribution to global GDP starts to decline after 1980. Some catching up is evident during the last three years following the start of the 2008 credit crunch


http://upload.wikimedia.org/wikipedia/commons/4/41/Flag_of_India.svg


Future Trends in “Big Data”


All human actions – “are simple individual choices in response to subjective

personal value judgments – which ultimately determine all market phenomena

– patterns of innovation and investment, supply and demand, production and

consumption, costs and prices, levels of profits and losses and ultimately real

(Austrian) Gross Domestic Product (rGDP).....”



Slow is smooth, smooth is fast.....

.....advances in “Big Data” have lead to a revolution

in economic forecasting and predictive modelling –

but it takes both human ingenuity, and time, for

Economic Models to develop and mature.....


• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service Providers and Future Management Strategy Consulting firms – Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media


– Email: [email protected] (Office) – Telephone: +44 (0) 1527 591020 (Office)











Business Cycles, Patterns and Trends: -

TECHNICAL APPENDICES

The Nature of Uncertainty – Randomness Classical Mechanics (Newtonian Physics) – governs the behaviour of everyday objects –

any apparent randomness is as a result of Unknown Forces

Quantum Mechanics – governs the behaviour of unimaginably small sub-atomic particles – all events are truly and intrinsically both symmetrical and random

Relativity Theory – governs the behaviour of impossibly super-massive cosmic structures – any apparent randomness or asymmetry is as a result of Quantum Dynamics

Wave Mechanics (String Theory) – integrates the behaviour of every type of object –randomness and asymmetry is a result of Unknown Forces and Quantum Dynamics

The Future of “Big Data” in Fin Tech

Financial Technology

Masters of a volatile Universe

Challenged by complexity and turbulence, leaders at the forefront of Financial Technology (Fin Tech) innovation are gaining an uncanny

ability to come up with the right solution at the right time.

Adapting to the New Regulatory Environment

• Technology has dramatically advanced the trading of financial instruments over the past two decades. During

the last twenty years, the practice of “open outcry” trading has been replaced by electronic trading platforms for

all equity, bond and currency markets – with the sole and notable exception of the London Metals Exchange.

• This shift has fundamentally changed the way these markets behave and has led to higher trading volumes.

Regulatory changes have also played a role in the increasing use of automated trading and asset management

processes and electronic exchanges. Today, new regulations are poised to accelerate this trend, bringing even

larger trading volumes and diminished cost-of-business to the huge derivatives market., amongst other areas.

• The proliferation of technology is certain, and as regulation forces more transactions onto electronic platforms,

most financial market participants will need to change the way they operate. This reality poses both challenges

and opportunities. To successfully navigate the new environment, market participants will need to adapt

strategies and determine how to best leverage current advances in Financial Technologies (Fin Tech).


• Technology with a Purpose: -

Financial Technology (Fin Tech)

• Technology has long been an essential

behind-the-scenes partner in the financial

services industry, providing the innovative

incremental advances necessary for the

industry to upgrade and expand its services.

Improvements in storage capacity and

processing speed, for example, have had a

profound impact on data management and

transactional capabilities, with accompanying

reductions in cost.

• Despite these and other advances, the

industry has struggled to fully leverage the

power and promise of Financial Technology

(Fin Tech), with market participants eager for

solutions that are not only richer, faster and

cheaper - but that also offer enhanced data

security on top of greater business efficiency.

Financial Technology Business Categories

Retail Banking

• Accounts

• Deposits

• Payments

• CRM

• Wealth Management

• Multi-channel Retail Platform

Merchant Banking

• Trade Desk / Automatic Trading

• Risk Management

• Asset Portfolio Management

• Finance

• Treasury

• Compliance

• Settlements

• Planning and Strategy

Financial Technology – Operational Regimes

Corporate Responsibility Regimes: -

• Business Principles Regime

• Enterprise Governance Regime

• Reporting and Controls Regime

• Enterprise Risk Management Regime

• Enterprise Performance Management Regime

Reporting and Controls Frameworks

• Accounting Standards • GAAP • IFRS •

Systemic / Operational Risk Frameworks

• Outsights • COSO •

Liquidity Risk Frameworks – Capital Adequacy

• Basle II - Banking

• Solvency II – Insurance

Financial Technology Architecture

Financial Technology Architecture

• The Fin Tech solution that is emerging across the

industry is not a technological abstraction, but rather

consists of several key elements that already lie within,

or close to, the realm of current capabilities.

• These include: -

– Efficiently quantify risk and return against asset portfolios.

– Proactive scenario analysis and “what if” capabilities

– Increased forward pricing management and enhanced

market risk performance with real-time analytics

– The ability to dramatically drive up business benefits

– The ability to dramatically drive down processing costs

– The ability to store, process, use and re-use information of

all types and from all sources quickly and make it

accessible anywhere - from multiple smart device types

– Automated low-maintenance platforms that allow for easy

data capture, streaming, replication and analysis

– New approaches to developing tightly integrated systems

which are based on best-of-class components

Financial Technology Road-map

• Technology innovation is increasingly viewed as a strategic imperative, rather than simply a support

function, as a game-changing chapter will begin to unfold across the whole of the financial services

industry. The idea of a seamlessly integrated approach to capturing, processing, managing, delivering

and correlating data has the potential to change not only the technology landscape but, much more

importantly, the entire business landscape as well.

• The bewildering array of technology tools available today, coupled with emerging new business models

and innovative approaches to compliance and risk geared to address current and future regulatory and

organisational challenges, can shift this idea from a future possibility to an actual solution for today.

Third-party providers, with their experience, expertise and deep resources, can help realise the future

Financial Technology vision - today. At the same time, although the stakes are high for solution

providers, the opportunities are enormous. Firms that excel at execution in achieving optimal computing

power and that have the capability to leverage it will be best-positioned to reap the benefits.

Fin Tech – Digital Enterprise

• The term Digital Technology is used to describe the use of digital resources in order to discover,

analyse, create, exploit, communicate and consume useful information within a digital context. This

encompasses the use of various Smart Devices and Smart Apps, Next Generation Network (NGN)

Digital Communication Architectures, web 2.0 and mobile programming tools and utilities, mobile and

digital media e-business / e-commerce platforms, and mobile and digital media software applications: -

• Cloud Services

– Secure Mobile Payments / On-line Gaming / Digital Marketing / Automatic Trading

– Automatic Data – Machine-generated Data for Remote Sensing, Monitoring and Control

• Mobile – Smart Devices, Smart Apps, Apps Shops and the Smart Grid

• Social Media Applications – FaceBook, LinkedIn, MySpace, Twitter, U-Tube

• Digital and Social Customer Relationship Management – eCRM and sCRM

• Multi-channel Retail – Home Banking, e-commerce and e-business platforms

• Next Generation Network (NGN) Digital Communication Architectures – 4G, Wifi

• Next Generation Enterprise (NGE) – Digital Enterprise Target Operating Models (eTOM)

• Big Data – Discovery of hidden relationships between data items in vast aggregated data sets

• Fast Data – Data Warehouse Engines, Data Marts, Data Mining, Real-time / Predictive Analytics

• Smart Buildings – Security, Environment Control, Energy, Multimedia and RSS Newsfeeds Automation

Fin Tech – Digital Enterprise

Digital Enterprise Planning Methodology: -

1. Understand business and technology environment–

Business Outcomes, Goals and Objectives domains

2. Understand business and technology challenges /

opportunities – Business Drivers and Requirements

3. Gather the evidence to quantify the impact of those

opportunities – Business Case

4. Quantify the business benefits of resolving the

opportunities – Benefits Realisation

5. Quantify the changes need to resolve the

opportunities – Business Transformation

6. Understand Stakeholder Management issues –

Communication Strategy

7. Understand organisational constraints –

Organisational Impact Analysis

8. Understand technology constraints – Technology

Strategy

Digital Enterprise Delivery Methodology: -

1. Understand success management – Scope, Budget,

Resources, Dependencies, Milestones, Timeline

2. Understand achievement measures – Critical Success

Factors / Key Performance Indicators / ROI

3. Produce the outline supporting planning documentation -

Business and Technology Roadmaps

4. Complete the detailed supporting planning documentation

– Programme and Project Plans

5. Design the solution options to solve the challenges –

Business and Solution Architectures

6. Execute the preferred solution implementation – using

Lean / Digital delivery techniques

7. Report Actual Progress, Issues, Risks and Changes

against Budget / Plan / Forecast

8. Delivery, Implementation and Go-live !

The Financial Technology driven Digital Enterprise is all about doing things better today in order to design and build a

better tomorrow. The Digital Enterprise is driven by rapid response to changing conditions so that we can create and maintain

a brighter future for our stakeholders to enjoy. The Digital Enterprise evolves from analysis, research and development into

long-term Strategy and Planning – ranging in scale from the formulation and shaping of Public-sector Political, Economic and

Social Policies to Private-sector Business Programmes, Work-streams and Projects for organisational change and business

transformation – enabling us to envision and achieve our desired future outcomes, goals and objectives

Portfolio Allocation and Modelling – A Technological Arms Race?

• Ever since the advent of Modern Portfolio Theory – asset managers have used computation and

mathematics to model risk and return against their portfolios. Being able to effectively quantify risk and

return against portfolios and markets has allowed managers to address the daily challenges of money

management with objective information and analysis — both of which have steadily increased in volume,

quality and granularity with the advance of computing power. While the application of technology to

portfolio management and asset allocation has helped drive the greatest accumulation of investment

assets in history, it has also had unintended consequences, effectively creating a kind of self-

perpetuating technological arms race that has been blamed for exacerbating the financial crisis.

Portfolio Allocation and Modelling

• Ever since the advent of Modern Portfolio Theory – asset managers have exploited computation

science and mathematics to model risk and return against their portfolios with proactive scenario

analysis and “what if” capabilities. Being so able to effectively quantify risk and return against

portfolios and markets has allowed managers to more effectively address the daily challenges of

money management with objective information and analysis – which in turn have steadily

increased in volume, quality and granularity with the advance of computing power. While the

application of technology to portfolio management and asset allocation has helped drive the

greatest accumulation of investment assets in history – it also has unintended consequences –

effectively creating a kind of self-perpetuating technological arms race which has been blamed

for both accelerating the pace and exacerbating the depth of the 2008 financial crisis.

• Although today’s risk managers enjoy computational tools with unprecedented power at low

costs, they must also navigate an ever-expanding investment universe as new emerging

markets enter the investment mainstream and new types of securities are created. As a result,

investors are confronting the challenges of comprehensively modelling portfolios and markets in

the face of dramatic increases in the scope, detail and timeliness of financial data.

• Accommodating all of these inputs demands ever increasing computational power, which in turn

leads to the further proliferation of data, markets and security types. While computational

capacity grows in line with Moore’s Law, the billions of possible scenarios in the investment

universe may expand at an even faster rate.

Portfolio Allocation and Modelling

• Perhaps even more important than advances in raw computing power are networks that

increase productivity through the global linking of workstations and the interoperability of

software, investment models and strategies. Thanks to the integration of capital markets

and the increased international regulatory cooperation, trading practices and software, a

risk assessment or alpha investment project that works in one market can rapidly be

adapted and deployed in another. With digital networks obliterating many traditional

geographical barriers, teams can exchange lessons learned, adapt strategies, rewrite code

and evolve models in a manner that was impossible even only a few years ago.

• This explosion of network-centric activity means that many asset owners and managers

will forge ahead with investments in IT infrastructure that can accommodate increasing

complexity. An illustration of this challenge can be found in technology-driven solutions to

some of the most important challenges of contemporary global asset management —

market crowding, pricing inefficiencies, risk and rebalancing. The solutions to these

problems are predicated on the notion that the effective application of computing power to

risk modelling and operational efficiency can be almost as important to portfolio

performance as the return characteristics of the underlying asset classes and investments

themselves. Of course, the technology must be used by financial professionals who

understand how best to apply it. Even the best tools in the wrong hands will not lead to

optimal outcomes - and so, the investment technology arms race moves steadily forward.

Enterprise Risk Management

“No human action happens by pure chance unconnected with other happenings. None is incapable of explanation”

• Dr. Hans Gross (Criminologist) •


• Risk Management is a structured approach to managing uncertainty through foresight and planning. A risk is related to a specific threat (or group of related threats) managed through a sequence of activities using various resources: -

Risk Research – Risk Identification – Scenario Planning & Impact Analysis – Risk Assessment – Risk Prioritization – Risk Management Strategies – Risk Planning –

Risk Mitigation

• Risk Management strategies may include: - – Transferring the risk to another party

– Avoiding the risk

– Reducing the negative effect of the risk

– Accepting part or all of the consequences of a particular risk .

• For any given set of Risk Management Scenarios, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurrence to be handled first – and those risks with a lower probability of occurrence and lower consequential losses are then handled subsequently in descending order of impact. In practice this prioritization can be challenging. Comparing and balancing the overall threat of risks with a high probability of occurrence but lower loss -versus risks with higher potential loss but lower probability of occurrence -can often be misleading.

Enterprise Risk Management • Scenario Panning and Impact Analysis: - In any Opportunity / Threat Assessment

Scenario, a prioritization process ranks those risks with the greatest potential loss and the greatest probability of occurring to be handled first - subsequent risks with lower probability of occurrence and lower consequential losses are then handled in descending order. As a foresight concept, Wild Card or Black Swan events refer to those events which have a low probability of occurrence - but an inordinately high impact when they do occur.

– Risk Assessment and Horizon Scanning have become key tools in policy making and strategic planning for many governments and global enterprises. We are now moving into a period of time impacted by unprecedented and accelerating transformation by rapidly evolving catalysts and agents of change in a world of increasingly uncertain, complex and interwoven global events.

– Scenario Planning and Impact Analysis have served us well as a strategic planning tools for the last 15 years or so - but there are also limitations to this technique in this period of unprecedented complexity and change. In support of Scenario Planning and Impact Analysis new approaches have to be explored and integrated into our risk management and strategic planning processes.

• Back-casting and Back-sight: - “Wild Card” or “Black Swan” events are ultra-extreme manifestations with a very low probability of occurrence - but an inordinately high impact when they do occur. In any post-apocalyptic “Black Swan Event” Scenario Analysis, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies – with a view to identifying those Weak Signals which may have predicated subsequent appearances of unexpected Wild Card or Black Swan events.

Risk Clusters and Connectivity

A

B

C

D

E

G

H

F

The above is an illustration of risks relationships - how risks might be connected. Any detailed and

intimate understanding of the connection between risks may help us to answer questions such as: -

• If risk A occurs does it make risk B or H more or less likely to occur ?

• If risk B occurs what effect does it have on Risks C,D,E, F and G ?

Answering questions such as these allows us to plan our risk management approach and mitigation

strategy – and to decide how better to focus our risk resources and effort

Risk Clusters and Connectivity

• Aggregated risk includes coincident, related, connected or interconnected risk: -

• Coincident - two or more risks appear simultaneously in the same domain–

but they arise from different triggers (unrelated causal events)

• Related - two more risks materialise in the same domain sharing common

risk features or characteristics (may share a possible hidden common trigger

or cause – and so are candidates for further analysis and investigation)

• Connected - two more risks materialise in the same domain due to the

same trigger (common cause)

• Interconnected - two more risks materialise together in a risk series - due to

the previous (prior) risk event triggering the subsequent (next) risk event

• Aggregated risks may result in large impacts and are therefore frequently defined

incorrectly defined as Black Swans

Aggregated Risk

A Trigger A

Coincident Risk

B Trigger B

Risk Event

Risk Event

C Trigger

Related Risk

D Trigger

Risk Event

Risk Event

E

Trigger

Connected Risk

Risk Event

Risk Event F

G Trigger

Inter-connected Risk

Risk Event

Risk Event

H

Risk Management Frameworks

Standard (Integrated) Risk Framework

• Eltville Model / Future Management Frameworks

• Systemic (external) Risk – Outsights

• Operational (internal) Risk – CLAS, SOX / COBIT

• Market (macro-economic) Risk – COSO, Basle II / Solvency II, BoE / FSA

• Trade (micro-economic) Risk – COSO, SOX / COBIT, GAAP / IFRS

Event Risk

• Event Risk is the threat of loss from unexpected events. Event Risk measurement systems seek to quantify the

actual or potential (realised or unrealised) exposure of the total asset portfolio to unexpected Wild Card or Black

Swan Events. Event Risk may arise from Systemic (external) sources – such as Natural Disaster, Geo-political

Crisis, or the collapse of Local, Regional or Global Markets or the failure of Sovereign Nation States - or Operational

(internal) sources – such as Rogue Trading or the failure of Compliance or Disclosure systems and processes.

Market Risk

• Market Risk is the threat of loss from movements in the level or volatility of Market Prices – such as interest rates,

foreign currencies, equities and commodities. Market Risk measurement systems seek to quantify the actual or

potential (realised or unrealised) exposure of the total asset portfolio

Trade Risk

• Trade Risk is the threat of loss from erosion in the attractiveness, desirability or value of specific traded instruments

from individual counterparties – including contracts for foreign currencies, equities and commodities. Trade Risk

measurement systems seek to quantify the actual or potential (realised or unrealised) value of specific contracts or

traded instruments, Trade Risk does not cover Incremental Risk Capital Charge (IRC) due to Toxic Asset lock-in.


Credit Risk

• Credit Risk is the threat of loss from changes in the status or liquidity of individual external debtors – changes in their

ability to service debts due to movement in their credit status, capitalisation, liquidity or solvency – or their exposure

to consequential losses due to statutory, regulatory or legal action. Credit Risk measurement systems seek to

quantify the actual or potential (realised / unrealised) ability of a Creditor to fulfil their contractual obligations.

Liquidity Risk – Solvency II and Basle II

• Liquidity Risk is the threat of loss from changes in the status or liquidity of an organisation –changes in their ability to

service debts due to internal movement in their credit status, capitalisation, liquidity or solvency – or their exposure to

consequential losses due to external statutory, regulatory or legal action. Liquidity Risk measurement systems seek to

quantify actual or potential (realised / unrealised) ability of a Bank or Insurer to meet provided / exposed liabilities.

• Basle II and Solvency II are Rules-based, Quantitative Risk Frameworks. The overhaul of the capital adequacy and

solvency rules is now well under way for European Financial Services - Banking and insurance - Life and Pensions,

General Insurers, Underwriters and Re-insurers -. Key drivers for Basle II and Solvency II include the following: -

• Key drivers for Basle II and Solvency II: -

• – EC directive around capital adequacy of Financial Services Companies

• – Critical requirement to bolster capital and strengthen balance sheets

• – Need to have reporting systems in place to demonstrate compliance

• – Deadline is Q4 2010 – so aggressive timeline for implementation

• – Fines and imprisonment for non-compliance or non-disclosure

• – Major insurance companies will invest £100m + in Compliance Programmes

• – Strategy, Business Process, Architecture and Technology changes

• – Specialisations include compliance, risk, finance, actuarial science


• Systemic Risk (external threats) - Eltville Model, Future Management Framework, Outsights

– Political Risk – Political Science, Futures Studies and Strategic Foresight

– Economic Risk – Fiscal Policy, Economic Analysis, Modelling and Forecasting

– Social Risk – Population Growth and Migration, Futures Studies and Strategic Foresight

– Environmental Risk – Climate Change, Environmental Analysis, Modelling and Forecasting

– Event Risk – exposure to unexpected local, regional or global events

• Wild Card Events – Horizon Scanning, Tracking and Monitoring – Weak Signals

• Black Swan Events – Scenario Planning and Impact Analysis – Future Management

• Market Risk (macro-economic threats) - COSO, Basle II / Solvency II, BoE / FSA

– Equity Risk – Traded Instrument Product Analysis, Valuation and Financial Management

– Currency Risk – FX Curves and Forecasting

– Commodity Risk – Price Curves and Forecasting

– Interest Rate Risk – Interest Rate Curves and Forecasting

• Trade Risk (micro-economic threats) - COSO, Basle II / Solvency II, BoE / FSA

– Credit Risk – Credit Rating, Balanced Scorecard, Debtor Forecasting and Analysis

– Contract Risk – Asset Valuation, Credit Default Propensity Modelling

– Liquidity Risk – Solvency and Capital Adequacy Rules (Solvency II / Basle II)

– Insurance Risk – Underwriting Due Diligence and Compliance

– Actuarial Risk – Geo-demographic profiling and Morbidity Analysis

– Counter-Party Risk – Counter-Party Threat Analysis and Risk Management

– Fraud Risk (Rogue Trading) – Real-time Analytics at Point-of-Contract-Execution


• Operational Risk (internal threats) - CLAS, SOX / COBIT

– Legal Risk – Contractual Law Due Diligence and Compliance

– Statutory Risk – Legislative Due Diligence and Compliance

– Regulatory Risk – Regulatory Due Diligence and Compliance

– Competitor Risk – Competitor Analysis, Defection Detection and Churn Management

– Reputational Risk – Internet Content Scanning, Intervention and Threat Management

• Business Risk

– Process Risk – Business Strategy / Architecture, Enterprise Target Operating Model (eTOM) / Business

Process Management (BPM) Verification /Validation

– Stakeholder Risk – Benefits Realisation Strategy and Communications Management

– Information Risk – Information Strategy and Architecture, Data Quality Management

– Disclosure Risk – Enterprise Governance, Reporting and Controls (SOX / COBIT)

• Digital Communications and Technology Risk

– Technology Risk – Technology Strategy and Architecture

– Security Risk – Security Principles, Policies, Architecture and Models (CLAS)

– Vendor / 3rd Party Risk – Strategic Vendor Analysis and Supply Chain Management

Trading and Risk Management

• MARKET RISK •

Market Risk = Market Sentiment – Actual Results (Reality)

• The two Mood States – “Greed and Fear” are primitive human instincts which, until now,

we've struggled to accurately qualify and quantify. Social Networks, such as Twitter and

Facebook, burst on to the scene five years ago and have since grown into internet

giants. Facebook has over 900 million active members and Twitter over 250 million, with

users posting over 2 billion "tweets“ or messages every week. This provides hugely

valuable and rich insights into how Market Sentiment and Market Risk are impacting on

Share Support / Resistance Price Levels – and so is also a source of real-time data that

can be “mined” by super-fast computers to forecast changes to Commodity Price Curves

Info-graphic – Apple Historic Stock Data Analysis.....

• Investors and traders around the world have accepted the fact that financial markets are

driven by “greed and fear”. This info-graphic is an example of the kind of correlation we

see between historic stock price and social media sentiment data. A trading advantage

can arrive if you spot a significant change in sentiment which is a leading asset price

indicator. Derwent Capital Markets are pioneers in trading the financial markets using

global sentiment derived from large scale social media analysis.

Market Risk

MARKET RISK = MARKET SENTIMENT – ACTUAL RESULTS (REALITY)

Market Risk

Financial Markets around the world are driven by “greed and fear”.....

Derwent Capital Markets –

Market Risk = Market Sentiment – Actual Results (Reality).....

• Derwent Capital Markets used Twitter to figure out where the money is going - just like that. A hedge

fund that analyzed tweets to figure out where to invest its managed funds closed its doors to new

investors last year – after just one month in which it made 1.86% Profit – Annual Projection 21% reports

the Financial Times. “As a result we made the strategic decision to re-use the Social Market Sentiment

Engine behind the Derwent Absolute Return Fund – and invest directly in developing a Social Media on-

line trading platform” commented Derwent Capital Markets founder Paul Hawtin,

Mood states – “greed and fear”.....

• These two mood states are primitive human instincts which, until now, we've struggled to accurately

quantify. Social networks, such as Twitter and Facebook, burst on to the scene five years ago and have

since grown into internet giants. Facebook has over 900 million active members and Twitter over 250

million, with users posting over 2 billion "tweets“ or messages every week. This provides a hugely

valuable and rich source of real-time data that can be “mined” by super-fast computers.....

• Derwent Capital Markets - the sentiment analysis provider launched by Paul Hawtin in May

2012 following the dissolution of his "Twitter Market Sentiment Fund", sold yesterday to the highest

bidder at the end of a two-week online auction. The winning bid came from a Financial Technology (Fin

Tech) firm, which Hawtin declined to name. Hawtin had set a guide price of £5 million ($7.8m), but

claimed at the start of the auction process that any bid over and above the £350,000 ($543,000) cash

he had invested would represent a successful outcome.....

CFD Trading, Spread Betting and FX Trading using “Big Data”

http://www.waterstechnology.com/buy-side-technology/analysis/2181304/twitter-hedge-fund-eyes-rebirth-dcm-capital





http://www.waterstechnology.com/waters/feature/2119649/industry-eyes-social-media-sentiment-analysis

http://www.waterstechnology.com/buy-side-technology/news/2241367/dcm-capital-goes-under-the-hammer



Event Risk

• EVENT RISK •

Black Swan Event = extreme event with Low Probability and High Impact

• A 'Black Swan' Event – is an extreme, rare and unexpected occurrence or event,

with low probability and high impact - difficult to forecast or predict, with outcomes and

consequences deviating far beyond the normal expectations for any given situation –

Nassim Nicholas Taleb - Finance Professor, Author and former Wall Street Trader.


• The two Mood States – “Greed and Fear” are primitive human instincts which, until

now, we've struggled to accurately qualify and quantify. Social Networks, such as Twitter

and Facebook, burst on to the scene five years ago and have since grown into internet

giants. Facebook has over 900 million active members and Twitter over 250 million, with

users posting over 2 billion "tweets“ or messages every week. This provides hugely

valuable and rich insights into how Market Sentiment and Market Risk are impacting on

Share Support / Resistance Price Levels – and so is also a source of real-time data that

can be “mined” by super-fast computers to forecast changes to Commodity Price Curves

The Eight Triggers of Disaster

Socio-graphic Risk

Risk

Management

Technology Change

Competition Change

Eco-system Change

Climate Change

Population Change

Culture Change

Economic Change

Political Change

Competitor Risk

Political Risk

Environment Risk

Ecological Risk Technology Risk

Demographic Risk Market Risk

Weak Signals Wild Cards, Black Swans

Wild Card

Strong Signal

Random Event

Weak Signal

Communicate Discover

Understand Evaluate

Random Event

Strong Signal

Weak Signal

Wild Card

Black Swan

Runaway Wild Card Scenario

Stock Market Panic of 2008

Trigger D

USA Sub-Prime Mortgage Crisis

Trigger F

CDO Toxic Asset Crisis

K

E Trigger

K Sovereign

Debt Crisis

B Trigger

I

Money

Supply

Shock

C Trigger

H

Financial

Services

Sector

Collapse

D Trigger

G

L

A Trigger

J

Credit

Crisis

Global

Recession

Black Swan Events

Definition of a “Black Swan” Event

• A “Black Swan” Event is an event or

occurrence that deviates beyond what is

normally expected of any given situation

and that would be extremely difficult to

predict. The term “Black Swan” was

popularised by Nassim Nicholas Taleb, a

finance professor and former Investment

Fund Manager and Wall Street trader.

• Black Swan Events – are unforeseen,

sudden and extreme change events or

Global-level transformations in either the

military, political, social, economic or

environmental landscape. Black Swan

Events are a complete surprise when

they occur and all feature an inordinately

low probability of occurrence - coupled

with an extraordinarily high impact when

they do happen (Nassim Taleb). “Black Swan” Event Cluster or “Storm”

Stock Market

Panic of 2008

Cluster Theory Business Clusters are economic agglomerations of firms - all of which are interconnected

by a common value-chain – co-located within a geographic area which also benefit from

regional access to local concentrations or availability of specific activities,

competencies and resources, such as input/output markets and infrastructure, in a

favourable environment which is coordinated via public and private sector institutions and

policies.

Cluster R&D tends to become more demand driven. Greater competition is

encouraged with a culture of co-operation also being fostered – but driving cluster

productivity is the opportunity for collaboration with co-specialisation amongst the

firms within the Cluster.

Ifor Ffowcs-Williams - CEO, Cluster Navigators Ltd & Author, “Cluster Development”

Cluster Theory – Industry Sectors

• A Business Cluster is a Geographic Location where a local concentration or availability of specific

competencies and resources in a industry sector, develops favourable conditions that reach a critical

concentration or threshold level, sufficient to create a decisive sustainable competitive advantage –

over and above that of other competing locations – and may further evolve into a position of regional

or even global supremacy in that industry sector or competitive field (e.g. Silicon Valley, Hollywood).

• The fundamental concept of Geographical Economic Clusters – to which social geographers and

economists have also referred to as agglomeration economies – is very well documented by Alfred

Marshall in his work of 1890. The term Business Cluster, also known as an Industry Cluster,

Competitive Cluster, or Technology Cluster, was further popularised by Michael Porter in his book

The Competitive Advantage of Nations (1990). The importance of the role of clusters in economic

geography, or more correctly geographical economics, was also brought to the public attention by

Paul Krugman in his book Geography and Trade (1991). Cluster development has since become an

important focus for numerous government infrastructure and regional development programs.

• Michael Porter claims that clusters have the potential to affect competition in three ways – through

increasing the productivity of the companies in the cluster, by driving innovation in the cluster, and by

stimulating new businesses in the cluster. According to Porter, from 1990 onwards in the modern

global economy, comparative advantage – where certain locations enjoy favourable conditions for

example, cheap labour for Manufacturing (China) and harbour, faculties for Mercantilism (Hong Kong

and Singapore) - are becoming less relevant. Today, it is how companies make efficient use of inputs

to stream continuous innovation – that has achieved increased significance for competitive advantage

.

https://en.wikipedia.org/wiki/Sustainable_competitive_advantage

https://en.wikipedia.org/w/index.php?title=Agglomeration_economies&action=edit&redlink=1

https://en.wikipedia.org/wiki/Alfred_Marshall

https://en.wikipedia.org/wiki/Alfred_Marshall

https://en.wikipedia.org/wiki/Michael_Porter

https://en.wikipedia.org/wiki/Paul_Krugman

https://en.wikipedia.org/wiki/Paul_Krugman

https://en.wikipedia.org/wiki/Cluster_development

https://en.wikipedia.org/wiki/Global_economy

https://en.wikipedia.org/wiki/Comparative_advantage

https://en.wikipedia.org/wiki/Innovation

https://en.wikipedia.org/wiki/Competitive_advantage


• Regional Clusters are created by the local availability or concentration of specific competencies and

resources. Cluster Theory states that any Regional Geographic concentration of any specific Industry

Sector may create a number of advantageous local conditions. The first effect is increased competition

– so greater efficiency is encouraged, leading to improved productivity and higher total profits which are

shared between all of the participating firms in that Industry Sector. It is also claimed that Business

Clustering drives increased Research, Development and Business Innovation (Michael Porter).

• Greater competition is encouraged, but also the opportunity for collaboration, and a culture of

co-operation is fostered – with co-specialisation amongst the firms within the cluster driving

productivity. Public R&D tends to become more demand driven – Ifor Ffowcs-Williams..

• Suppliers are attracted to co-locate into the Regional Cluster – thus shortening the Supply Chain and

improving Logistics. The presence of a wide choice of suppliers in the region leads to greater vendor

performance and thus reduced costs for collaborating firms. Those firms with a successful Business

Operating Model also tend to become more competitive, eventually leading to economies of scale being

derived from both vertical and horizontal integration – Business Agglomeration – that is, absorption of

smaller, less efficient competitors, customers and suppliers by expanding industry conglomerates. The

presence of a regional centre of excellence for any Industry Sector also attracts an increasingly Global

customer base seeking reliable Business Partners – this Globalisation effect in turn promotes both local

and inward investment and drives further business expansion and industry sector growth.


• Globalisation and localisation are two sides of the same coin. Merger and Acquisition activity is

healthily enhanced within a strong cluster - but needs to be continually fed by new start-ups and

spin-offs - Ifor Ffowcs-Williams..

• Concentrating related industries together in specific regions also creates greater demand in the local

Labour Market, leading over time to the development of a specialist regional skills base. This may

cause the spin-off of new businesses exploiting the skills available in the labour pool. Increased

employment opportunities also means increased Wages flowing into the Regional Economy and greater

Regional Taxation Revenues - which in turn yields multiple benefits across the region as a whole.

• 'Smart Specialisation' is the term being increasingly used by the European Union. Skills

development is often the main issue facing a high growth cluster - Ifor Ffowcs-Williams.

• Note that a cluster is not artificially confined within a rigid Geographic Area - e.g. Tech City lies both in

and around the boundaries of Shoreditch's. There are related Digital Clusters - e.g. The Science Park

north of Cambridge, and in the Innovation Campus around the BT Laboratories Hub at Adastral Park in

Martlesham Heath, near Ipswich.

Expert Commentator: -

• Ifor Ffowcs-Williams, CEO, Cluster Navigators Ltd and Author, “Cluster Development” – Address : Nelson 7010, New Zealand (Office)




Cluster Definitions

• Clusters are economic agglomerations of firms co-located within a geographic area - all connected by

a common value-chain – which benefit from regional access to local concentrations or availability of

specific activities, competencies and resources, such as input/output markets and infrastructure, in a

favourable environment which is coordinated via public and private sector institutions and policies.

• Clustering is the tendency of vertically and horizontally integrated firms in related lines of business to

concentrate together geographically (OECD, 2001). Clusters are geographically co-located groups of

interconnected companies and institutions which operate together in a specific field or industry sector

and are linked together by a number of common and complementary factors (Michael Porter, 1998).

• Clusters are co-located groups of Business Enterprises, Government Agencies and NGOs for whom a

close association is an important source of individual and collective competitive advantage – using

common factors such as Finance (venture capital) , Procurement (buyer-supplier relationships) and

Distribution (supply chain channels), that exploits shared activities, resources, technologies, skills,

knowledge and labour pools – which binds the cluster closely together (Bergman and Feser, 1999).

• Clusters are networks of strongly interdependent enterprises (customers and suppliers), all linked

together in an integrated production chain, in value-added activities or via business partnerships, (e.g

Automotive and Aerospace sector). In many types of Cluster enterprise relationships also encompass

strategic alliances with Government Agencies, universities, research institutes, bridging institutions and

knowledge providers (i.e. consultants, brokers, business services), (Roelandt / den Hertog, 1999)

NESTA

creative clusters

• NESTA have created the first ever map of the UK's most creative business clusters.

• This definitive work identifies all of the nation's top 'creative hotspots', - areas which host clusters of creative businesses which are promoting technology innovation and driving economic growth across their region.

Moore's Law

Moore's Law

• In 1965, the observation made by Gordon Moore, co-founder of Intel, is that the number

of transistors per square inch on integrated circuits had doubled every year since the integrated

circuit was invented. Moore predicted that this trend would continue for the foreseeable future. In

subsequent years, the pace of change has slowed down somewhat - but Data Storage Density

(gigabytes) has doubled approximately every 18 months - a definition which Moore himself has

blessed. The current definition of Moore's Law, accepted by most experts, including Disruptive

Futurists and Moore himself, is that Computing Power (gigaflops) will double about every two

years. Expect Moore's Law to hold good for at least another generation.....

• A forecast - and a challenge. Gordon Moore’s forecast for the pace of change in silicon

technology innovation - known as Moore's Law - essentially describes the basic business model

for the semiconductor industry. Intel, through investments in technology and manufacturing has

made Moore’s Law a reality. As transistor scale gets ever smaller Intel expects to continue to

deliver on Moore’s prediction well into the foreseeable future by using an entirely new transistor

formula that alleviates wasteful electricity leaks creating more energy-efficient processors.

• Exponential growth that continues today. Continuing Moore's Law means the rate of

progress in the semiconductor industry will far surpass that of nearly all other industries. The

future of Moore’s Law could deliver a magnitude of exponential capability increases, driving a

fundamental shift in computing, networking, storage, and communication devices to handle the

ever-growing digital content and Intel's vision of 15 billion intelligent, connected smart devices.

http://www.webopedia.com/TERM/I/Intel.html

http://www.webopedia.com/TERM/T/transistor.html

http://www.webopedia.com/TERM/I/integrated_circuit_IC.html

Research Philosophies

• This section aims to discuss Research Philosophies in detail, in order to develop a

general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full

Research Design. Denzin and Lincoln (2003) and Kvale (1996) highlight how

different Research Philosophies can result in much tension amongst academics

• When undertaking any research of either a Scientific or Humanistic nature, it is most

important to consider, compare and contrast all of the varied and diverse Research

Philosophies and Paradigms available to the researcher and supervisor - along with

their respective treatment of ontology and epistemology issues.

• Since Research Philosophies and paradigms often describe perceptions, beliefs,

and assumptions about the nature of reality and truth (knowledge of that reality), they

can influence the way in which the research is undertaken, from design through to

outcomes and conclusions – so it is important to understand and discuss these

contrasting aspects in order that approaches congruent to the nature and aims of the

particular inquiry in question, are adopted - and to ensure that researcher and

supervisor biases are understood, exposed, and mitigated.


• James and Vinnicombe (2002) caution that we all have our own inherent

preferences that are likely to shape our research designs and conclusions, Blaikie

(2000) describes these aspects as part of a series of choices that the researcher

has to consider, and demonstrates that this alignment that must connect choices

made back to the original Research Problem. If this is not achieved, then certain

research methods may be adopted which turn out to be incompatible with the

researcher’s stance, and result in the final work being undermined through lack of

coherence and consistency.

• Blaikie (1993) argues that Research Methods aligned to the original Research

Problem are highly relevant to Social Science since the humanistic element

introduces a component of “free wil”’ that adds a complexity beyond those usually

encountered in the natural sciences – whilst others, such as Hatch and Cunliffe

(2006) draw attention to the fact that different paradigms ‘encourage researchers to

study phenomena in different ways’, going on to describe a number of

organisational phenomena from three different perspectives, thus highlighting how

different kinds of knowledge may be derived through observing the same

phenomena from different philosophical viewpoints and perspectives.

Research Methods

• When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,


• Blaikie (1993) also describes the root definition of ontology as “the science or

study of being” and develops this existential description for the social sciences to

encompass “claims about what exists, what it looks like, what units it is made up

of, and how these units interact with each other”. In short, ontology describes

our own personal world view (whether as claims or assumptions) of the nature of

reality, and specifically - is this an objective reality that actually exists - or simply

a self created subjective reality, which exists only within our own minds..... ?

• All of us nurture our own deeply embedded and strongly held ontological views

and assumptions – which tends to affect whether we attribute the phenomenon

of existence to one set of things over another set of different things. Such views

and assumptions may also impact on what we perceive to be real – against what

truly does exist and what actual reality is. If these underlying assumptions and

biases are not identified, considered and mitigated – then the researcher may

take for granted, obscure, explicitly ignore, or even become totally unaware of -

certain critical phenomena or aspects of the inquiry. Since those critical aspects

or phenomena are implicitly assumed either to exist or not exist – they are as a

result, no longer open to question, discussion, review - or even consideration.....


• Hatch and Cunliffe (2006) quote both an everyday example, and a social

science example to illustrate this point. For the everyday example, they use

the instance of a workplace report – seeking to question whether the report

describes what is really going on – or only what the author thinks is going on.

• They go on to highlight the complexity which is introduced when considering

any phenomena such as culture, power or control, and whether any given

reality exists – or is simply an illusion. Further extending the discussion as to

how individuals (and groups) determine these realities, Hatch and Cunliffe

pose the following question:

– does any given phenomenon of reality exist only internally and through the subject’s

own thoughts and interpretation of their life and experiences (subjectivism) ?

– or does this specific phenomenon of reality fully and independently exist externally

of the individual as a demonstrable collective belief set or experience common to a

group or community of individuals who collectively perceive, own and live with this

specific reality (objectivism) – which can be documented by external observers ?

Future Management Methods and Techniques

Throughout eternity, all that is of like form comes around

again – everything that is the same must return again in

its own everlasting cycle.....


Primary Futures Research Disciplines

• Futures Studies

– History and Analysis of Prediction

– Future Studies – Classification and Taxonomy

– Future Management Primary Disciplines

– Future Management Secondary Specialisations

• Strategic Foresight

– Foresight Regimes, Frameworks and Paradigms

– Foresight Models, Methods, Tools and Techniques

• Qualitative Techniques

• Quantitative Techniques

• Systems Theory - Complexity

• Chaos Theory – Random Events, Uncertainty and Disruption

• Political and Economic Futures

• Science and Technology Futures

• Entrepreneurship and Innovation Futures

• Personal Futures – Trans-humanism, NLP / EHT

• The Future of Philosophy, Knowledge and Values

• Future Beliefs – Moral, Ethical and Religious Futures

• Massive Change – Human Impact and Global Transformation

• Human Futures – Sociology, Anthropology and Cultural Studies

• The Future of Information, Knowledge Management and Decision Support

DETERMINISTIC versus PROBABILISTIC PARADIGMS

• Utopian (Idealistic) Paradigm - Strategic Positivism

• Humanist (Instructional) Paradigm - Sceptic Futurism

• Dogmatic (Theosophical) Paradigm - Reactionary Futurism

• Utilitarian (Consequential) Paradigm – Egalitarian Futurism

• Extrapolative (Projectionist) Paradigm – Wave, Cycle, Pattern and Trend Analysis

• Steady State (La meme chose - same as it ever was) Paradigm – Constant Futurism

• Hellenistic (Classical) Paradigm – Future of Human Ethics, Morals, Values and Beliefs

• Pre-ordained (Pre-disposed, Stoic) Paradigm - Cognitive Analysis / Intuitive Assimilation

• Elitism (New World Order) - Goal Seeking, Leadership Studies and Stakeholder Analysis

• Existentialist Paradigm (Personal Futures) - Trans-humanism, The Singularity, NLP / EHT

• Empirical (Scientific Determinism, Theoretical Positivism) Paradigm – Hypothetical Futurism

• Predictive (Ordered, Systemic, Mechanistic, Enthalpy) Paradigm – Deconstructionist Futurism

DETERMINISTIC PHILOSOPHIES and FUTURE VIEWPOINTS

DETERMINISTIC versus PROBABILISTIC PARADIGMS

• Polemic (Rational) Paradigm - Enlightened Futurism

• Dystopian (Fatalistic) Paradigm – Probabilistic Negativism

• Postmodernism (Reactionary) Paradigm - Structural Futurism

• Complexity (Constructionist) Paradigm - Complex Systems and Chaos Theory

• Metaphysical (Naturalistic, Evolutionary, Adaptive) Paradigm - Gaia Hypothesis

• Mystic (Gnostic, Sophistic, Esoteric, Cathartic) Paradigm – Contemplative Futurism

• Uncertainty (Random, Chaotic, Disorderly, Enthalpy) Paradigm - Disruptive Futurism

• Experiential (Forensic, Deductive, Realist, “Blue Sky”) Paradigm – Pragmatic Futurism

• Qualitative (Narrative, Reasoned) Paradigm - Scenario Forecasting and Impact Analysis

• Simplexity (Reductionist) Paradigm – Loosely-coupled Linear Systems and Game Theory

• Interpretive (Ordered, Systemic, Mechanistic, Entropic) Paradigm – Constructive Futurism

• Quantitative (Logical, Technical) Paradigm - Mathematical Modelling & Statistical Analysis

PROBABILISTIC PHILOSOPHIES and FUTURE VIEWPOINTS

Secondary Future Specialties

• Monte Carlo Simulation • Forecasting and Foresight • Back-casting and Back-sight • Causal Layered Analysis (CLA) • Complex Adaptive Systems (CAS) • Political Science and Policy Studies • Linear Systems and Game Theory • War-gaming and Lanchester Theory • Complex Systems and Chaos Theory • Integral Studies and Future Thinking • Critical and Evidence-Based Thinking • Predictive Surveys and Delphi Oracle • Visioning, Spontaneity and Creativity • Foresight, Intuition and Pre-cognition • Developmental & Accelerative Studies • Systems & Technology Trends Analysis • Scenario Planning and Impact Analysis • Collaboration, Facilitation & Mentoring

• Black Swan Events - Weak Signals, Wild Cards, Chaos, Uncertainty & Disruption

• Economic Modelling & Planning • Financial Planning and Analysis • Ethics of Emerging Technology Studies • Horizon Scanning, Tracking & Monitoring • Intellectual Property and Knowledge • Critical Futures and Creative Thinking • Emerging Issues and Technology Trends • Patterns, Trends & Extrapolation Analysis • Linear Systems & Random Interactions • Cross Impact Analysis and Factors of

Global Transformation and Change • Preferential Surveys / Polls and Market

Research, Analysis and Prediction • The Future of Religious Beliefs - Theology,

Divinity, Ritual, Ethics and Value Studies • Theosophical, Hermetic, Mystic, Esoteric

and Enlightened Spiritual Practices


• Science and Technology Futures • The Cosmology Revolution

– Dark Energy, Dark Mass – String Theory and the Nature of Matter

• SETI – The Search for Extra-Terrestrial Planetary Systems, Life and Intelligence

• Nano-Technology, Nuclear Physics and Quantum Mechanics

• The Energy Revolution - Nuclear Fusion Hydrolysis and Clean Energy

• Science and Society Futures – the Social Impact of Technology

• Smart Cities of the Future • The Information Revolution – Internet

Connectivity and the Future of the Always-on Digitally Connected Society

• Digital Connectivity, Smart Devices, the Smart Grid & Cloud Computing Futures

• Content Analysis (“Big Data”) – Data Set “mashing”, Data Mining & Analytics

• Earth and Life Sciences – the Future of Biology, Geology & Geographic Science

• Environmental Sustainability Studies – Climatology, Ecology and Geography

• Human Activity – Climate Change and Future Environmental Degradation – Desertification and De-forestation

• Human Populations - Profiling, Analysis, Streaming and Segmentation

• Human Futures - Population Drift and Urbanisation - Human Population Curves and Growth Limit Analysis

• The Future of Agriculture, Forestry, Fisheries, Agronomy & Food Production

• Terrain Mapping and Land Use – Future of Topology, Topography & Cartography

• Future Natural Landscape Planning, Environmental Modelling and Mapping

• Future Geographic Information Systems, Spatial Analysis & Sub-surface Modelling


• Macro-Economic and Financial Futures • Micro-Economic and Business Futures • Strategic Visioning – Possible, Probable &

Alternative Futures • Strategy Design – Vision, Mission and

Strategy Themes • Strategy Development – Outcomes, Goals

and Objectives • Performance Management – Target Setting

and Action Planning • Critical Success Factors (CSF’s) and Key

Performance indicators (KPI’s) • Business Process Management (BPM) • Balanced Scorecard Method • Planning and Strategy

– (foundation, intermediate & advanced)

• Modelling and Forecasting – (foundation, intermediate & advanced)

• Threat Assessment & Risk Management – (foundation, intermediate & advanced)

• Layers of Power, Trust and Reputation • Leadership Studies, Goal-seeking and

Stakeholder Analysis • Military Science, Peace and Conflict

Studies – War, Terrorism and Insecurity • Corporate Finance and Strategic

Investment Planning Futures • Management Science and Business

Administration Futures • Future Management and Analysis of Global

Exploitation of Natural Resources • Social Networks and Connectivity • Consumerism and the rise of the new

Middle Classes • The BRICs and emerging powers

– • Brazil • Russia • India • China • • The Seven Waves of Globalisation

– • Goods • People • Capital • Services – • Ideology • Economic Control •

– • Geo-Political Domination •


• Human Values, Ethics and Beliefs • History, Culture and Human Identity • Human Geography & Industrial Futures • Human Factors and Behavioural Theory • Anthropology, Sociology and Factors of

Cultural Change • Human Rites, Rituals and Customs - the

Future of Cults, Sects and Tribalism • Ethnographic and Demographic Futures • Epidemiology, Morbidity and Actuarial

Science Futures • Infrastructure Strategy, Regional Master

Planning and Urban Renewal • Future Townscape Envisioning. Planning

Modelling and Virtual Terrain Mapping • The Future of Urban and Infrastructure

Master Planning, Zoning and Control • Architecture and Design Futures - living

in the Built Environment of the Future

• Trans-humanism – The Future Human State – Qualities, Capabilities, Capacities

• The Future of Medical Science, Bio-Technology and Genetic Engineering

• The Future of the Human Condition - Health, Wealth and Wellbeing

• The Future of Biomechanics, Elite Sports and Professional Athletics

• Personal Futures – Motivational Studies, Life Coaching and Personal Training

• Positive Thinking – Self-Awareness, Self-Improvement & Personal Development

• Positive Behavioural Psychology and Cognitive Therapy - NLP and EHT

• Intuitive Assimilation and Cognitive Analysis

• Predictive Envisioning and Foresight Development

• Contemplative Mediation and Psychic Methods

• Divination, Lexicology, Numerology and Theological Methods


• Business Strategy, Transformation and Programme Management Futures

• Next Generation Enterprises (NGE) – Envisioning, Planning and Modelling

• Multi-tier Collaborative Future Business Target Operating Models (eTOM)

• Corporate Responsibility / Triple Bottom Line Management

• Regulatory Compliance - Enterprise Governance, Reporting and Controls

• Future Economic Modelling, Long-range Forecasting and Financial Analysis

• The Future of Organisational Theory and Operational Analysis

• Business Innovation and Product Planning Futures

• Technology Innovation and Product Design Futures

• Product Engineering and Production Planning Futures

• Enterprise Resource Planning and Production Management Futures

• Marketing Needs Analysis, Propositions and Product Life-cycle Management

• The Future of Marketing Services, Communications and Advertising

• The Future of Media, Entertainment and Multi-channel Communications

• The Future of Leisure, Travel & Tourism – Culture, Restaurants and Entertainment

• The Future of Spectator Events - Elite Team Sports and Professional Athletics

• The Future of Art, Literature and Music • The Future of Performance Arts, Theatre

and the Moving Image • Science Fiction & Images of the Future • Interpreting Folklore, Legends & Myths –

Theology, Numerology & Lexicography • Utopian and Dystopian Literature, Film

and Arts

Research Philosophies and Investigative Methods

Qualitative and Quantitative Investigative Methods

Qualitative Methods: –

tend to be deterministic, interpretive and subjective in nature.

Quantitative Methods: –

tend to be probabilistic, analytic and objective in nature.....

Qualitative and Quantitative Methods

Research Study Roles and Responsibilities

• Supervisor – authorises and directs the Research Study.

• Project Manager – plans and leads the Research Study.

• Moderator – reviews and mentors the Research Study.

• Researcher – undertakes the detailed Research Tasks.

• Research Aggregator – examines hundreds of related Research

papers - looking for hidden or missed Findings and Extrapolations.

• Author – compiles, documents and edits the Research Findings.

The Temporal Wave


of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)


temporal information in today's data-rich landscape – are becoming increasingly difficult to



attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the


of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Linear and Non-linear Systems

Linear Systems – all system outputs are directly and proportionally related to system inputs

• Types of linear algebraic function behaviours; examples of Simple Systems include: -

– Game Theory and Lanchester Theory

– Civilisations and SIM City Games

– Drake Equation (SETI) for Galactic Civilisations

Non-linear Systems – system outputs are asymmetric and not proportional or related to inputs

• Types of non-linear algebraic function behaviours: examples of Complex / Chaotic Systems are: -

– Complex Systems – large numbers of elements with both symmetric and asymmetric relationships

– Complex Adaptive Systems (CAS) – co-dependency and co-evolution with external systems

– Multi-stability – alternates between multiple exclusive states.(lift status = going up, down, static)

– Chaotic Systems

• Classical chaos – the behaviour of a chaotic system cannot be predicted.

• A-periodic oscillations – functions that do not repeat values after a certain period (# of cycles)

– Solitons – self-reinforcing solitary waves - due to feedback by forces within the same system

– Amplitude death – any oscillations present in the system cease after a certain period (# of cycles)

due to feedback by forces in the same system - or some kind of interaction with external systems.

– Navis-Stokes Equation for the motion of a fluid: -

• Weather Forecasting

• Plate Tectonics and Continental Drift

http://en.wikipedia.org/wiki/Classical_chaos




http://en.wikipedia.org/wiki/Soliton

http://en.wikipedia.org/wiki/Amplitude_death



Qualitative Methods - tend to be deterministic, interpretive and subjective in nature. • When we wish to design a research project to investigate large volumes of unstructured data

producing and analysing graphical image and text data sets with a very large sample or set of information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity - what people think or feel about the world - enters into the scope (e.g. discovering Market Sentiment via Social Media postings), then the adoption of a qualitative research method is vital. If your aim is to understand and interpret people’s subjective experience and the broad range of meanings that attach to it, then interviewing, observation and surveying a range of non-numerical data (which may be textual, visual, aural) are key strategies you will consider. Research approaches such as using focus groups, producing case studies, undertaking narrative or content analysis, participant observation and ethnographic research are all important qualitative methods. You will also want to understand the relationship of qualitative data to numerical research. Any qualitative methods pose their own problems with ensuring the research produces valid and reliable results (see also: Analytics - Working with “Big Data”).

Quantitative Methods - tend to be probabilistic, analytic and objective in nature. • When we want to design a research project to tests a hypothesis objectively by capturing and

analysing numerical data sets with a large sample or set of information – then the quantitative method is preferred. There are many key issues to consider when you are designing an experiment or other research project using quantitative methods, such as randomisation and sampling. Also, quantitative research uses mathematical and statistical means extensively to produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).

Qualitative (Narrative) Analysis

• Qualitative (Narrative) Analysis may involve the further processing of summarised

results generated by Quantitative (Technical) Analysis - using “Big Data” super sets

aggregated from many thousands of discrete, individual data sets. Methods such as

Monte Carlo Simulation – cycle model runs repeatedly through thousands of iterations –

minutely varying the starting conditions for every individual cycle run.

– Climate Forecasting – Global or Continental summarised weather cell data sets

– Weather Forecasting – Regional or Local detailed weather cell data sets

– Fiscal Output and Performance Forecasting - macro-economic data sets

– Industrial Output and Performance Forecasting - micro-economic data sets

– Market Sentiment Movement Forecasting – social media content data sets

– Commodity Price Curve Forecasting – Market Data - commodity price data sets

• Results appear as a scatter diagram consisting of thousands of individual points, for

example, commodity prices over a given time line. Instead of a random distribution – we

discover clusters of closely related results against a background of scattered outliers.

Each of these clusters represents a Scenario – which is analysed using Cluster Analysis

methods - Causal Layer Analysis (CLA), Scenario Planning and Impact Analysis– where

numeric results are explained as a narrative story about a possible future outcome –

along with the probability of that scenario materialising.


TECHNICAL (QUANTITATIVE) METHODS TECHNICAL (QUANTITATIVE) METHODS (cont.)

Asymptotic Methods and Perturbation Theory Statistical Arbitrage

“Big Data” - Statistical analysis of very large scale (VLS) datasets Technical (Quant) Analysis

Capital Adequacy – Liquidity Risk Modelling – Basle / Solvency II Trading Strategies - neutral, HFT, pairs, macro; derivatives;

Convex analysis Trade Risk Modelling: – Risk = Market Sentiment – Actual Results

Credit Risk Modelling (PD, LGD) Value-at-Risk (VaR)

Data Audit, Data Profiling. Data Mining and CHAID Analysis Volatility modelling (ARMA, GARCH)

Derivatives (vanilla and exotics)

Dynamic systems behaviour and bifurcation theory NARRATIVE (QUALITATIVE) METHODS

Dynamic systems complexity mapping and network reduction

Differential equations (stochastic, parabolic) “Big Data” Clustering – Clinical Trials, Epidemiology, Morbidity and Actuarial Science

Extreme value theory Business Strategy, Planning, Forecasting Simulation and Consolidation

Economic Growth / Recession Patterns (Boom / Bust Cycles) Causal Layer Analysis (CLA)

Economic Planning and Long-range Forecasting Chaos Theory

Economic Wave and Business Cycle Analysis Cluster Theory

Financial econometrics (economic factors and macro models) Complexity Theory

Financial time series analysis Complex (non-linear) Systems

Game Theory and Lanchester Theory – linear systems Complex Adaptive Systems (CAS)

Integral equations – non-linear systems Computational Theory (Turing)

Interest rates derivatives Delphi Oracle /Expert Panel / Social Media Survey

Ordered (Linear) Systems (simple linear multi-factor equations) Economic Wave Theory – Business Cycles (Austrian School)

Market Risk Modelling (Greeks; Value at Risk - VaR) Fisher-Pry Analysis and Gomperttz Analysis

Markov Processes Forensic “Big Data” – Social Mapping and Fraud Detection

Monte Carlo Simulations and Cluster Analysis Geo-demographic Profiling and Cluster Analysis

Non-linear (quadratic) equations Horizon Scanning, Monitoring and Tracking

Neural networks, Machine Learning and Computerised Trading Information Theory (Shannon)

Numerical analysis & computational methods Monetary Theory – Money Supply (Neo-liberal and Neo-classical)

Optimal Goal-seeking, System Control and Optimisation Scenario Planning and Impact Analysis

Options pricing (Black-Scholes; binomial tree; extensions) Social Media – market sentiment forecasting and analysis

Price Curves – Support / Resistance Price Levels - micro models Value Chain Analysis – Wealth Creation and Consumption

Quantitative (Technical) Analysis Wave-form Analytics, Pattern, Cycle and Trend Analysis

Statistical Analysis and Graph Theory Weak Signals, Wild Cards and Black Swan Event Forecasting

Quantitative (Technical) Analysis

• Quantitative (Technical) Analysis in Economics involves studying detailed micro-economic models which process vast quantities of Market Data (commodity price data sets). This method utilises a form of historic data analysis technique which smoothes or profiles market trends into more predictable short-term price curves - which will vary over time within a specific market.

• Quantitative (Technical) Analysts can initiate specific market responses when prices reach support and resistance levels – via manual information feeds to human Traders or by tripping buying or selling triggers where autonomous Computer Trading is deployed. Technical Analysis is data-driven (experiential), not model-driven (empirical) because our current economic models do not support the observed market data. The key to both approaches, however, is in identifying, analysing, and anticipating subtle changes in the average direction of movement for Price Curves – which in turn reflect relatively short-term Market Trends.


• The design of a research study begins with the selection of a topic and a paradigm. A

paradigm is essentially a worldview, a whole framework of beliefs, values and methods

within which research takes place. It is this world view within which researchers work.

• According to Cresswell (1994) "A qualitative study is defined as an inquiry process of

understanding a social or human problem, based on building a complex, holistic picture,

formed with words, reporting detailed views of informants, and conducted in a natural setting.

• Alternatively a quantitative study, consistent with the quantitative paradigm, is an inquiry into

a social or human problem, based on testing a theory composed of variables, measured with

numbers, and analyzed with statistical procedures, in order to determine whether the

predictive generalizations of the theory hold true.“

• The paradigm framework is made up of: -

– Philosophy

– Paradigms

– Ontology

– Epistemology

– Methodology

• (Source: University of Sheffield)

Qualitative and Quantitative Paradigms

• Qualitative and quantitative Paradigms are rooted in philosophical and scientific traditions with different epistemological and ontological assumptions.

• Philosophy - Fundamental principles about the nature of knowledge and existence which are derived from a shared world view or common belief system that belong to a readily identified philosophical or scientific school or community.

• Paradigms - Collections or sets of frameworks, models, methods, techniques which provide guidelines to a group of researchers within that community as to how they should act with regard to organising the research study or inquiry

• Ontology - concerns the philosophy of existence and the assumptions and beliefs that we hold about the nature of knowledge, being and existence.

• Epistemology - is the theory of knowledge and the assumptions and beliefs that we have about the nature of knowledge. How do we know and understand the world? What is the relationship between the inquirer and the known?

• Methodology - how we gain knowledge about the world or "an articulated, theoretically informed approach to the production of data" (Ellen, 1984, p. 9).


Key Distinctions between Qualitative and Quantitative Research

• A summary of the Quantitative POEM (Scientific, Empiric) philosophy might be: -

– Philosophy – Scientific Empiricism

– Paradigms – Theories / Hypotheses, Mathematical Frameworks / Structures / Equations

– Ontology – Laws of nature

– Epistemology – Measurable “evidence” and observable “proof”

– Methodology – Experiment, large scale data collection, quantitative analysis

• A summary of the Qualitative POEM (Humanistic / Post Modern / Narrative) philosophy might be summarized as follows: -

– Philosophy – Humanistic / Post Modern / Narrative

– Paradigms – Homocentric reality as a social construct, contextual verities

– Ontology – The nature of the psyche, of perception, creativity, intelligence

– Epistemology – Self-verified evidence, grounded theory, recorded testimony

– Methodology – Phenomenology, ethnography, in-depth interviews


1. Words and numbers

• Qualitative (Narrative) Analysis research projects place emphasis on material

understanding through looking closely at people's words, actions and records.

The traditional scientific approach to research (quantifies) the results (collected

data) from these observations. The scientific or Quantitative (Technical)

Analysis approach to research is to look past these words, actions and records

– fundamentally towards the data and its intrinsic mathematical significance.

2. Proof versus Discovery

• The traditional scientific or Quantitative (Technical) Analysis research projects

approach is to discover data relationships (cycles, patterns and trends) to either

Prove (or Disprove.....) a Hypothesis. The goal of a Qualitative (Narrative)

Analysis research projects is to discover data affinities (clusters or patterns) in

the Data which emerge after close observation, thoughtful analysis and careful

documentation of the research content. Qualitative (Narrative) Analysis

discovers contextual findings - not sweeping generalizations. This process of

discovery is basic to the philosophical underpinning of the qualitative approach.


3. Content Analysis - Patterns in the Data versus Meaningful views

• Quantitative (Technical) Analysis research projects examine the patterns

which emerge from the data and to present those patterns for others to inspect.

The task of the Qualitative (Narrative) researcher is to find meanings within the

context of those words (and actions) – which are often presented in the subjects

or participants' own words - whilst at the same time staying as close possible to

the construction of the world as the subjects and participants originally provided.

4. Deterministic (Subjective) versus Probabilistic (Objective) views

• Qualitative (Narrative) research projects may be outcome-driven – that is, to

start out with some fixed ideas or deterministic viewpoint of the desired broad

outcomes, goals and objectives of the Project In contrast Quantitative

(Technical) research projects are data-driven – using Analytic techniques to

look for and discover patterns and trends which might emerge from the data –

often presented as Clusters in the Data. The role of the Quantitative researcher,

therefore, is to discover hidden or unseen Patterns and Trends within large

datasets and to present them for others to inspect, analyse, verify and validate.


Definitions of Qualitative Research

Denzin and Lincoln (1994) define qualitative research:

• Qualitative research is multi-method in focus, involving an interpretive,

naturalistic approach to its subject matter. This means that qualitative

researchers study things in their natural settings, attempting to make sense

of or interpret phenomena in terms of the meanings people bring to them.

Qualitative research involves the studied use and collection of a variety of

empirical materials case study, personal experience, introspective, life story

interview, observational, historical, interactional, and visual texts-that

describe routine and problematic moments and meaning in individuals' lives.

Cresswell (1994) defines it as:

• Qualitative research is an inquiry process of understanding based on distinct

methodological traditions of inquiry that explore a social or human problem.

The researcher builds a complex, holistic picture, analyzes words, reports

detailed views of informants, and conducts the study in a natural setting.

Qualitative Methods

Characteristics of Qualitative Research

• An exploratory and Descriptive or Narrative focus

• Emergent Design

• Data Collection in the natural setting

• Emphasis on ‘human-as-instrument’

• Qualitative methods of data collection

• Early and On-going inductive analysis

Cresswell (1994) divides Qualitative Research into five main Qualitative Research Types and identifies the key challenges of each mode of inquiry: -

• The Biography

• Phenomenology

• Grounded Theory

• Ethnography

• Case Study

Qualitative Methods

Challenges of Biography

• The researcher needs to collect extensive information from and about the subject

of the biography.

• The investigator needs to have a clear understanding of historical, contextual

material to position the subject within the larger trends in society or in the culture.

• It takes a keen eye to determine the particular stories, slant, or angle that "works"

in writing a biography and to uncover the "figure under the carpet" (Edel, 1984) that

explains the multilayered context of a life.

• The writer, using an interpretive approach, needs to be able to bring himself or

herself into the narrative.

• A phenomenological study may be challenging to use \because: -

– The researcher requires a solid grounding in the philosophical precepts of

phenomenology.

– The participants in the study need to be carefully chosen to be individuals who

have experienced the phenomenon

• Bracketing personal experiences by the researcher may be difficult: -

– The researcher needs to decide how and in what way his or her personal

experiences will be introduced into the study.

Qualitative Methods

• A grounded Theory Study challenges researchers for the following reasons: -

– The investigator needs to set aside, as much as possible, theoretical ideas

or notions so that the analytic, substantive theory can emerge.

– Despite the evolving, inductive nature of this form of qualitative inquiry, the

researcher must recognize that this is a systematic approach to research

with specific steps in data analysis.

– The researcher faces the difficulty of determining when categories are

saturated or when the theory is sufficiently detailed.

• The researcher needs to recognize that the primary outcome of this study is a

theory with specific components: a central phenomenon, causal conditions,

strategies, conditions and context, and consequences. These are prescribed

categories of information in the theory.


• Ethnographic studies are challenging to use following reasons: -

– The researcher needs to have a solid grounding in cultural anthropology

and the meaning of a socio-cultural system as well as those concepts typically explored by ethnographers – language, custom, ritual, religeon.

• The time and effort needed to collect data and distances travelled are extensive, involving prolonged periods of time spent in the field: -

– In many ethnographies, the narratives are written in a literary, almost

storytelling approach, an approach that may limit the audience for the work and may be challenging for authors accustomed to traditional approaches to writing social and human science research.

– There is a possibility that the researcher will "go native" and be unable to complete the study or be compromised in the study. This is but one issue in the complex array of fieldwork issues facing ethnographers who venture into an unfamiliar cultural group or system.

Qualitative Methods

The Case study poses the following challenges: -

• The researcher must identify his or her case. He or she must decide what

bounded system or domain to study, recognizing that several cases might be

possible candidates for this selection and realizing that either the case itself or

an issue, for which a case or cases are selected to illustrate, is worthy of study.

• The researcher must consider whether to study a single case or compare and

contrast the merits of multiple cases to illustrate a principle. Whilst the study of

more than one case may dilute the overall case analysis; as the more cases that

a researcher studies, the greater the potential for loss of depth in any single

case.

• When a researchers chooses multiple cases, the issue becomes "How many?"-

Typically, the researcher usually chooses to compare no more than four cases.

What often motivates the researcher to consider a large number of cases is the

concept of abstraction and generalisation - terms common in Quantitative

Analysis - but which holds little meaning for most Qualitative researchers.

Qualitative Methods

Qualitative Methods of Data Collection

• People’s words and actions represent the data of qualitative inquiry and this

requires methods that allow the researcher to capture language and

behaviour. The key ways of capturing these are: -

– The collection of relevant documents and other sources

– Observation – both participant and direct

– Audio Recordings

– Video Recordings

– Photographs

– Structured Interviews – one-to-one

– Group Interviews - Workshops

Qualitative Methods - the Interview

The Interview

• The interview is one of the major sources of data collection, and it is also one of

the most difficult ones to get right. In qualitative research the interview is a form of

discourse. According to Mischler (1986) its particular features reflect the

distinctive structure and aims of interviewing, namely, that it is discourse shaped

and organized by asking and answering questions. An interview is a joint

product of what interviewees and interviewers talk about together and how they

talk with each other. The record of an interview that we researchers make and

then use in our work of analysis and interpretation is a representation of that talk.

Interview Probes

• One of the key techniques in good interviewing is the use of investigative probes.

Patton (1990) identifies several types of interview probes:

– detail-oriented probes

– elaboration probes

– clarification probes

– confirmation probes


1. Detail-oriented probes. In our natural conversations we ask each other questions to get more

detail. These types of follow-up questions are designed to fill out the picture of whatever it is

we are trying to understand. We easily ask these questions when we are genuinely curious: -

– Who was with you?

– What was it like being there

– Where did you go then?

– When did this happen in your life?

– How are you going to try to deal with the situation?

2. Elaboration probes. Another type of probe is designed to encourage the interviewee to tell us

more. We indicate our desire to know more by such things as gently nodding our head as the

person talks, softly voicing 'un-huh' every so often, and sometimes by just remaining silent but

attentive. We can also ask for the interviewee to simply continue talking: -

– Tell me more about that.

– Can you give me an example of what you are talking about?

– I think I understand what you mean.

– Talk more about that, will you?

– I'd like to hear you talk more about that.


3. Clarification probes. There are likely to be times in an interview when the interviewer is unsure of

what the interviewee is talking about, what she or he means. In these situations the interviewer can

gently ask for clarification, making sure to communicate that it is the interviewer's difficulty in

understanding and not the fault of the interviewee.

– I'm not sure I understand what you mean by 'hanging out'. Can you help me understand what that

means?

– I'm having trouble understanding the problem you've described. Can you talk a little more about

that?

– I want to make sure I understand what you mean. Would you describe it for me again?

– I'm sorry. I don't quite get. Tell me again, would you?

4. Confirmation probes. There are also likely to be times in an interview when the interviewer has

conflicting information. either from the same interviewee – or between different interviewees. In these

situations the interviewer can seek confirmation - by asking the same question in a different way, .

– Repeating the same question at different times in the interview ?

– Re-phrasing the same question in different ways ?

– Cross-referencing the interviewee about his / her previous responses or information given by

others ?

– Challenging he interviewee about his / her previous responses or information given by others ?

Quantitative Research

Characteristics of Good Quantitative Research

• We begin with defining the scope of the study. The Programme starts with a single

Problem Domain, Idea or Concept that the researcher seeks to understand better -

rather than a causal relationship of variables or a comparison of clusters or groups .

• We use the long-established tradition of scientific inquiry. This means that the

researcher identifies a topic of study, makes observations of the behaviour of the

Problem / Opportunity Domain and records those Observations, then formulates a

hypothesis to explain this behaviour and then designs and executes an experiment

to prove or disprove the hypothesis – by collecting / analysing the experimental data

• The study includes detailed methods, a rigorous approach to data collection, data

analysis, and report writing. The researcher verifies the accuracy of the account of

the process using one of many scientific procedures for validation and verification

• We analyse data using multiple levels of generalisation and abstraction. Often,

reporters present their studies in stages or phases (e.g., the multiple themes that can

be combined into larger perspectives) or layer their analyses from the particular case

to the general case - reflecting all the complexities that exist in real life. The very best

Quantitative studies also engage the reader in a highly lucid discovery, exploration, examination and explanation of the Clusters, Patterns and Trends found in the Data

Qualitative Research

Characteristics of Good Qualitative Research

• We begin with a single focus. The project begins by examining a primary Problem

Domain, Idea or Concept that the researcher seeks to validate, clarify or understand

better - rather than a causal relationship of variables or a comparison of groups .

• Although relationships between variables or or comparisons between groups might

evolve - these emerge later in the study after we have fully scoped (defined) and

documented (described) a single Problem / Opportunity Domain, Idea or Concept

• We use the long-established tradition of narrative inquiry, research and analysis.

This means that the researcher identifies, selects and deploys one or more of the

mainstream Qualitative Research and Analysis frameworks and sets of methods

• The study includes detailed methods, a rigorous approach to data collection, data

analysis, and report writing. This means that the researcher audits the accuracy of

the account using one or more of the many procedures for validation and verification.

• We write persuasively so that the reader experiences participation in the study

"being there.....“. Often, writers present their studies in steps or stages – reflecting

real life complexities (e.g., multiple themes that can be combined into larger stories,

epics and narratives) or layer their analysis from the specific to the general case.

• The very best Qualitative studies engage the reader in a lucid discovery, exploration, examination and explanation of the Themes, Stories and Epics found in the Data

Qualitative Research

REASONS FOR CONDUCTING QUALITATIVE RESEARCH • Given these distinctions and definitions of a qualitative study, why does a

person engage in such a rigorous design? To undertake qualitative research requires a strong commitment to study a problem and demands time and resources. Qualitative research shares good company with the most rigorous quantitative research, and it should not be viewed as an easy substitute for a "statistical" or quantitative study. Qualitative inquiry is for the researcher who is willing to undertake the following: -

• Commit to extensive time in the field. The investigator spends many hours in the field, collects extensive data, and labours over field issues of trying to find locations, gain access, and obtain permissions, rapport, and an "insider" perspective.

• Engage in the complex, time-consuming process of data analysis – the ambitious task of sorting through large amounts of data and reducing them to a few themes or categories. For a multidisciplinary team of Quantitative researchers, this task is usually automated using statistical analysis or analytics software packages and Smart Apps – or at least can be sub-divided and shared; For single Qualitative researchers, it is a lonely, isolated time of struggling with the data. The task is challenging, especially because the database consists of complex texts and images.

Guidelines for Qualitative Research

• The Quantitative Approach is always the default mode for every research project. If a researcher is willing to engage in Qualitative Approach to answer a question or resolve an inquiry in a Subject (Problem / Opportunity) Domain, then the Research Programme moderators and supervisors need to determine with the Research Team whether a strong rationale exists for not choosing a Quantitative Approach, and that there are compelling reasons to undertake either a Qualitative study – or a mixed Quantitative / Qualitative Analysis.

• Write long, narrative and descriptive passages, because the evidence must substantiate all of the claims made in the Project Scope and Description – and because the writer must be able to demonstrate, contrast and compare multiple views and different perspectives. The incorporation of participants quotes and examples to provide multiple perspectives deepens and broadens the study.

• Participation in human and social research (e.g., Big Data, Social Media Analytics) that are new, novel or emerging and do not have firm guidelines or specific procedures – and so are constantly evolving and changing. Subject (Problem / Opportunity) Domains which are generally neither well-known nor well understood complicates communication - how the researcher plans to conduct a study and how others might judge results when the study is complete.

Guidelines for Qualitative Research

GUIDLINES FOR CONDUCTING QUALITATIVE RESEARCH

• Write long, descriptive narrative passages to show how where the evidence

substantiates claims, and the writer needs to demonstrate multiple perspectives.

The study is expanded by incorporation of examples, illustrations and quotes to

provide evidence in support (or to challenge.....) multiple views / perspectives.

• Participate in a form of social and human science research that does not have

firm guidelines or specific procedures and is evolving and changing constantly.

This complicates communicating how the researcher plans to conduct a study

and how moderators and supervisors might judge the study when it is complete.

• If a researcher is willing to engage in qualitative inquiry, then the supervisor

needs to determine whether a strong rationale exists for choosing a qualitative

approach and that there are compelling reasons to undertake a qualitative study.

In this respect Cresswell (1994) offers the following advice:

Reasons for Qualitative Research

1. Firstly - only select a Qualitative Approach to a Research Study when the nature of the

fundamental research question (Problem / Opportunity Domain) cannot be easily resolved

in a Quantitative Approach. In any Qualitative Approach to a Research Study , the

research question often starts with a who, what or where so that initial forays into the topic

describe what is going on. This is in contrast to Quantitative questions that ask a what ,

why or how and look for a comparison of groups (e.g., Is Group 1 better at something than

Group 2) or a relationship between variables, with the intent of establishing an association,

relationship, or cause and effect (e.g., Did Variable X explain what happened to Variable Y)

2. Secondly - choose a Qualitative study because the topic needs to be explored. 'By this, I

mean that variables cannot be easily identified and hypotheses are not available to explain

behaviour of participants or their population of study – so theories need to be developed.

3. Thirdly - use a Qualitative study because of the need to present a detailed view of the

topic. The side angle lens of the distant panoramic shot will not suffice to present answers

to the problem, or the close-up view does not exist.


4. Fourthly - choose a Qualitative approach in order to study individuals in their natural

setting. This involves going out to the setting or field of study, gaining access, and

gathering material. If participants are removed from their setting, it leads to contrived

findings that are out of context.

5. Fifth - select a Qualitative approach because of interest in writing in a literary style; the

writer brings himself or herself into the study, the personal pronoun "I" is used, or perhaps

the writer engages a storytelling form of narration.

6. Sixth - employ a Qualitative study because of sufficient time and resources to spend on

extensive data collection in the field and detailed data analysis of "text" information.

7. Seventh - select a Qualitative approach because audiences are receptive to qualitative

research. This audience might be a graduate adviser or committee, a discipline inclusive of

multiple research methodologies, or publication outlets with editors receptive to qualitative

approaches.


8. Eighth, employ a Qualitative approach to emphasize the researcher's role as

an active learner who can tell the story from the participants' view rather than as an

"expert" who passes judgment on subjects and other participants.

The Sculptor Michelangelo was once asked: -

"How do you create an object of such beauty from a rough piece of stone ?"

His Reply was: -

"I take a block of Marble - and remove everything that is unnecessary....."

9. Ninth, and finally, as it is with Sculpture - so it is with Editing Research Findings. We

should employ a Qualitative approach to Authoring in order to optimise the Writing-

up Process – particularly the ability to Edit and Structure Raw Text from a "Stream of

Data” into clean, crisp, simple, tight and elegant, incisive and intuitive blocks or units

of Text describing Research Scope, Objective, the nature of the Problem, the Method

used, the Findings and Conclusions – carefully choosing structure and content and

crafting it into clear and lucid Chapters, sections, paragraphs and sentences…..

Future Management Methods and Techniques

Throughout eternity, all that is of like form comes around

again – everything that is the same must return again in

its own everlasting cycle.....


Summary

Futures Research Philosophies and Investigative Methods

Qualitative and Quantitative Investigative Methods

Qualitative Methods: –

tend to be deterministic, interpretive and subjective in nature.

Quantitative Methods: –

tend to be probabilistic, analytic and objective in nature.....


Research Study Roles and Responsibilities

• Supervisor – authorises and directs the Futures Research Study.

• Project Manager – plans and leads the Futures Research Study.

• Moderator – reviews and mentors the Futures Research Study.

• Researcher – undertakes the detailed Futures Research Tasks.

• Research Aggregator – examines hundreds of related Research

papers - looking for hidden or missed Findings and Extrapolations.

• Author – compiles, documents and edits the Research Findings.



Qualitative Methods - tend to be deterministic, interpretive and subjective in nature. • When we wish to design a research project to investigate large volumes of unstructured data

producing and analysing graphical image and text data sets with a very large sample or set of information – “Big Data” – then the quantitative method is preferred. As soon as subjectivity - what people think or feel about the world - enters into the scope (e.g. discovering Market Sentiment via Social Media postings), then the adoption of a qualitative research method is vital. If your aim is to understand and interpret people’s subjective experience and the broad range of meanings that attach to it, then interviewing, observation and surveying a range of non-numerical data (which may be textual, visual, aural) are key strategies you will consider. Research approaches such as using focus groups, producing case studies, undertaking narrative or content analysis, participant observation and ethnographic research are all important qualitative methods. You will also want to understand the relationship of qualitative data to numerical research. Any qualitative methods pose their own problems with ensuring the research produces valid and reliable results (see also: Analytics - Working with “Big Data”).

Quantitative Methods - tend to be probabilistic, analytic and objective in nature. • When we want to design a research project to tests a hypothesis objectively by capturing and

analysing numerical data sets with a large sample or set of information – then the quantitative method is preferred. There are many key issues to consider when you are designing an experiment or other research project using quantitative methods, such as randomisation and sampling. Also, quantitative research uses mathematical and statistical means extensively to produce reliable analysis of its results (see also: Cluster Analysis and Wave-form methods).

Futures Research Philosophies

and Investigative Methods • This section aims to discuss Research Philosophies in detail, in order to develop a

general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full

Research Design. Denzin and Lincoln (2003) and Kvale (1996) highlight how

different Research Philosophies can result in much tension amongst stakeholders.

• When undertaking any research of either a Scientific or Humanistic nature, it is most

important to consider, compare and contrast all of the varied and diverse Research

Philosophies and Paradigms available to the researcher and supervisor - along with

their respective treatment of ontology and epistemology issues.

• Since Research Philosophies and paradigms often describe dogma, perceptions,

beliefs and assumptions about the nature of reality and truth (and knowledge of that

reality) - they can radically influence the way in which the research is undertaken,

from design through to outcomes and conclusions. It is important to understand and

discuss these contrasting aspects in order that approaches congruent to the nature

and aims of the particular study or inquiry in question, are adopted - and to ensure

that researcher and supervisor biases are understood, exposed, and mitigated.

Futures Research Methods

• When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,

Weak Signals and Wild Cards

• “Wild Card” or "Black Swan" manifestations are extreme and unexpected events which have a very low probability of occurrence, but an inordinately high impact when they do happen Trend-making and Trend-breaking agents or catalysts of change may predicate, influence or cause wild card events which are very hard - or even impossible - to anticipate, forecast or predict.

• In any chaotic, fast-evolving and highly complex global environment, as is currently developing and unfolding across the world today, the possibility of any such "Wild Card” or "Black Swan" events arising may, nevertheless, be suspected - or even expected. "Weak Signals" are subliminal indicators or signs which may be detected amongst the background noise - that in turn point us towards any "Wild Card” or "Black Swan" random, chaotic, disruptive and / or catastrophic events which may be on the horizon, or just beyond......

• Back-casting and Back-sight: - In any post-apocalyptic Black Swan Event Scenario, we can use Causal Layer Analysis (CLA) techniques in order to analyse and review our Risk Management Strategies to identify those Weak Signals which may have predicted, suggested, pointed towards or indicated subsequent Wild Cards or Black Swan Events – in order to discover changes and improvements to strengthen and improve Enterprise Risk Management Frameworks.


• Scenario Planning and Impact Analysis is the archetypical method for futures studies

because it embodies the central principles of the discipline:

– It is vitally important that we think deeply and creatively about the future, or else we run

the risk of being either unprepared or surprised – or both......

– At the same time, the future is uncertain - so we must prepare for a range of multiple

possible and plausible futures, not just the one we expect to happen.

• Scenarios contain the stories of these multiple futures, from the expected to the

wildcard, in forms that are analytically coherent and imaginatively engaging. A good

scenario grabs us by the collar and says, ‘‘Take a good look at this future. This could be

your future. Are you going to be ready?’’

• As consultants and organizations have come to recognize the value of scenarios, they

have also latched onto one scenario technique – a very good one in fact – as the

default for all their scenario work. That technique is the Royal Dutch Shell/Global

Business Network (GBN) matrix approach, created by Pierre Wack in the 1970s and

popularized by Schwartz (1991) in the Art of the Long View and Van der Heijden (1996)

in Scenarios: The Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the

‘‘gold standard of corporate scenario generation.’’

Outsights "21 Drivers for the 21st Century"

• Scenarios are specially constructed stories about the future - each one portraying

a distinct, challenging and plausible world in which we might one day live and work - and for which we need to anticipate, plan and prepare.

• The Outsights Technique emphasises collaborative scenario building with internal clients and stakeholders. Embedding a new way of thinking about the future in the organisation is essential if full value is to be achieved – a fundamental principle of the “enabling, not dictating” approach

• The Outsights Technique promotes the development and execution of purposeful action plans so that the valuable learning experience from “outside-in” scenario planning enables building profitable business change.

• The Outsights Technique develops scenarios at the geographical level; at the business segment, unit and product level, and for specific threats, risks and challenges facing organisations. Scenarios add value to organisations in many ways: - future management, business strategy, managing change, managing risk and communicating strategy initiatives throughout an organisation.

Outsights "21 Drivers for the 21st Century"

1. War, terrorism and insecurity 2. Layers of power 3. Economic and financial stability 4. BRICs and emerging powers • Brazil • Russia • India • China

5. The Five Flows of Globalisation • Ideas • Goods • People • Capital • Services

6. Intellectual Property and Knowledge 7. Health, Wealth and Wellbeing 8. Transhumanism – Geo-demographics,

Ethnographics and Social Anthropology 9. Population Drift, Migration and Mobility 10. Market Sentiment, Trust and Reputation 11. Human Morals, Ethics, Values and Beliefs

12. History, Culture, Religion and Human Identity 13. Consumerism and the rise of the Middle Classes 14. Social Media, Networks and Connectivity 15. Space - the final frontier

• The Cosmology Revolution - String Theory

16. Science and Technology Futures • The Nano Revolution • The Quantum Revolution • The Information Revolution • The Bio-Technology Revolution • The Energy Revolution • Oil Shale Fracking • • Kerogen • Tar Sands • Methane Hydrate • • The Hydrogen Economy • Nuclear Fusion •

17. Science and Society – the Social Impact of Disruptive Technology and Convergence

18. Natural Resources – availability, scarcity and control – Food, Energy and Water (FEW) crisis

19. Climate Change • Global Massive Change – the Climate Revolution

20. Environmental Degradation & Mass Extinction 21. Urbanisation and the Smart Cities of the Future

At the very Periphery of Corporate Vision and Awareness…..

• The Cosmology Revolution – new and exciting advances in Astrophysics and Cosmology (String Theory and star clustering) is leading Physicists towards new questions and answers concerning the make-up of stellar clusters and galaxies, stellar populations in different types of galaxy, and the relationships between high-stellar populations and local clusters. What are the implications for galactic star-formation histories and relative stellar formation times – overall, resolved and unresolved – and their consequent impact on the evolution of life itself ?.

• The Quantum Revolution – The quantum revolution could turn many ideas of science fiction into science fact - from meta-materials with mind-boggling properties such as invisibility, limitless quantum energy via room temperature superconductors an onwards and upwards to Arthur C Clarke's space elevator. Some scientists even forecast that in the latter half of the century everybody will have a personal fabricator that re-arranges molecules to produce everything from almost anything. How ultimately will we use this gift? Will we have the wisdom to match our mastery of matter like Solomon? Or will we abuse our technology strength and finally bring down the temple around our ears like Samson?

• The Nano-Revolution – Autonomous Fabrication and Construction micro-robots (and also De-Fabrication and De-construction micro-robots) built using novel and exciting nanotechnology meta-materials with strange properties - will roam freely across our Cities creating and maintaining the Built Environment of the Future.


• The Energy Revolution • Oil Shale • Kerogen • Tar Sands • Methane Hydrate • The

Hydrogen Economy • Nuclear Fusion • Every year we consume the quantity of Fossil

Fuel energy which took nature 3 million tears to create. Unsustainable fossil fuel energy

dependency based on Carbon will eventually be replaced by the Hydrogen Economy

and Nuclear Fusion. The conquest of hydrogen technology, the science required to

support a Hydrogen Economy (to free up humanity from energy dependency) and

Nuclear Fusion (to free up explorers from gravity dependency) is the final frontier which,

when crossed, will enable inter-stellar voyages of exploitation across our Galaxy.

• Nuclear Fusion requires the creation and sustained maintenance of the enormous

pressures and temperatures to be found at the Sun’s core This is a most challenging

technology that scientists here on Earth are only now just beginning to explore and

evaluate its extraordinary opportunities. To initiate Nuclear Fusion requires creating the

same conditions right here on Earth that are found the very centre of the Sun. This

means replicating the environment needed to support quantum nuclear processes which

take place at huger temperatures and immense pressures in the Solar core – conditions

extreme enough to overcome the immense nuclear forces which resist the collision and

fusion of two deuterium atoms (heavy hydrogen – one proton and one neutron) to form a

single Helium atom – accompanied by the release of a vast amount of Nuclear energy.


• Renewable Resources • Solar Power • Tidal Power • Hydro-electricity • Wind Power • The Hydrogen Economy • Nuclear Fusion • Any natural resource is a renewable resource if it is replenished by natural processes at a rate compatible with or faster than its rate of consumption by human activity or other natural uses or attrition. Some renewable resources - solar radiation, tides, hydroelectricity, wind – can also classified as perpetual resources, in that they can never be consumed at a rate which is in excess of their long-term availability due to natural processes of perpetual renewal. The term renewable resource also carries the implication of prolonged or perpetual sustainability for the absorption, processing or re-cycling of waste products via natural ecological and environmental processes.

• For the purposes of Nuclear Fission, Thorium may in future replaced enriched Uranium-235. Thorium is much more abundant, far easier to mine, extract and process and far less dangerous than Uranium. Thorium is used extensively in Biomedical procedures, and its radioactive decay products are much more benign.

• Sustainability is a characteristic of a process or mechanism that can be maintained indefinitely at a certain constant level or state – without showing any long-term degradation, decline or collapse.. This concept, in its environmental usage, refers to the potential longevity of vital human ecological support systems - such as the biosphere, ecology, the environment the and man-made systems of industry, agronomy, agriculture, forestry, fisheries - and the planet's climate and natural processes and cycles upon which they all depend.


• Trans-humanism – advocates the ethical use of technology to extend current human form and function - supporting the use of future science and technology to enhance the human genome capabilities and capacities in order to overcome undesirable and unnecessary aspects of the present human condition.

• The Intelligence Revolution – Artificial Intelligence will revolutionise homes, workplaces and lifestyles. Augmented Reality will create new virtual worlds – such as the interior of Volcanoes or Nuclear Reactors, the bottom of the Ocean or the surface of the Moon, Venus or Mars - so realistic they will rival the physical world. Robots with human-level intelligence may finally become a reality, and at the ultimate stage of mastery, we'll even be able to merge human capacities with machine intelligence and attributes – via the man-machine interface.

• The Biotech Revolution – Genome mapping and Genetic Engineering is now bringing doctors and scientists towards first discovery, and then understanding, control, and finally mastery of human health and wellbeing. Digital Healthcare and Genetic Medicine will allow doctors and scientists to positively manage successful patient outcomes – even over diseases previously considered fatal. Genetics and biotechnology promise a future of unprecedented health, wellbeing and longevity. DNA screening could diagnose and gene therapy prevent or cure many diseases. Thanks to laboratory-grown tissues and organs, the human body could be repaired as easily as a car, with spare parts readily available to order. Ultimately, the ageing process itself could ultimately be slowed or even halted.


• Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and dystopian views of the emerging world future state, in which climate, the environment, ecology and even geology are dominated by human manipulation –

1. Human Impact is now the major factor in climate change, environmental and

ecological degradation.

2. Environmental Degradation - man now moves more rock and earth than do all of the natural geological processes

3. Ecological Degradation – biological extinction rate - is currently greater than that of the Permian-Triassic boundary (PTB) extinction event

4. Food, Energy, Water (FEW) Crisis – increasing scarcity of Natural Resource

• Society’s growth-associated impacts on its own ecological and environmental support systems, for example intensive agriculture causing exhaustion of natural resources by the Mayan and Khmer cultures, de-forestation and over-grazing causing catastrophic ecological damage and resulting in climatic change – for example, the Easter Island culture, the de-population of upland moors and highlands in Britain from the Iron Age onwards – including the Iron Age retreat from northern and southern English uplands, the Scottish Highland Clearances and replacement of subsistence crofting by deer and grouse for hunting and sheep for wool on major Scottish Highland Estates and the current sub-Saharan de-forestation and subsequent desertification by semi-nomadic pastoralists

• The global shortage of Food, Energy and Water – the FEW Crisis...

FEW Crisis


• FEW - Food, Energy, Water Crisis - as scarcity of Natural Resources (FEW - Food, Energy, Water) and increased competition from a growing population o obtain those scarce resources begins to limit and then reverse population growth, global population levels will continue expansion towards an estimated 8 or 9 billion human beings by the middle of this century – and then collapse catastrophically to below 1 billion – slowly recovering and stabilising out again at a sustainable population of about 1 billion human beings by the end of the century.

• Anthropogenic Impact (Human Activity) on the natural Environment - Global Massive Change Events. In their starkest warning yet, following nearly seven years of new research on the climate, the Intergovernmental Panel on Climate Change (IPCC) said it was "unequivocal" and that even if the world begins to moderate greenhouse gas emissions, warming is likely to cross the critical threshold of 2C by the end of this century. This will have serious environmental consequences, including sea level rises, heat-waves and changes to rainfall meaning dry regions get less and already wet areas receive more.

• In the past, many complex human societies (Neanderthal, Soloutrean, Clovis, Mayan, Khmer, Easter Island) have failed, died out or just simply disappeared - often as a result of either climate change or their own growth-associated impacts on ecological and environmental support systems. Thus there is a clear precedent for modern industrial societies - which continue to grow unchecked in terms of globalisation complexity and scale, population growth and drift, urbanisation and environmental impact – societies which are ultimately unsustainable, and so in turn must also be destined for sudden and catastrophic instability, failure and collapse.

http://www.theguardian.com/environment/ipcc

The Food Energy and Water (FEW) Crisis

History and Future of Climate and Environmental Change

Anthropogenic Impact (Human Activity) on the natural Environment

• Global Massive Change Events – many Human Activity Cycles, such as Business, Social,

Political, Economic, Historic and Pre-historic (Archaeology) Human Activity Waves - may be

compatible with, and map onto – one or more Natural Cycles. In their starkest warning yet,

following nearly seven years of new research on the climate, the Intergovernmental Panel on

Climate Change (IPCC) said it was "unequivocal" and that even if the world begins to moderate

greenhouse gas emissions, warming is likely to cross the critical threshold of 2C by the end of

this century. That would have serious consequences, including sea level rises, heat-waves and

changes to rainfall meaning dry regions get less and already wet areas receive more.

Possible Mechanisms for driving Human Activity Cycles • Cosmic Processes – ultra long-term Astronomic changes (e.g. Inter- / Intra-gallactic and solar system events)

• Geological Processes – very long-term global change e.g. Orogonies (Mountain Building), Volcanic Activity

• Biological Processes – Evolution and Carbon, Nitrogen, Oxygen and Sulphur Cycles (terra-forming effects)

• Solar Forcing – long-term periodic change in Insolation (solar radiation) due to Milankovitch Orbital Cycles

• Oceanic Forcing – ocean currents and climate systems– oscillation, temperature, salinity – Bond Cycles

• Atmospheric Forcing – rapid change in air temperature and Ice Mass / Melt-water Cycles – Heinrich Events

• Human Processes – Human Activity (agriculture, industrialisation) and impact on Global Climate / Ecosystems

• Atomic / Sub-atomic Processes – Particle Physics, Quantum Mechanics, Wave Mechanics and String Theory




Climate Change and Environmental Futures

• Increased severity and frequency of extreme weather events – El Nino and La Nina – combined with rising sea levels and natural disasters - has already begun to threaten our low-lying coastal cities (New Orleans, Brisbane, Fucoshida, Bangkok), A combination of rising sea levels, storm surges of increased intensity and duration, tsunamis and flash floods – will flood land up to 90 km into the interior from the present coast much more frequently by 2040 – drowning many major cities along with much of our most productive agricultural land – washing away homes and soil in the process. Human Population Drift and Urbanisation causes the destruction of arable land – as it is consumed by urban settlers and property speculators to build more cities.

By 2050 we may well have achieved the end of the World as we know it.....

• .....Global Massive Change is an evaluation of global capacities and limitations. It includes both utopian and dystopian views of the emerging world future state, in which climate, the environment and geology are dominated by human manipulation –

– Human Activity is the major factor in climate change environmental and ecological degradation.

– Environment – man now moves more rock and earth than do all natural geological processes.

– Ecology – global extinction rate is currently greater than that of the PTB extinction event

– Natural Resources – Food, Energy and Water (FEW) Crisis – global shortage, natural resources


• For most of human existence our ancestors have led precarious lives as scavengers, hunters, and

gatherers, and there were fewer than 10 million human beings on Earth at any one time. Today, many

of our cities have more than 10 million inhabitants each - as global human populations continue to

grow unchecked. The total global human population stands today at 7 billion - with as many as two or

three billion more people arriving on the planet by 2050.

• Human Activity Cycles - Business, Social, Political, Economic, Historic and Pre-historic (Archaeology)

Waves - may be compatible with, and map onto - one or more Natural Cycles. Current trends in

Human Population Growth are unsustainable – we are already beginning to run out of Food, Energy

and Water (FEW) – which will first limit, then reverse human population growth. Over the long term,

ecological stability and sustainability will be preserved – but at the expense of the continued,

unchecked growth of human populations. There are eight major episodic threats to Human Society,

which are “Chill”, “Grill”, “Ill”, “Kill”, “Nil”, “Spill”, “Thrill” and “Till” Moments: -

• “Chill Moments” – periods of rapid cooling, e.g. Ice Age Glaciations (Pluvial Periods) causing

depopulation of early hominids in Northern Europe in Pleistocene Eolithic times, abandonment of the

high fells, moors and highlands in Britain during the Iron Age Climate Anomaly, and impact of the

medieval “mini Ice Age” on Danish settlers in Greenland. These events may be linked to the periodic

reduction or failure of the Gulf Stream current – which brings warm water (and air) from the Caribbean

to the North Atlantic.


• “Grill Moments” - rapidly rising temperatures such as found in Ice Age Inter-Glacial episodes (Inter-

pluvial Periods) – causing environmental and ecological change under heat stress and drought –

precipitating the disappearance of the Neanderthal, Solutrean and Clovis cultures, drying, deforestation

and desertification driving the migration of the Anastasia in SW America along with desertification

drifting south and impacting on Sub-Saharan cultures today.

• “Ill Moments” - Contact with an foreign civilization or alien population and their parasitic bio-cloud -

carrying contagious diseases, which in pandemics to which the native population under exposure has

little or no immunity. Examples are the Bubonic Plague - Black Death - arriving in Europe from Asia,

Spanish Explorers sailing up the Amazon and spreading Smallpox to Amazonian Basin Indians from the

Dark Earth - Terra Prate - Culture and Columbian Sailors returning to Europe introducing Syphilis from

the New World, the Spanish Flu Pandemic carried home by returning soldiers at the end of the Great

War – infecting 40% and killing more people than did all the military action during the whole of WWI.

• “Kill Moments” – Invasion, conquest and genocide by a foreign civilization or alien population with

superior technology – destruction of mega-fauna, Roman conquest of Celtic Tribes in Western Europe,

William the Conquerors’ “Harrying of the North” in England, Spanish conquistadores meet Aztecs and

Amazonian Indians in Central and South America, Cowboys v. Indians in the plains of North America…..

• “Nil Moments” – Singularity or Hyperspace Events where the Earth and Solar System are swallowed

up by a rogue Black Hole – or the dimensional fabric of the whole Universe is ripped apart when two

Membranes (Universes) collide in hyperspace and one dimension set is subsumed into the other – they

could then merge into a large multi-dimensional Membrane – or split up into two new Membranes?....


• “Spill Moments” - Local or Regional Natural Disasters e.g. Andesitic volcanic eruption at subduction

tectonic plate margins - Vesuvius eruption and pyroclastic cloud destroying the Roman cities of

Herculaneum and Pompeii, Volcanic eruption / collapse causing Landslides and Tsunamis - Stromboli

eruption / collapse weakening the Minoan Civilisation on Crete, Krakatau eruption causing Indonesian

Tsunamis, ocean-floor sediment slips causing in recent years the recent Pacific and Indian Oceanic, and

Japanese Tsunamis – resulting in widespread coastal flooding, inundation & destruction.

• “Thrill Moments” - Continental or Global Natural Disasters – Extinction-level Events (ELE) such as the

Deccan and Siberian Traps Basaltic Flood Vulcanicity, Asteroid and Meteorite Impacts, Gamma-ray

Bursts from nearby collapsing stars dying and going Supernova – which have all variously contributed

towards the late Pre-Cambrian “Frozen Globe”, Permian-Triassic and Cretaceous-Tertiary boundary

global mass extinction events,,,,,”

• “Till Moments” - Society’s growth-associated impacts on its own ecological and environmental support

systems, for example intensive agriculture causing exhaustion of natural resources by the Mayan and

Khmer cultures, de-forestation and over-grazing causing catastrophic environmental damage and

ecological disasters - resulting in climatic change – for example, the Easter Island culture, the de-

population of upland moors and highlands in Britain from the Iron Age onwards – including the Iron Age

retreat from northern and southern English uplands, the Scottish Highland Clearances and replacement

of subsistence crofting by deer and grouse for hunting and sheep for wool on major Scottish Highland

Estates and the current sub-Saharan de-forestation and subsequent desertification by semi-nomadic

pastoralists.....


• Current trends in Human Population Growth are unsustainable – today we are already beginning

to run out of Food, Energy and Water (FEW) – a crisis which will first limit, then reverse human

population growth. Ecological stability and sustainability may be preserved – but only at the

expense of the continued, unchecked growth of human populations. Most natural resources –

arable land, fertilisers, food, energy sources, even clean water – begin to run out by about 2040.

Worst-case Extrapolated Population Curves and Growth Limit Analysis scenarios indicate a

dramatic collapse in population from about 2040 onwards - with numbers falling to well below the

1bn mark and probably recovering and stabilising out at around 1bn by the end of the century.

• Socio-Anthropologists, Economists and Demographic / Ethnographic Geographers – based on

the principles of Thomas Malthus and Pierre Verhulst – have updated population growth limit

curve extrapolations, which tend to converge towards a Global Population Collapse scenario by

the middle of this century. There are over 7bn Humans on the Earth today – rising from 1.6bn at

the turn of the 20th century. Over one-half of that human population is now urbanised, living in

cities – most of which are built either on the coast or alongside inland waterways. By 2050,

upwards of two-thirds of the 8-9bn human population will now be dwelling in cities – built mostly

near the coast, estuaries and deltas, alongside rivers, lakes and inland waterways. Rising sea

levels and intensifying weather systems will periodically create storm surges and flash floods

which will inundate land as far as 90 km from the present coast into the interior – drowning those

cities, killing and carrying off their inhabitants and washing away valuable real-estate and

infrastructure systems – along with the most productive coastal and river valley agricultural land.

Human Activity Shock Waves

1. Stone – Tools for hunting, crafting artefacts and making fire

2. Fire – Combustion for warmth, cooking and for managing the environment

3. Agriculture – Neolithic Age Human Settlements

4. Bronze – Bronze Age Cities and Urbanisation

5. Ship Building – Communication, Culture ,Trade

6. Iron – Iron Age Empires, Armies and Warfare

7. Gun-powder – Global Imperialism, Colonisation

8. Coal – Mining, Manufacturing and Mercantilism

9. Engineering – Bridges, Boats and Buildings

10. Steam Power – Industrialisation and Transport

11. Industrialisation – Mills, Factories, Foundries

12. Transport – Canals, Railways and Roads

13. Chemistry – Dyestuff, Drugs, Explosives, Petrochemicals and and Agrochemicals

14. Electricity – Generation and Distribution

15. Internal Combustion – Fossil Fuel dependency

16. Aviation – Powered Flight – Airships, Aeroplanes

17. Physics – Relativity Theory, Quantum Mechanics

18. Nuclear Fission – Abundant Energy & Cold War

19. Electronics – Television, Radio and Radar

20. Jet Propulsion – Global Travel and Tourism

21. Global Markets – Globalisation and Urbanisation

22. Aerospace – Rockets, Satellites, GPS, Space Technology and Inter-planetary Exploration

23. Digital Communications – Communication Age -Computers, Telecommunications and the Internet

24. Smart Devices / Smart Apps – Information Age

25. Smart Cities of the Future – The Smart Grid – Pervasive Smart Devices - The Internet of Things

26. The Energy Revolution – The Solar Age – Renewable Energy and Sustainable Societies

27. Hydrogen Economy – The Hydrogen Age – fuel cells, inter-planetary and deep space exploration

28. Nuclear Fusion – The Fusion Age – Unlimited Energy - Inter-planetary Human Settlements

29. Space-craft Building – The Exploration Age - Inter-stellar Cities and Galactic Urbanisation

“Kill Moments” – Major Natural and Human Activity catastrophes – War, Famine, Disease, Natural Disasters

“Culture Moments” – Major Human Activity achievements - Technology Development, Culture and History

Industrial Cycles – the phases of evolution for any given industry at a specific location / time (variable)

Technology Shock Waves – Stone, Agriculture, Bronze, Iron, Steam, Digital and Information Ages: -


• When we look at possible, probable and likely future human outcomes, we often extrapolate Malthus / Verhulse population growth curves / economic limiting factors / patterns and trends from previous human civilisations– which in the past have all ended in collapse scenarios where human cultures and societies have emerged, developed, experienced rapid growth, plateaued, declined – and have finally failed, died out or just simply disappeared from the historic record: -

• Many complex human societies (Solutrean, Clovis, Mayan, Aztec, Khmer and Easter Island) have been displaced, over-run, lost or disappeared, often as a result of a catastrophic event – a natural disaster, climate change, disease, exposure to a culture with superior technology – or simply as a consequence of their own society’s growth-associated impacts on the destruction of ecological and environmental support systems – dangers that we all still vey much face today.

Inca (Peru)

Aztecs (Mexico)

Olmec Civilisation

Mayan Civilisation

Muisca and Tairona Cultures

Pueblo Indians (Anastasia) – South-Western USA

Amazonian Indians - Dark Earth (Terra Prate) Culture

Indus Valley (Ayrian) Civilisation

Khmer Civilisation (Amkor)

Easter Islanders

Greenland Vikings (Medieval “mini Ice Age”)

Eocene - early hominids

Neanderthals

Solutrean / Clovis Cultures

Scythes

Parthians

Mesopotamians

Babylonians

Assyrians

Minoan Civilisation

Phoenicians

Etruscans

Seeing in Multiple Horizons: - Connecting Strategy to the Future

• THE THREE HORIZONS MODEL describes a Strategic Foresight method called “Seeing in Multiple Horizons: - Connecting Strategy to the Futures " The current THREE HORIZONS MODEL differs significantly from the original version first described in management literature over a decade ago. This model enables a range of Futures Studies techniques to be integrated with Strategy Analysis methods in order to reveal powerful and compelling future insights – and may be deployed in various combinations, whenever or wherever the Futures Studies techniques and Strategy Analysis methods are deemed to support the futures domains, subjects, applications and data in the current study.

• THE THREE HORIZONS MODEL method connects the Present Timeline with deterministic (desired or proposed) futures, and also helps us to identify probabilistic (forecast or predicted) future scenarios which may emerge as a result of interaction between embedded present-day factors and emerging catalysts of change – thus presenting us with a range of divergent possible futures. The “Three Horizons” method connects to models of change developed within the “Social Shaping” Strategy Development Framework via the Action Link to Strategy Execution. Finally, it summarises a number of futures applications where this evolving technique has been successfully deployed.

• The new approach to “Seeing in Multiple Horizons: - Connecting Strategy to the Future” has several unique features. It can relate change drivers and trends-based futures analysis to emerging issues. It enables policy or strategy implications of futures to be identified – and links futures work to processes of change. In doing so this enables Foresight to be connected to existing and proposed underlying system domains and data structures, with different rates of change propagation impacting across different parts of the system, and also to integrate seamlessly with tools and processes which facilitate Strategic Analysis. This approach is especially helpful where there are complex transformations which are likely to be radically disruptive in nature - rather than simple incremental transitions.

Andrew Curry Henley Centre HeadlightVision

United Kingdom

Anthony Hodgson Decision Integrity United Kingdom

Seeing in Multiple Horizons: - Connecting Strategy to the Future

The Three Horizons

Horizon Scanning, Tracking and Monitoring Processes

• Horizon and Environment Scanning, Tracking and Monitoring processes exploit the

presence and properties of Weak Signals – their discovery, analysis and interpretation

were first described by Stephen Aguilar Milllan in the 1960’s, and later popularised by

Ansoff in the 1970’s. Horizon Scanning is defined as “a set of information discovery

processes which data scientists, environment scanners, researchers and analysts use

to prospect, discover and mine the truly massive amounts of internet global content -

innumerable news and data feeds - along with the vast quantities of information stored

in public and private document libraries, archives and databases.”

• All of this external data is found widely distributed across the internet as Global Content

– RSS News Feeds and Data Streams, Academic Research Papers and Datasets - is

processed in order to detect and identify the possibility of unfolding random events and

clusters – “to systematically reduce the level of exposure to uncertainty, to reduce risk

and gain future insights in order to prepare for adverse future conditions – or to exploit

novel and unexpected opportunities for innovation" (LESCA, 1994). As a management

support tool for strategic decision-making, horizon and environment scanning process

have some very special challenges that need to be taken into account by environment /

horizon scanners, researchers, data scientists and analysts - as well as stakeholders.


• Horizon Scanning (Human Activity Phenomena) and Environment Scanning (Natural

Phenomena) are the broad processes of capturing input data to drive futures projects and

programmes - but they also refer to specific futures studies tool sets, as described below.

• Horizon Scanning, Tracking and Monitoring is a highly structured evidence-gathering

process which engages participants by asking them to consider a broad range of input

information sources and data sets - typically outside the scope of their specific expertise.

This may be summarised as looking back for historic Wave-forms which may extend into

the future (back-casting), looking further ahead than normal strategic timescales for wave,

cycle, pattern and trend extrapolations (forecasting), and looking wider across and beyond

the usual strategic resources (cross-casting). A STEEP structure, or variant, is often used.

• Individuals use sources to draw insights and create abstracts of the source, then share

these with other participants. Horizon scanning lays a platform for further futures activities

such as scenarios or roadmaps. This builds strategic analysis capabilities and informs

strategy development priorities. Once uncovered, such insights can be themed as key

trends, assessed as drivers or used as contextual information within a scenario narrative.

• The graphic image below illustrates how horizon scanning is useful in driving Strategy

Analysis and Development: -

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Scenarios.html

http://hsctoolkit.bis.gov.uk/Roadmaps.html

Strategy versus Horizon Scanning


• Horizon Scanning, Tracking and Monitoring is the major input for unstructured “Big Data” to

be introduced into the Scenario Planning and Impact Analysis process (along with Monte

Carlo Simulation and other probabilistic models providing structured data inputs). In this

regard, Scenario Planning and Impact Analysis helps to create a conducive team working

environment. It allows consideration of a broad spectrum of input data – beyond the usual

timescales and sources – drawing information together in order to identify future challenges,

opportunities and trends. It looks for evidence at the margins of current thinking as well as in

more established trends. This allows the collective insights of the group to be integrated -

demonstrating the many differing ways which diverse sources contribute to these insights.

• Horizon Scanning, Tracking and Monitoring is ideal as an initial activity for collecting Weak

Signal data input into the Horizon Scanning, Tracking and Monitoring process to kick-off

major futures studies projects and future management programmes. Scenario Planning and

Impact Analysis is also useful as a sense-making and interaction tool for an integrated

future-focused team. Horizon Scanning, Tracking and Monitoring combined with Scenario

Planning and Impact Analysis works best if people external to the organisation are included

in the team - and are encouraged to help bring together new and incisive perspectives.

• The graphic image below illustrates how horizon scanning is useful in spotting weak signals

that might be otherwise difficult to see – and so risk being overlooked: -

Seeing in Multiple Horizons


• The insights discovered by Scenario Planning and Impact Analysis can provide the basis

for prioritising research and development programmes, gathering business intelligence,

designing organisational scorecard objectives and establishing visions and strategies.

Steps

1. Participants are given a scope, focus and time horizon for the exercise.

2. Horizon Scanning, Monitoring and Tracking and Monte Carlo Simulations provide

sources of information. These data sets can come from internal or external sources

– Data Scientists, Domain Experts and Researchers, “Big Data” Analysts, the project

team, or from prior studies and data collection exercises from the individual team

participants. These should cover a broad external analysis, such as STEEP.

3. Individuals review the sources and spot items that cause personal insights on the

focus given. These insights and their sources are captured in the form of abstracts.

4. Abstracts are discussed and themed to indicate wave-forms over the time horizon

concerned. Scenarios are stacked, racked and prioritised by impact and probability.

5. The participants agree on how to address the resulting Scenarios, Waves, Cycles,

Patterns and Trends with supporting information for further futures analysis.

• More information about tools and uses of horizon scanning in Central Government can

be found on the Foresight Horizon Scanning Centre website.

http://hsctoolkit.bis.gov.uk/STEEP-a.k.a.-PEST.html

http://hsctoolkit.bis.gov.uk/Review-of-abstracts.html

http://www.bis.gov.uk/foresight/our-work/horizon-scanning-centre

Horizon Scanning, Tracking and Monitoring

• Are you all at sea over your future.....?

Forecasting and Predictive Analytics

• ECONOMIC MODELLING and LONG-RANGE FORECASTING •

• Economic Modelling and Long-range Forecasting is driven by atomic Data Warehouse

Structures and sophisticated Economic Models containing both Historic (up to 200 years daily

closing prices for Commodities, shares and bonds) and Future values (daily forecast and weekly

projected price curves, monthly and quarterly movement predictions, and so on for up to 50

years into the future – giving a total timeline of up to 250 years (Historic + 50 years Future trends

summary, outline movements and highlights). Forecast results are obtained using Economic

Models - Quantitative (technical) Analysis (Monte Carlo Simulation, Pattern and Trend Analysis -

Economic Growth and Recession / Depression shapes and Commodity Price Data Sets) in order

to construct a continuous 100 year “window” into Commodity Price Curves and Business Cycles

for Cluster Analysis and Causal Layer Analysis (CLA) – which in turn is used for driving out

Qualitative (narrative) Scenario Planning and Impact Analysis for describing future narrative epic

stories, scenarios and use-cases.

• PREDICTIVE ANALYITICS and EVENT FORECASTING •

• Predictive Analytics and Event Forecasting uses Horizon Scanning, Tracking and Monitoring

methods combined with Cycle, Pattern and Trend Analysis techniques for Event Forecasting and

Propensity Models in order to anticipate a wide range of business. economic, social and political

Future Events – ranging from micro-economic Market phenomena such as forecasting Market

Sentiment and Price Curve movements - to large-scale macro-economic Fiscal phenomena

using Weak Signal processing to predict future Wild Card and Black Swan Events - such as

Monetary System shocks.


• MARKET RISK •


• The two Mood States – “Greed and Fear” are primitive human instincts which, until now, we've

struggled to accurately qualify and quantify. Social Networks, such as Twitter and Facebook,

burst on to the scene five years ago and have since grown into internet giants. Facebook has

over 900 million active members and Twitter over 250 million, with users posting over 2 billion

"tweets“ or messages every week. This provides hugely valuable and rich insights into how

Market Sentiment and Market Risk are impacting on Share Support / Resistance Price Levels –

and so is also a source of real-time data that can be “mined” by super-fast computers to forecast

changes to Commodity Price Curves

• STRATEGIC FORESIGHT •

• Strategic Foresight is the ability to create and maintain a high-quality, coherent and functional

forward view, and to utilise Future Insights in order to gain Competitive Advantage - for example

to identify and understand emerging opportunities and threats, to manage risk, to inform

planning and forecasting and to shape strategy development. Strategic Foresight is a fusion of

Foresight techniques with Strategy Analysis methods – and so is of great value in detecting

adverse conditions, threat assessment, guiding policy and strategic decision-modelling, in

identifying and exploring novel opportunities presented by emerging technologies, in evaluating

new markets, products and services and in driving transformation and change.


• INNOVATION •

• Technology Innovation is simply combining existing resources in new and different ways –

in order to create novel and innovative Products and Services. Understanding the impact

of Technology Convergence is the Key to driving Innovation. Many common and familiar

objects in use today exist only as a result of technology convergence - your average,

everyday passenger vehicle or laptop computer is the culmination of a series of technology

consolidation and integration events of a large number of apparently separate, unrelated

technological innovations and advancements. Light-weight batteries were developed to

provide independence from fixed power sockets and hard-disk drives were made compact

enough to be installed in portable devices. Then the smart phone and tablet resulted from

a further convergence of technologies such as cellular telecommunications, mobile

internet, and Smart Apps - mini-applications that do not need an on-board hard-disk drive.

• FUTURE MANAGEMENT •

• Providing future analysis and strategic advice to stakeholders so that they might

understanding how the Future may unfold - in order to anticipate, prepare for and manage

the Future, to resolve challenging business problems, to envision, architect, design and

deliver novel solutions in support of major technology refreshment and business

transformation programmes • Future Analysis • Innovation • Strategic Planning •

Business Transformation • Technology Refreshment •


. • GEO-DEMOGRAPHICS •

• The profiling and analysis of large aggregated datasets in order to determine a ‘natural’ or

implicit structure of data relationships or groupings where no prior assumptions are made

concerning the number or type of groups discovered or group relationships, hierarchies or

internal data structures - in order to discover hidden data relationships - is an important starting

point forming the basis of many statistical and analytic applications. The subsequent explicit

Cluster Analysis as of discovered data relationships is a critical technique which attempts to

explain the nature, cause and effect of those implicit profile similarities or geographic

distributions. Geo-demographic techniques are frequently used in order to profile and segment

populations by ‘natural’ groupings - such as common behavioural traits, Clinical Trial, Morbidity

or Actuarial outcomes, along with many other shared characteristics and common factors –and

then attempt to understand and explain those natural group affinities and geographical

distributions using methods such as Causal Layer Analysis (CLA).....

• Social Media is the fastest growing category of user-provided global content and will eventually

grow to 20% of all internet content. Gartner defines social media content as unstructured data

created, edited and published by users on external platforms including Facebook, MySpace,

LinkedIn, Twitter, Xing, YouTube and a myriad of other social networking platforms - in addition

to internal Corporate Wikis, special interest group blogs, communications and collaboration

platforms. Social Mapping is the method used to describe how social linkage between

individuals defines Social Networks and to understand the nature and dynamics of intimate

relationships between individuals


• GIS MAPPING and SPATIAL DATA ANALYSIS •

• A Geographic Information System (GIS) integrates hardware, software, and data capture devices for acquiring, managing, analysing, distributing and displaying all forms of geographically dependant location data – including machine generated data such as Computer-aided Design (CAD) data from land and building surveys, Global Positioning System (GPS) terrestrial location data - as well as all kinds of aerial and satellite image data.

• Spatial Data Analysis is a set of techniques for analysing spatial (Geographic) location data. The results of spatial analysis are dependent on the locations of the objects being analysed. Software that implements spatial analysis techniques requires access to both the locations of objects and their physical attributes. Spatial statistics extends traditional statistics to support the analysis of geographic data. Spatial Data Analysis provides techniques to describe the distribution of data in the geographic space (descriptive spatial statistics), analyse the spatial patterns of the data (spatial pattern or cluster analysis), identify and measure spatial relationships (spatial regression), and create a surface from sampled data (spatial interpolation, usually categorized as geo-statistics).


• “BIG DATA” •

• “Big Data” refers to vast aggregations (super sets) of individual datasets whose size and

scope is beyond the capability of conventional transactional Database Management

Systems and Enterprise Software Tools to capture, store, analyse and manage. Examples

of Big Data include the vast and ever changing amounts of data generated in social

networks where we have (unstructured) conversations with each other, news data streams,

geo-demographic data, internet search and browser logs, as well as the ever-growing

amount of machine data generated by pervasive smart devices - monitors, sensors and

detectors in the environment – captured via the Smart Grid, then processed in the Cloud –

and delivered to end-user Smart Phones and Tablets via Intelligent Agents and Alerts.

• Data Set Mashing and “Big Data” Global Content Analysis – supports Horizon Scanning,

Monitoring and Tracking activities by taking numerous, apparently un-related RSS and

other Information Streams and Data Feeds, loading them into Very large Scale (VLS) DWH

Structures and Document Management Systems for Real-time Analytics – searching for

and identifying possible signs of relationships hidden in data (Facts/Events)– in order to

discover and interpret previously unknown “Weak Signals” indicating emerging and

developing Application Scenarios, Patterns and Trends - in turn predicating possible,

probable and alternative global transformations unfolding as future “Wild Card” or “Black

Swan” events.


• WAVE-FORM ANAYITICS in “BIG DATA” •

• Wave-form Analytics help identify Cycles, Patterns and Trends in Big Data – characterised as

a sequence of high and low activity in time-series data – resulting in periodic increased and

reduced phases in regular, recurring cyclic trends. This approach supports an integrated study

of the impact of multiple concurrent cycles - and no longer requires iterative and repetitive

processes of trend estimation and elimination from the background “noise”.

• FORENSIC “BIG DATA” •

• Social Media Content and Spatial Mapping Data is used in order to understand intimate

personal relationships between individuals and to identify, locate and describe their participation

in various Global Social Networks. Thus the identification, composition, monitoring, tracking

,activity and traffic analysis of Social Networks Criminal Enterprises and Terrorist Cells – as

defined by common locations, business connections, social links and inter-personal

relationships – is used by Businesses to drive Influencer Programmes and by Government for

National Security, Counter-Terrorism, Anti-Trafficking, Criminal Investigation and Fraud

Prevention purposes.....

• Forensic “Big Data” combines the use of Social Media and Social Mapping Data in order to

understand intimate inter-personal relationships for the purpose of National Security, anti-

Trafficking and Fraud Prevention – through the identification, composition, activity analysis and

monitoring of Criminal Enterprises and Terrorist Cells.....


Throughout eternity, all that is of like form comes around again –

everything that is the same must return in its own everlasting cycle.....


Many Economists and Economic Planners have arrived at the same

conclusion – that most organizations have not yet widely developed

sophisticated Economic Modelling and Forecasting systems – yet alone

integrated their model outputs into core Strategic Planning and Financial

Management process.....


Throughout eternity, all that is of like form comes around again – everything that is the same must return in its own everlasting

cycle.....


Many Economists and Economic Planners have arrived at the same conclusion - that most organisations have not yet widely adopted

sophisticated Business Intelligence and Analytics systems – let alone integrated BI / Analytics and “Big Data” outputs into their core Strategic

Planning and Financial Management processes.....


• Abiliti: Origin Automation is part of a global consortium of Digital Technologies Service Providers and Future Management Strategy Consulting firms for Digital Marketing and Multi-channel Retail / Cloud Services / Mobile Devices / Big Data / Social Media


– Email: (Office) – Telephone: (Mobile)











Business Cycles, Patterns and Trends Version 6 PDF

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