A multi-agent system integrating a learning process applied to Financial market

摘要

This paper presents a Multi-agent System (MAS) applied model to design a financial market system. The key Impact of an agent approach is that the behavior of the whole system can be understood by fragmenting it into many small autonomous and interacting units and by specifying the behavior of each unit. The agents actions in their environment are inductive. It means the market agents are allowed to adapt and learn from their environment by recording the success and the failure of their past actions. It is shown in the paper how agents make or loose money according to their ability for learning from their environment. An interesting result concerns agent oriented simulation in social systems where various issues can be investigated to study the emergent dynamic of the system and understand more about the relationship between micro-levels specification of individual entities and the macro level emergent behavior of the whole system. Our experimental results show clearly emergent features like the mass effect and the correlation appearing between the number of agents and their enrichment's speed and performance in the market.