The worst-case payoff in games with stochastic revision opportunities

We study infinitely repeated games in which players are limited to subsets of their action space at each stage—a generalization of asynchronous games. This framework is broad enough to model many real-life repeated scenarios with restrictions, such as portfolio management, learning by doing and training. We present conditions under which rigidity in the choice of actions benefits all players in terms of worst-case equilibrium payoff and worst-case payoff. To provide structure, we exemplify our result in a model of a two-player repeated game, where we derive a formula for the worst-case payoff. Moreover, we show that in zero-sum games, lack of knowledge about the timing of the revision can compensate for inability to change the action.