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CORMAS : A multiagent simulation toolkit to model natural and social dynamics at multiple scales

Le Page C., Bousquet F., Bakam I., Bah A., Baron C.. 2000. Wageningen : Resource Modeling Association, p. 1-20. International Conference of the Resource Modeling Association 2000 : The ecology of scales, 2000-06-26/2000-06-30, Wageningen (Pays-Bas).

Dealing with multiple scales is often a key question in renewable resources management. In some cases, the decision to incorporate a spatial entity is influenced by the fact that information is available at this level. In other cases, the system dynamics is intrinsically linked to a specific spatial entity, which should obviously be taken into account in the model. Nevertheless, it is important to have the possibility to manipulate and to incorporate into the same model spatial entities defined at different hierarchical levels. Originated from the field of Distributed Artificial Intelligence, Multi-Agent Systems (MAS) are potentially suitable for linking several hierarchical levels. In a MAS, an agent is a computerized autonomous entity that is able to act locally in response to stimuli from the environment or to communication with other agents. Cormas (Common-pool Resources and Multi-Agent Systems) is a multi-agent simulation platform specially designed for renewable resource management. It provides the framework for building models of interactions between individuals and groups sharing natural resources. With Cormas, the design of the spatial support rests on spatial entities, which are themselves a category of agents. When these entities yield resources, they are competent to arbitrate their allocation, according to pre-defined protocols, between concurrent demands formulated by other agents exploiting these resources. The way agents are exploiting resources may depend on their own partial representation of the environment, which are based on these same spatial entities. Following a general overview of the Cormas simulation platform, examples of models built by using this toolkit are presented, by emphasizing the overlapping of their multiple hierarchical scales. Finally, the use of multi-agent systems to represent knowledge on processes at various levels of complexity and to simulate their interactions according to a bottom-up approach for understanding landscape dynamics are discussed.

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