Collective Learning in Social Coordination Games

Akira Namatame and Hiroshi Sato

An important aspect of collective intelligence is the learning strategy adapted by each agent. An interesting problem which has been widely investigated is under what circumstances will agents converge to some particular equilibrium? We consider the networks of agents, in which each agent faces strategic decision problems. We investigate the dynamics of collective decision when each agent adapts the strategy of interaction to its, neighbors. We are interested in to show how the society gropes its way towards an equilibrium situation. We show that the society selects the most efficient equilibrium among multiple equilibria when the agents composing it do learn from each other as collective learning, and they co-evolve their strategies over time. The possibility of mutual learning allows the socially optimality where the model with random matching may result in inefficiency.

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