Individual evolutionary learning with many agents

Abstract

Individual Evolutionary Learning (IEL) is a learning model based on the evolution of a population of strategies of an individual agent. In prior work, IEL has been shown to be consistent with the behavior of human subjects in games with a small number of agents. In this paper, we examine the performance of IEL in games with many agents. We find IEL to be robust to this type of scaling. With the appropriate linear adjustment of the mechanism parameter, the convergence behavior of IEL in games induced by Groves–Ledyard mechanisms in quadratic environments is independent of the number of participants.

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