Management in complexity

The exploration of a new paradigm

Complexity in computing and AIWalter Baets, PhD, HDR

Associate Dean for Innovation and Social ResponsibilityProfessor Complexity , Knowledge and InnovationEuromed Marseille – Ecole de Management

Chris Langton

Artificial life research

Genetic programming/algorithms

Self-organization (the bee colony)

Interacting (negotiating) agents

Conway’s game of life

One of the earlier artificial life simulations

Simulates behavior of single cells

Rules:

•Any live cell with fewer than two neighbors dies of loneliness•Any live cell with more than three neighbors dies of crowding•Any dead cell with exactly three neighbors comes to life•Any cell with two or three neighbors lives, unchanged to the

next generation

John Holland

Father of genetic programming

Agent-based systems (network)

Individuals have limited characteristics

Individuals optimize their goals

Limited interaction (communication) rules

Complex Adaptive Systems

Artificial Neural Networks

Agent-based systems (network)

Genetic Algorithms

Fuzzy logic

Fuzzy neural networks

ARTIFICIAL NEURAL NETWORKS (ANN) (1)

How does the brain operate?

ARTIFICIAL NEURAL NETWORKS (ANN) (2)

What does an artificial neural network look like?

Out2

Out1

Input Layer Hidden Layer Output Layer

X1

X2

X3

X4

X5

Xn

ARTIFICIAL NEURAL NETWORKS (ANN) (3)

How does an artificial neural network works (gets trained)

NET

TRESHOLDVALUE

X1

X2

X3

X4

Inputs

W1

W2

W3

W4

KNOT

Out-F (net)

Output

ARTIFICIAL NEURAL NETWORKS (ANN) (4)

Comparison to other DSS techniques (advantages)

Able to simulate non-linear behaviour

Has learning behaviour

Non-parametric (no equations)

Fault tolerant (can easily deal with NAs)

Seeking diversity (instead of averages)

Pattern recognition

FUZZY LOGIC (1)

Fuzzy sets and overlapping membership-functions

0 150 185 200

Tall

.7 1

Deg

ree

ofm

embe

rshi

p

Height in cm

FUZZY LOGIC (2)

Representation of the concept size using fuzzy sets

Height in cm

average height

0 150 185 200

Tall

.7

1

Deg

ree

ofm

embe

rshi

p

0.49

short

very tall

FUZZY LOGIC (3)

Fuzzy rules (1)

1

100 90 80 70 60 50 40 30 20 10 0

0

1

45 50 55 60 65 70 75 80 85 90

IF WARMTHEN FAST

STOP

MEDIUM

FAST

0

SLOW

BLAST

COLD COOL

JUST

RIGH

T

WARM HOT

AIR

MO

TOR

SPEE

D

TEMPERATURE IN DEGREES FAHRENHEIT

FUZZY LOGIC (4)

Fuzzy rules (2)

7º 13º 16º 18º 21º 24º 27º 29º 32º10º0

1

0 1

100

90

80

70

60

50

40

30

20

10

IFCOLD,THENSTOP

IF COOL,THENSLOW

IF JUSTRIGHT,THENMEDIUM

IF WARM,THEN FAST

IF HOT,THENBLAST

Fuzzy rulesA

IR M

OT

OR

SP

EE

D

TEMPERATURE IN DEGREES CELSIUS

FUZZY LOGIC (5)

ADVANTAGES:• Smooth behaviour• “Human-like” behaviour• Natural language approach

EXAMPLES:• Sendai Subway• Trading systems• Washing machines, CAM-corders,

micro-waves

FUZZY NEURAL NETWORKS IN MANAGEMENT

Combination of the learning behaviour of neural networks with the fuzziness and the (though fuzzy) rules

Overlapping and vague memberships is a reality in managerial problems

Fuzzy rules is a reality in management

Fuzzy and learning behaviour is very human

Pretty much to be discovered in management sciences

GENETIC ALGORITHMS (1)

GENETIC ALGORITHMS (2)

GENETIC ALGORITHMS (3)

GENETIC ALGORITHMS (4)

GENETIC ALGORITHMS (5)

GENETIC ALGORITHMS (6)

GENETIC ALGORITHMS (7)

GENETIC ALGORITHMS (8)

A beginning of evidenceSome research projects

Complexity and emergent learning in innovation projects: Agents, Sara Lee/DE

Innovation in SME’s: a network structure: ANNs, brainstorm sessions

Telemedecin: a systemic research into the ICT innovations in themedical care market: Agents

Knowledge management at Akzo Nobel: improving the knowledgecreation ability: ANNs, Akzo Nobel

Information ecology: For the moment a conceptual model Agents

Conflict managementAgents

Knowledge management at Bison: contribution to innovationAgents

Complexity in economics

Law of increasing returns (Brian Arthur)

• Characteristics of the information economy (a non-linear dynamic system)

• Phenomenon of increasing returns

• Positive feed-back

• No equilibrium

• Quantum structure of business (WB)

Summary (until now)

• Non - linearity• Dynamic behavior• Dependence on initial conditions• Period doubling• Existence of attractors• Determinism• Emergence at the edge of chaos