1. 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

2. Chris Langton Artificial life research Genetic programming/algorithms Self-organization (the bee colony) Interacting (negotiating) agents

3. 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

4. John Holland Father of genetic programming Agent-based systems (network) Individuals have limited characteristics Individuals optimize their goals Limited interaction (communication) rules

5. Complex Adaptive Systems Artificial Neural Networks Agent-based systems (network) Genetic Algorithms Fuzzy logic Fuzzy neural networks

6. ARTIFICIAL NEURAL NETWORKS (ANN) (1) How does the brain operate?

7. 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

8. 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

9. 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

10. FUZZY LOGIC (1) Fuzzy sets and overlapping membership-functions

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

12. 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 WARM THEN FAST STOP MEDIUM FAST 0 SLOW BLAST COLD COOL JUST RIGHT WARM HOT AIR MOTOR SPEED TEMPERATURE IN DEGREES FAHRENHEIT

13. FUZZY LOGIC (4) Fuzzy rules (2)

14. FUZZY LOGIC (5) ADVANTAGES: Smooth behaviour “Human-like” behaviour Natural language approach EXAMPLES: Sendai Subway Trading systems Washing machines, CAM-corders, micro-waves

15. 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

16. GENETIC ALGORITHMS (1)

17. GENETIC ALGORITHMS (2)

18. GENETIC ALGORITHMS (3)

19. GENETIC ALGORITHMS (4)

20. GENETIC ALGORITHMS (5)

21. GENETIC ALGORITHMS (6)

22. GENETIC ALGORITHMS (7)

23. GENETIC ALGORITHMS (8)

24. 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

25. Complexity in economics

26. 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)

27. Summary (until now) Non - linearity Dynamic behavior Dependence on initial conditions Period doubling Existence of attractors Determinism Emergence at the edge of chaos