Post on 25-Jan-2016
description
About complexity and knowledgeHow order leads to chaos !
Prof dr Walter BaetsEuromed Marseille, Ecole de Management
The Nyenrode Institute for Knowledge Management and Virtual Education
Flatland: Edwin Abbott, 1884
A. Square meets the third dimension
Wanderer, your footprints arethe path, and nothing more;Wanderer, there is no path,it is created as you walk.By walking,you make the path before you,and when you look behindyou see the path which after youwill not be trod again.Wanderer, there is no path,but the ripples on the waters
Antonio Machado,Chant XXIX Proverbios y cantares,Campos de Castilla, 1917
A very great musician came and stayed in our house,He made one big mistake …He was determined to teach me musicand consequently, no learning took place.Nevertheless, I did casually pick up from hima certain amount of stolen knowledge
Rabindranath Tagore
Sometimes small differences in the initialconditions generate very large differencesin the final phenomena. A slight error in the former could produce a tremendous error in the latter.Prediction becomes impossible; we haveaccidental phenomena.
Poincaré in 1903
Taylor’s view on the brain
The computer: attempt to automate human thinking
Manipulating symbols Modeling the brain
Represent the world Simulate interaction of neurons
Intelligence = problem solving Intelligence = learning
0-1 Logic and mathematics Approximations, statistics
Rationalist, reductionist Idealized, holistic
Became the way of building computersBecame the way of looking at minds
I
WE
IT
ITS
Interior-IndividualIntentional
Interior-collectiveCultural
Exterior-IndividualBehavioral
Exterior-CollectiveSocial
World of: sensation, impulses, emotion, concepts, vision
World of: magic, mythic, values
World of: atoms, molecules, neuronal organisms, neocortex
World of: societies, division of labour, groups, families, tribes, nation/state,agrarian, industrial and informational
Truthfulness
Justness Functional fit
Truth
Ken Wilber: A Brief History of Everything
Complexity Theory
Sensitivity to initial conditions (Lorenz)
Xn+1 = a * Xn * (1 - Xn)
0.294 1.4 0.3 0.7
Cobweb Diagrams (Attractors/Period Doubling)
Xn+1 = * Xn * (1 - Xn) (stepfunction)
dX / dt = X (1 - X) (continuous function)
On the diagrams one gets:• Parabolic curve• Diagonal line Xn+1 = Xn
• Line connecting iterations
Lorenz curve (Butterfly effect)
Lorenz (1964) was finally able to materialize Poincaré’s claim
Lorenz weather forecasting model
dX / dt = B ( Y - X )
dY / dt = - XZ + rX - Y
dZ / dt = XY - bZ
Ilya Prigogine
• Non-linear dynamic models (initial state, period doubling,….)
• Irriversibility of time principle
• Behaviour far away from equilibrium (entropy)
• A complex system = chaos + order
• Knowledge is built from the bottom up
Why can chaos not be avoided ?
• Social systems are always dynamic and non-linear
• Measurement can never be correct
• Management is always a discontinuous approximation of a continuous phenomenon
Francesco Varela
• Self-creation and self-organization of systems and structures (autopoièse)
• Organization as a neural network
• The embodied mind
• Enacted cognition
• Subject-object division is clearly artificial
• How do artificial networks operate (Holland)
Knowledge and learning
ENVIRONMENT
Single-loop learning
ENVIRONMENT
Single-loop learning
Environmentalresponse
Individual action
INDIVIDUAL MENTAL MODEL &
FRAMEWORKS
INDIVIDUAL MENTAL MODEL &
FRAMEWORKS
ORGANIZATIONALROUTINES &
SHARED MENTALMODELS
ORGANIZATIONALROUTINES &
SHARED MENTALMODELS
Individual double-loop learning
Organizational double-loop learning
Organizational action
OADI-cycle/Individual learning
ASSESS
DESIGN
IMPLEMENT
OBSERVE
The Hybrid Business School
EXPERIENCES
INDIVIDUAL MENTALMODEL &
TACIT KNOWLEDGE
SHARING AND COMMUNICATION
SHARED MENTAL MODEL &
KNOWLEDGEREPOSITORY
ContextualInter-Action
Contextualization
CONTEXTUAL KNOWLEDGE
Real life Databases Procedures Simulators Executive seminars Concepts Theory
Inter-Action
IT for the Hybrid Business School
CASE BASEDREASONING
SYSTEM
Structuring
Advising
ARTIFICIAL NEURALNETWORKS &OTHER A.I.
TECHNIQUES
Sharing and Communicating the
Emergent
COMMUNICATIONPLATFORM /
NEURAL NETWORKS
Consultatio
n
Rules
Learning MaterialExpertise
DATA BASES LEARNING
ENVIRONMENT SIMULATORS EXPERT
SYSTEMS COMPUTER
BASED TEACHING
VIDEO-CONFERENCING
IT for the Corporate Knowledge Approach
INDIVIDUAL MENTAL MODEL
INDIVIDUAL MENTAL MODEL
SHARED MENTALMODELS
SHARED MENTALMODELS
Innovation as learning
Internal External
Individual
Collective
EXPERIENCES
CONTEXTUAL KNOWLEDGE
Individuals with characteristics (agents)
Interaction
Emotions Facts
Emotions
Emotions
Your knowledge infrastructure
Learning platform
Provide an ICTinfrastructurethat allows full
access and sharing facilities
Content
What knowledgeto share:•explicit•implicit•learned
Ownership (search/learn principles)Remains with those that use it
Those that want to learn decide what to learnJust-in-time, just-enough
Culture
Turn XYZinto a learning
culture (viaprojects)
Rewarding
Your knowledge infrastructureYour knowledge infrastructure
Learning platform and search/learn principles
The knowledge net
Explicit knowledge (database)
Searc
h e
ngin
e
Implicit knowledge (case base)Case based reasoning systemCases stored in an adapted wayA methodology for case analysis and storageCorporate knowledge repository Notion
Learned knowledge (case base)Explicit knowledge that is enhanced via experienceUsing the same methodology for implicit knowledgeInterviews with key knowledge owners
Open learning platformCollaborative tools
Dedicated search enginesAccessibility for all
Open to connect ‘any’application
Solution for e-learning
The userwith its learning
agenda
INTERNETINTRANET
PCCD ROM
BOOKS
WWW site + other knowledge applications
A TYPICAL MANAGEMENT DIPLOMA COURSE 3O % SELF-STUDY (learning-by-doing) 2O % WORKSHOPS 50 % PROJECT WORK
SKIL
LS/
ACTIV
ITIE
S
CASES
CONCEPTS
LEARNING/DATABASE SOFTWARE
EXECUTIVECOURSES
HYPERTEXTDATABASE
For a 18-24 months period
Workshops: 200 study hours Innovative projects: 700 study hoursVirtual grouplearning: 600 study hours
Some interesting technologies
Artificial Neural NetworksGenetic AlgorithmsGenetic ProgrammingFuzzy LogicArtificial life/Agent simulationsNegotiating AgentsSemantic Search EnginesCase Based ReasoningLanguage technologiesMachine learning technologiesConversational technologies
The Hybrid Business School
Building Blocks
Ownershiplearn/search
Learning Agenda(Pers. Development)
cult
ure
plat
form
IC
T
cont
ent
Explicit knowledge
Implicit knowledge
Sear
ch
engi
ne
learner+
learningagenda
Knowledge platform
case
sSki
llsActi
vitie
s
Concepts
Pra
ctic
es
Hyp
er
linke
d
Hyp
er
linke
d
Methodology Actions Outcomes(company-specific)
Brainstorm
4 Brainstorms
Project team• Notion• MD/HRM• Line mgt• IT• Marketing/R&D
IT/Application plan
White Paper(Board approval)E-learning view
4 Action plans(Board approval)
Infrastructure(Plan)
Architecture
Some statements
Knowledge products can easily be copied (pharma example);Information even faster
Is legal protection possible in the knowledge economy? (patents) Protection on HIV drugs: ethics against law (South Africa)Mobile phones: money is not made on the hardware, but
on the servicesWhat is a company’s value added: is it learning or repetition
(can it be machine replaced ?)Information against faster learning based innovation
I
WE
IT
ITS
Interior-IndividualIntentional
Interior-collectiveCultural
Exterior-IndividualBehavioral
Exterior-CollectiveSocial
World of: sensation, impulses, emotion, concepts, vision
World of: magic, mythic, values
World of: atoms, molecules, neuronal organisms, neocortex
World of: societies, division of labour, groups, families, tribes, nation/state,agrarian, industrial and informational
Truthfulness
Justness Functional fit
Truth
Ken Wilber: A Brief History of Everything