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I propose an equilibrium solution concept in which players sequentially sample to resolve strategic uncertainty over their opponents’ distribution of actions. Bayesian players sample from their opponents’ distribution of actions at a cost and make optimal choices given their posterior beliefs. The solution concept makes predictions on the joint distribution of players’ choices, beliefs, and decision times, and generates stochastic choice through the randomness inherent to sampling, without relying on indifference or choice mistakes. It rationalizes well-known deviations from Nash equilibrium such as the own-payoff effect and I show its novel predictions relating choices, beliefs, and decision times are supported by existing data.
