Complexity Education and Training: Enhancing Innovation in Companies

1. EIASM Academic Council Meeting Roberto Serra Modena and Reggio Emilia University

2. CETRA: Complexity Education and TRAining • A EU-Leonardo project • To improve the knowledge of companies (and in particular SME’s) in complex systems concepts and applications • Case studies • Complexity and innovation • Guidelines for courses and curricula • Development and test of selected modules EIASM AC Meeting

3. Self organization emergent properties Description levels Reciprocal causality Positive feedback Path dependency Frozen accidents Networks Tangled hierarchies Self-similarity Universality Adaptation and exaptation Coevolution Edge of chaos Redundancy and degeneracy Key concepts selected in Cetra EIASM AC Meeting

4. The importance of models • Dynamical models • Cellular automata • Agent-based models • Genetic algorithms • Genetic programming • Genetic networks • ... EIASM AC Meeting

5. Tools to solve well-defined technical problems EU-Esprit projects Caboto and Colombo: in-situ bioremediation of contaminated soils. Scale-up from pilot plant to the field (cellular automata model) EIASM AC Meeting

6. Models as conceptual tools to address largely unknown problems • Example: innovation (requires analysis at different levels!) • EU-FET project Iscom (Information Society as a COMplex system) • Modena and Reggio Emilia University (I) • Universitè Paris La Sorbonne (F) • CNRS, Paris (F) • Imperial College (UK) • Models allow us to bridge the gap from a micro-theory to its consequences at a macro-level EIASM AC Meeting

7. Constraints from the theory • transformations occur both in agent and artifact space • => Agents and artifacts are both important • Innovation leads to modifications of the role of agents as well as of the meaning of artifacts • => both must be endogeneously generated • External fitness functions cannot be used here • Directedness (both in artifact and in agent space) • => agents have intentionality EIASM AC Meeting

8. outline of the model • agents use artifacts, produced by other agents, to build other artifacts • Using suitable recipes • Presently, artifacts are represented as numbers and recipes as operators • which can be “sold” to yet other agents, or to an “external world” • agents and artifacts coevolve in order to better exploit the opportunities of their mutual relationships and of the “external” world • the meaning of artifacts is defined by which agents use them, and for what • the role of agents is defined by what they do, and by the other agents with which they interact • Agents can innovate: they identify a new artifact as their goal and try to build the corresponding recipe EIASM AC Meeting

9. Example: heterogeneity • What are the effects of the agent heterogenity? • We can consider different styles of innovation • Innovation rate • Jump frequency and range • Jump identifies an attempt to build something really different from the existent • Homogeneous vs heterogeneous systems EIASM AC Meeting

10. In general, frequent innovators perform better than the others • But a world populated only by large jumpers is very fragile • On the other hand, innovation is very slow in a world populated by small jumpers only • The best results are achieved in the case where both types coexist • A result which was not obvious a priori – it shows that the theory can account for this phenomenon! EIASM AC Meeting

11. Network of artifact types t=350 initial EIASM AC Meeting t=4000