MANAGING SPATIAL SELF-ORGANIZATION VIA COLLECTIVE BEHAVIORS

Spatial self-organizations appear in many natural and artificial systems. Spatial systems creation and development, called morphogenesis, is the subject of many research studies since many years (2). Fractal computation approach is, for exemple, one of the methods proposed to deal with such studies. But, even if this method is able to describe unlimited local formations on multi-scale descriptions, the formation process itself is described in a global way. The goal of this paper is to introduce the distributed and decentralized computing as a general methodology to propose emergent spatial formation, able to deal with local perturbations and with non homegeneous formation rules. Both multi-criteria and multi-center systems modelling are available in the simulation processes proposed here, based on two study cases. The first study case deals with interacting population over an environment based on a regular grid. The second study case deals with intelligent swarms over an environment based on both, a stigmergic space and a geographical information system. The confrontation of these two study cases reveals the singularities of each of them and contributes to better understand the decentralized based algorithms leading to spatial emergence of self-organizations. SPATIAL MORPHOLOGY MODELLING ON THE EDGE OF COMPLEXITY The study of spatial morphology is a major aspect of the understanding of many phenomena for natural or artificial systems. Living systems or social systems, for example, are systems where the spatial formation has a high meaning and modifies deeply by itself the system evolution. The system evolution leads to modify itself the spatial formations by feed-back processes. Spatial morphology models can be classified by many criteria. Some of these models are static (finding the optimal shape of some problem) or dynamic (morphogenesis, for example). When the models involve dynamical processes, these dynamics can be expressed in a global way, like we do using partial differential equations: the objective of the system description consists in describing the different phenomena involved (diffusion, transport, ...). Spatial morphology systems can be involved inside a multi-scale processus, giving some specific properties to this multi-scale formation, like the development of important exchange area. Fractal systems are wellknowned to model these multi-scale systems: capacit de stockage d’air dans les poumons, fractal shape of plants, etc. Even if these fractal geometry are able to model multi-scale description, they are generally completly deterministic and not suitable to describe geometrical evolution or to be able to integrate local disturbations. For this purpose, we need to change the model concept and go from global and deterministic models to decentralized approaches where the whole system is only knwon by the interaction systems of behavior population.