Learning in Games by Random Sampling
Abstract We study repeated interactions among a fixed set of “low rationality” players who have status quo actions, randomly sample other actions, and change their status quo if the sampled action yields a higher payoff. This behavior generates a random process, the better-reply dynamics. Long run behavior leads to Nash equilibrium in games with the weak finite improvement property, including finite, supermodular games and generic, continuous, two-player, quasi-concave games. If players make mistakes and if several players can sample at the same time, the resulting better-reply dynamics with simultaneous sampling converges to the Pareto optimal Nash equilibrium in common interest games. Journal of Economic Literature Classification Numbers: C70, C72, C73.