Study on norm emergence and its stability in conflict situations of heterogeneous agent network society

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We proposed the learning method in which agents produce norm in the society for effective conflict resolution and investigated the characteristics (convergence, stability, etc.) of the norm. For this purpose, we expressed conflict situations using a Markov game with a variety of payoff matrices that indicate agent's attitudes to situations and rewards as the result of behavior. We also represent the conflict situations that are never resolved unless one of agents gives up the best action (so this may result in the temporal penalty). Using our proposed method, we could find that (1) norms could emerge but its efficiency of resolution and stability is strongly affected by the payoff matrices in agents; (2) we often observed the phenomenon in which the established norms are broken by a few anomaly of agents that have strong attitude to conflicts; and finally (3) inconsistent norms often coexist in the agent network whose structure has the small-world property.