Complex Social Systems: a guided tour to concepts and methods · Weaver W. Science and Complexity....

Overview Presentation

Complex Social Systems: a guided tour to concepts and methods

Martin Hilbert (Dr.; Ph.D.)

MartinHilbert[at]gmail.com

Today’s questions

I. What are the characteristics of complex systems?

II. What are some of the tools to analyze complex systems?

Definitions of Complexity

Seth Lloyd. Measures of complexity: a nonexhaustive list. IEEE Control Systems. Aug;21(4):7–8.

42 definitions of complexity:

“Three questions that researchers frequently ask to quantify the complexity of the thing:

1. How hard is it to describe?

[entropy, minimum description length, etc.]

2. How hard is it to create?

[computational complexity, cost, etc.]

3. What is its degree of organization?

[tree subgraph diversity, mutual information, etc.]”

Weaver W. Science and Complexity. American Scientist. 1948;36:536–544.

1. Problems of simplicity: “…science before 1900 was largely concerned with two-variable problems” temperature & pressure; population & time; production & trade, etc.

2. Problems of disorganized complexity: “…subsequent to 1900… scientists… developed powerful techniques of probability theory and of statistical mechanics … each of the many variables has a behavior which is individually erratic.”

billiard balls & air molecules; normal distributions; etc.

3. Problems of organized complexity: “…dealing simultaneously with a sizable number of factors which are interrelated into an organic whole… cannot be handled with the statistical techniques so effective in describing average behavior… “

Science must, over the next 50 years, learn to deal with these problems of organized complexity”

Historical context of Complexity

Characteristics of Complexity

Connected Interdependent Diverse Adaptive Path-dependent Emergent

Social Networks

Source: Christakis and Fowler, 2007

Different network structures lead to different diffusion curves

% of U.S. households

1900 1915 1930 1945 1960 1975 1990 2005

General tendency: increased social connectedness leads to increased speed of diffusion

9/11 Terrorist Network

Friendship by race

Source: http://www.visualcomplexity.com/vc/; Powell et.al. (2010);

Food Network

Innovation & Industry Social = network

Connected: social network analysis

Characteristics of Complexity

Gender Location Income Educat. …

Jorge M Urban 700 Tertiary …

Maria F Urban 500 Second. …

Juan M Rural 300 Primary …

Magda F Rural 200 --- …

… … … … …

Maria Jorge

Juan Magda

Traditional database of attributes Network database of links

Jorge Maria Juan Magda …

Jorge Self --- …

Maria Self --- …

Juan --- --- Self --- …

Magda --- Self …

… … … … …

Maria Jorge

Juan Magda

Summary of characteristics and (selected) methodological tools of Complexity


Interdependent: dancing landscapes

Diverse

Adaptive

Path-dependent

Emergent

Scientific management Mount Fuji (Osorno, Villarica) landscapes (from Taylorism to Fordism)

21 pounds

Am

ou

nt

of

coal

lif

ted

in 8

ho

urs

Size of shovel

Interdependency between coal and shovel size

Rugged Landscape: engineering of internal trade-offs P

erf

orm

ance

/ f

itn

ess

Fixed costs

Variable costs

0

50

100

150

200

250

300

1930 1935 1940 1945 1950 1955 1960 1965

airspeed propeller mph

airspeed jet mps

Source: Devendra Sahal, Patterns of Technological Innovation, 1981

Evolution of Aircraft Technology Perfromance

9% growth

5% growth

2% growth

Wave-like nature of punctuated equilibria (discovering new peaks)

Continuous technological

progress

Disruptive technological

progress

Disruptive technological

progress

Dancing/ coupled/ “tunably rugged” Landscapes: interdependencies

The NK model N: number of components

K: ‘degree’ of interdependency

Dancing business strategy / public policy landscapes

Interdependencies

among internal

choices

(trade-offs)

makes landscape

rugged

Interdependencies

with external

agents makes

landscape

dance!

Exploration and Exploitation & dancing landscapes

Fitn

ess

“…individual agents balance the necessity to explore and exploit, producing complexity as a result…” (S.E. Page, 2009)




Diverse: entropy and differences

Adaptive

Path-dependent

Emergent

From Entropy…

Claude E. Shannon (1948) A Mathematical Theory of Communication, Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656.

…to Variance…

Ø = 1.8

Ø = 1.5 Ø = 1.2

Ø = 1.8

Ø = 2.5

Ø = 4.2





Adaptive: information processing

Path-dependent

Emergent






Path-dependent: dynamics & chaos

Emergent

Chaos => change over time depends on initial conditions!

Henri Poincaré

“If we knew exactly the laws of nature and the situation of the universe at the initial moment, we could predict exactly the situation of that same universe at a succeeding moment. But even if it were the case that the natural laws had no longer any secret for us, we could still only know the initial situation approximately. …it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter. Prediction becomes impossible, and we have the fortuitous phenomenon.”

(Science and Method, 1903)

0.1 =>> 0.01 =>> … =>> 0.00000000000000001

(1854 –1912)







Emergent: agent-based models

http://www.plataformaurbana.cl ; http://globedia.com

Schelling’s segregation model

2400 agents: 51 % => ? 49 % => ? 33% => ? 26 % & 25 % => ? 75 % & 76 % => ?

Total is different than sum of parts (total racists lead to mixed society…)

Phase transitions Dependence on initial conditions

Invariant Distribution

Empirical data from Los Angeles, Milwaukee, Cincinnati, Omaha, & Kansas City show “that the Schelling description of preferences is broadly correct but that the empirical curves are less regular than those posited by Schelling…” Clark (1991). Residential Preferences and Neighborhood Racial Segregation: A Test of the Schelling Segregation Model. Demography, 28(1), p.17

Alternative Approaches

Schelling, T. C. (1971). Dynamic models of segregation. The Journal of Mathematical Sociology, 1(2), 143–186.

“I cannot too strongly urge you to get the dimes and pennies and do it yourself… there is nothing like tracing it through for yourself and seeing the thing work itself out. In an hour you can do it several times and experiment with different rules of behavior, sizes and shapes of boards, and (if you turn some of the coins heads and some tails) subgroups of dimes and pennies…” (p. 150)

Sugarscape

Physical landscape

Rule: move to next open spot with most sugar and consume it

Random “genetic” endowments:

Cognitive ability (vision)

Metabolism

Maximum age

Initial location (random)

Pareto “sugar/income” distribution emerges!

Li, J. and Wilensky, U. (2009). NetLogo Sugarscape 1 Immediate Growback model. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Initial endowments: 1:2? 1:200? 199:200?

Why?

?

?

?

?

?

?

…just one of a couple of dozen of Sugarscapes

Iain Weaver (2009) The Sugarscape; http://ccl.northwestern.edu/netlogo/models/community/Sugarscape

Fine-tuned capability diversity

Foresight and planning

Seasons

Migration patterns

Off-springs and inheritance

Sugar & Spice

Trade and price

Lending and interest rates

Pollution of resource extraction

Disease

Cultural belonging

…

Complex Challenges

Descriptive / bottom-up / micro => macro:

Given (change in) individual rules, what is the global behavior?

Schelling (1969). Models of Segregation. Am. Econ. Review, 59(2), p.488

“Economists are familiar with systems that lead to aggregate results that the individual neither intends nor needs to be aware of, the results sometimes having no recognizable counterpart at the level of the individual. …savings decisions cause depression or inflation… The interplay of individual choices… is a complex system with collective results that bear no close relation to the individual intent.”

Epstein & Axtell (1996). Growing Artificial Societies. Bradford; pp. 51-52.

“…upon first exposure to these familiar social, or macroscopic, structures …some people say, “Yes, that looks familiar. But, I’ve seen it before. What’s the surprise?” The surprise consists precisely in the emergence of familiar macrostructures from the bottom up – from simple local rules that outwardly appear quite remote from the social, or collective, phenomena they generate. In short, it is not the emergent macroscopic object per se that is surprising, but the generative sufficiency of the simple local rules.”

Macro can be surprising Micro can be surprising

Interventionist / top-down / macro => micro:

Given (desired) global behavior, what should be the individual rules?

…more realistic / complex models of society:

Source: Computational Modeling at Olin College. https://sites.google.com/site/compmodolin/lectures/lecture-01

Summary of characteristics and (selected)

analytical tools of Complex Systems






Emergent: agent-based models

Complex Social Systems: a guided tour to concepts and methods · Weaver W. Science and Complexity....

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