Subgame Maxmin Strategies in Zero-Sum Stochastic Games with Tolerance Levels

We study subgame $$\phi $$ -maxmin strategies in two-player zero-sum stochastic games with a countable state space, finite action spaces, and a bounded and universally measurable payoff function. Here, $$\phi $$ denotes the tolerance function that assigns a nonnegative tolerated error level to every subgame. Subgame $$\phi $$ -maxmin strategies are strategies of the maximizing player that guarantee the lower value in every subgame within the subgame-dependent tolerance level as given by $$\phi $$ . First, we provide necessary and sufficient conditions for a strategy to be a subgame $$\phi $$ -maxmin strategy. As a special case, we obtain a characterization for subgame maxmin strategies, i.e., strategies that exactly guarantee the lower value at every subgame. Secondly, we present sufficient conditions for the existence of a subgame $$\phi $$ -maxmin strategy. Finally, we show the possibly surprising result that each game admits a strictly positive tolerance function $$\phi ^*$$ with the following property: if a player has a subgame $$\phi ^*$$ -maxmin strategy, then he has a subgame maxmin strategy too. As a consequence, the existence of a subgame $$\phi $$ -maxmin strategy for every positive tolerance function $$\phi $$ is equivalent to the existence of a subgame maxmin strategy.