Management in complexity The exploration of a new paradigm Complexity in computing and AI Walter Baets, PhD, HDR Associate Dean for Innovation and Social Responsibility Professor Complexity, Knowledge and Innovation Euromed 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 TRESHOLD VALUE X1 X2 X3 X4 Inputs W1W1 W2W2 W3W3 W4W4 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

FUZZY LOGIC (2) Representation of the concept size using fuzzy sets

FUZZY LOGIC (3) Fuzzy rules (1) IF WARM THEN FAST STOP MEDIUM FAST 0 SLOW BLAST COLD COOL JUST RIGHT WARM HOT AIR MOTOR SPEED TEMPERATURE IN DEGREES FAHRENHEIT

FUZZY LOGIC (4) Fuzzy rules (2)

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 evidence Some 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 the medical care market: Agents Knowledge management at Akzo Nobel: improving the knowledge creation ability: ANNs, Akzo Nobel Information ecology: For the moment a conceptual model Agents Conflict management Agents Knowledge management at Bison: contribution to innovation Agents

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