Learning behavior and mixed-strategy Nash equilibria

Abstract

The paper considers whether an adaptive justification, like those commonly available for non-interactive optimization models, can be found for the mixed-strategy Nash equilibrium. Although it is known that such a justification is frequently available for pure-strategy equilibria, it is shown that all members of a wide class of behaviorally plausible learning mechanisms must fail to converge in ‘almost all’ games in which the equilibrium involves mixed strategies. An exact formal analogy is developed, which allows these learning mechanisms to be viewed as discrete taˆtonnement processes in properly chosen competitive exchange economies. In the analogy, the instability derives from the independence of excess demand functions (except at the boundaries) from ‘own’ prices. This independence arises because of the linearity in probabilities of von Neumann-Morgenstern risk preferences, and therefore does not extend to pure-strategy equilibria in general. Thus, assuming that agents will play equilibrium strategies implicitly assumes more sophistication when the equilibrium involves mixed strategies than when it involves only pure strategies.