Abstract
This paper presents an agent-based computer simulation of equilibrium in some games that do not have dominant strategy equilibria. The agents learn optimal strategies via a flexible learning algorithm somewhat based on simulated annealing. As in the dominant-strategy cases, agents adapt flexibly but imperfectly to changing population learning, partly through fluctuations in the resist.
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