Abstract
We model repeated play of noncooperative stage games in terms of approximate gradient steps. That simple format requires little information and no optimization. Moreover, it allows players to evaluate marginal cost or profit inexactly and to move with different velocities. Uncertainty can also be accommodated. Granted some crucial stability, we show that play converges to Nash equilibrium.
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Flåm, S.D. Learning Equilibrium Play: A Myopic Approach. Computational Optimization and Applications 14, 87–102 (1999). https://doi.org/10.1023/A:1008709129421
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DOI: https://doi.org/10.1023/A:1008709129421