Adaptation And Evolution in Collective Systems (Advances in Natural Computation)
Adaptation And Evolution in Collective Systems (Advances in Natural Computation)
Individual and Social Learning
Computational Economics
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Using a game-theoretic model combined with the evolutionary model, we investigate the conditions under which the desirable interaction rules will evolve and sustain in various social interaction settings. The direction of the research to come is to understand how the interaction structure, the network topology, determines the path of evolutionary dynamics. For the emergence of desirable outcomes at the macroscopic level, the patterns of social interaction are critical. We find that the efficient and fair outcome emerges relatively quickly in symmetric networks where each agent plays the game with the same number of players. In symmetric networks, agents appear to easily recognize the possibility of a coordinated turn-taking behaviour or alternating reciprocity as a means to generate an efficient and fair outcome.