knowledge management and management learning...
TRANSCRIPT
Knowledge Management
Complexity theory and
Walter Baets, PhD, HDRAssociate Dean for ResearchAssociate Dean for ResearchMBA DirectorProfessor Complexity , Knowledge and InnovatioEuromed Marseille – Ecole de Management
Erna Baets Oldenboom, MA, MPhilProfessor Leadership, Sustainable Performance,
t and Management Learning
the quantum interpretation
on
and Mind/Body Medicine
Sometimes small differe
conditions generate very
in the final phenomena.
former could produce a
the latter.
Prediction becomes impo
accidental phenomena.
PP
ences in the initial
y large differences
A slight error in the
tremendous error in
ossible; we have
i é i 1903oincaré in 1903
Sensitivity to initial
X * XXn+1 = a * Xn
0.294 1.4 0.3
conditions (Lorenz)
* (1 X )n * (1 - Xn)
3 0.7
Cobweb Diagrams (AttCobweb Diagrams (Att
Xn+1 = μ * Xn *
dX / dt = μ X (1 -μ (
On the diagram• Parabolic curv
Di l li• Diagonal line • Line connectin
tractors/Period Doubling)tractors/Period Doubling)
(1 - Xn) (stepfunction)
- X) (continuous function)) ( )
ms one gets:ve
X XXn+1 = Xnng iterations
Lorenz curve (But
L (1964) fi ll bl Lorenz (1964) was finally able
Lorenz weather forecasting mo
dX / dt = B ( Y - X )
dY / dt = - XZ + rX - Y
dZ / dt = XY - bZ
tterfly effect)
t t i li P i é’ l i to materialize Poincaré’s claim
odel
Hénon Att
X n+1 = 1 - a X n+1 a
Y n+1 = b * X n+1
A in diff nt ttAgain, different attr
Other examples: PenPoincaré, HorsePoincaré, Horse
tractor
* X 2 n + Y n X n Y n
nn
t h nractors are shown
ndulum of e Shoee Shoe
h h Why can chaos n
• Social systems areSocial systems arenon-linear
• Measurement can Measurement can
M i l• Management is alwapproximation oapproximation ophenomenon
b d d not be avoided ?
e always dynamic and e always dynamic and
never be correctnever be correct
di i ways a discontinuous of a continuous of a continuous
Ilya Prigogine
• Non-linear dynamic mperiod doublingperiod doubling,….
• Irreversibility of tim• Irreversibility of tim
• The constructive roleThe constructive role
• Behavior far away froBehavior far away fro
• A complex system = cA complex system = c
• Knowledge is built froKnowledge is built frobottom up
models (initial state, ).)
me principleme principle
e of timee of time
om equilibrium (entropy)om equilibrium (entropy)
chaos + orderchaos + order
om the om the
Entropy
M sur f r th m unt f dMeasure for the amount of d
When entropy is 0, no furtheWhen entropy is 0, no furthe(interpretation is that no info
h There is a maximum entropy diagram, this is 4)
Connection between statisticaentropy to a chaotic system py yassociated statistical system
dis rd rdisorder
er information is necessaryer information is necessaryormation is missing
h ( h b fin each system (in the bifurcation
al mechanics and chaos is applying in order to compare with anp
Francesco VarelaFrancesco Varela
• Self-creation and selsystems and structuy
• Organization as a neu• The embodied mind• Enacted cognition• Subject-object divisj j• How do artificial netw• Morphic fields and mp
(Sheldrake)
lf-organization of ures (autopoièse)( p )ural network
ion is clearly artificialyworks operate (Holland)
morphic resonance p
Chris LangtonChris Langton
Artificial life researchArtificial life research
Genetic programming/a
Self-organization (the
Interacting (negotiatin
algorithms
bee colony)
ng) agents
Conway’s game of
One of the earlier artific
Simulates behavior of sin
Rules:
•Any live cell with fewer than •Any live cell with more than t•Any dead cell with exactly th•Any cell with two or three ne
next generationnext generation
Plife.exe (windows)( )
f life
cial life simulations
gle cells
two neighbors dies of lonelinessthree neighbors dies of crowdinghree neighbors comes to lifeeighbors lives, unchanged to the
John HollandJ H
Father of genetic progr
Agent-based systems (n
I di id ls h li it d Individuals have limited
Individuals optimize theIndividuals optimize the
Limited interaction (com
ramming
network)
h t isti s characteristics
eir goalseir goals
mmunication) rules
Law of increasing Law of increasing (Brian Arthur)
• Characteristics of th( li d(a non-linear dynam
• Phenomenon of incre
• Positive feed-back
• No equilibrium
• Quantum structure oQ m(WB)
returns returns
he information economyi )ic system)
asing returns
of business f
Summary (un
• Non - linearity• Dynamic behavio• Dynamic behavio• Dependence on iP i d d bli• Period doubling
• Existence of att• Determinism• Emergence at thEmergence at th
ntil now)
ororinitial conditions
tractors
he edge of chaoshe edge of chaos
Gödel’s theorem: 1931Gödel s theorem: 1931No absolute axiomatic syst
Relativity theory (Einstein)No absolute measurement i
Quantum mechanics: first pObservation is interpretatiObservation is interpretati
Complexity theory (Prigoginp y yEmergence, bifurcations, st
tem is possible
): first part of the 20st centuryis possible
part of the 20st centuryionion
ne): second part of 20st centuryp ytrange attractors
Once holism and complm mpwe cannot avoid a fund
PAULI comple
Syy(=occurring
From causal coherence (from cause to effect)
A-cau
exity acceptedy pdamental question
ementary physics
ynchronicityy yg–together-in-time)
Coincidence (occurring together)
usal linkshence….
Mechanistic verThe evolution i
Product oriented Unique distribution channelsqControlStabilityM t b bj tiManagement by objectiveProcesses are the assetsHierarchical organization Hierarchical organization Machine thinking (symbolic)Industrial era
rsus organic:gn business
The client co-createsMultiple channelspEmergent processesChange (learning) is the goalM t i h d l itManagement in change and complexityLearning is the assetHuman networksHuman networksHuman thinking (fuzzy)Knowledge era
S tSome quantumMaxwell, Planck and Bohr: intro
beauty and coherenceEinstein de Br lie and SchrödEinstein, de Broglie and Schröd
continuous wave as a bacausal descriptioncausal description
Heisenberg, Pauli, Jordan and Devent-by-event causalit
ll d f d well-defined trajectorieIn 1935, Schrödinger formulatePauli: Background physics has aPauli: Background physics has a
to a natural science whias with consciousness
Pauli accepted that physical valin the eyes of the obserf h m n ns i sn ssof human consciousness
t im storiesoduced criteria such as fertility,
din er: shared a c mmitment t a dinger: shared a commitment to a asic physical entity subject to a
Dirac: we no longer have ty and particles do not follow
b k des in a space-time backgrounded his famous ‘cat paradox’n archetypal origin and that leads n archetypal origin and that leads ich will work just as well with matter
lues, as much as archetypes, change rver. Observation is the result
ss
Some quantum sq
Polkinghorne: The implication of phenomenon of “entanglephenomenon of entangleremote activity, not simpontological in nature
Polkinghorne (1990): The greatethe more the consciousnresonate with the hologrresonate with the hologr"quantum zero point" (thin an almost resting, but g,energy field
stories (2)( )
these observations is that the ement” (non-locality) includes a real ement (non locality) includes a real ply epistemological, but in fact
r the experience of satisfaction, ess of each cell in the body will raphic information engraved in the raphic information engraved in the he lowest possible state of energy, not quite, situation) of the q , )
So, on the Copenhagen interph i l t thphysical processes are, at the
inherently indeterministic anclassical physics is dead Theclassical physics is dead. Theentanglement (or non-separabgives rise to the measuremenmakes it impossible to assign arbitrary isolated physical sywith another system in the pawith another system in the pasystems are no longer interaccharacteristic of quantum sysf q m yindication of the ‘holistic’ cha
pretation of quantum mechanics, t f d t l l l b th e most fundamental level, both
d non-local. The ontology of e heart of the problem is the e heart of the problem is the bility) of quantum states that nt problem. This entanglement independent properties to an
ystem once it has interactedast even though these two ast – even though these two cting. The non-separability stems can be seen as an maracter of such systems.
A quantum in
In the arts: Cara et MurphyIn linguistics: Dalla Chiarra egIn the physical sciences: PauIn biology: Sheldrake (morphI m di i : Ch th AIn medicine: Chopra, the Ay
regular medici
nterpretation
yet Giuntiniulihogenetic fields and resonance)
d b t l i i l i yurveda, but also increasingly in ine
The Bogdanov
Beyond the « Wall Beyond the « Wall Before “the big baThere is a fifth dim
a fourth of spf f pimaginary tim
Time-space really bTime-space really bThat singularity ha
t movement any
Singularity (2)
of Planck » ? of Planck » ?ng” ?mension, beingpace expressed in p p
mebecomes a continuumbecomes a continuumas no classical
(“ h t i ”)ymore (“what is”)
The Bogdanov
Beyond the « Wall Beyond the « Wall Before “the big baThere is a fifth dim
a fourth of spf f pimaginary tim
Time-space really bTime-space really bThat singularity ha
t movement any
Singularity (2)
of Planck » ? of Planck » ?ng” ?mension, beingpace expressed in p p
mebecomes a continuumbecomes a continuumas no classical
(“ h t i ”)ymore (“what is”)
EEntra(Institute of Heart M(Institute of Heart M
Ph si l f m tiPhysiology of emotioHow emotions influe
behavior and hThe heart is a highlThe heart is a highl
a sensory orga(nervous cente(nervous cente
That “heart brain” at k d i itakes decisionthe brain’s cer
ainmentMath; www heartmath org)Math; www.heartmath.org)
sonsence cognition, healthy complex system:y complex system
an, a heart brain er)er)allows us to learn and
i d d t fns independent fromrebral cortex
Entrainme
There is a strong iThere is a strong ibody via an el
Rythms should natuRythms should natuthat heart rh
The same happens (pendulum clo(p m
Socio-emotional intmother and cmother and c
Heart coherence
nt (2)
nteraction through thenteraction through thelectromagnetic fieldurally synchronize onurally synchronize onhythmbetween people
ocks: Huygens)yg )teraction between hildhild
A beginning of g gSome research pr
Complexity and emergent learninp y gAgents, Sara Lee/DE
Innovation in SME’s: a network sANN b i t iANNs, brainstorm sessio
Telemedecin: a systemic researcmedical care market:medical care market:Agents
Knowledge management at Akzo creation ability: ANNs, Akzo Nobel
Information ecology: Information ecology: For the moment a concepAgentsg
Conflict managementAgents
K l d t t BiKnowledge management at BisonAgents
evidenceojectsng in innovation projects:g p j
structure:onsch into the ICT innovations in the
Nobel: improving the knowledge
ptual model
t ib ti t i ti: contribution to innovation
ResearchI h f In search of «
Expected contributions
• Can we visualize synchronicit• What are the organizing prin
emergenceemergence• Emergent concepts in manag• « Complex Adaptive SystemsComplex Adaptive Systems
Agents, Neural Networ• The contribution of this par
i i i iinnovation in companies• Another understanding of inn
h agendah i itsynchronicity »
s
ty in managementnciples and what is precisely
ements » as research tools s as research tools rks, Learning systemsadigm for knowledge, learning and snovation