Stochastic discounting in repeated games: awaiting the almost inevitable

This paper studies repeated games with pure strategies and stochastic discounting under perfect information. We consider infinite repetitions of any finite normal form game possessing at least one pure Nash action profile. The period interaction realizes a shock in each period, and the cumulative shocks while not affecting period returns, determine the probability of the continuation of the game. We require cumulative shocks to satisfy the following: (1) Markov property; (2) to have a non-negative (across time) covariance matrix; (3) to have bounded increments (across time) and possess a denumerable state space with a rich ergodic subset; (4) there are states of the stochastic process with the resulting stochastic discount factor arbitrarily close to 0, and such states can be reached with positive (yet possibly arbitrarily small) probability in the long run. In our study, a player’s discount factor is a mapping from the state space to (0, 1) satisfying the martingale property. In this setting, we, not only establish the (subgame perfect) folk theorem, but also prove the main result of this study: In any equilibrium path, the occurrence of any finite number of consecutive repetitions of the period Nash action profile, must almost surely happen within a finite time window. That is, any equilibrium strategy almost surely contains arbitrary long realizations of consecutive period Nash action profiles

The bounded memory folk theorem

We show that the Folk Theorem holds for n-player discounted repeated games with bounded-memory pure strategies. Our result requires each player’s payoff to be strictly above the pure minmax payoff but requires neither time-dependent strategies, nor public randomization, nor communication. The type of strategies we employ to establish our result turn out to have new features that may be important in understanding repeated interactions

Partners or rivals? Strategies for the iterated prisoner's dilemma

Within the class of memory-one strategies for the iterated Prisoner's Dilemma, we characterize partner strategies, competitive strategies and zero-determinant strategies. If a player uses a partner strategy, both players can fairly share the social optimum; but a co-player preferring an unfair solution will be penalized by obtaining a reduced payoff. A player using a competitive strategy never obtains less than the co-player. A player using a zero-determinant strategy unilaterally enforces a linear relation between the two players' payoffs. These properties hold for every strategy used by the co-player, whether memory-one or not

Reactive learning strategies for iterated games

In an iterated game between two players, there is much interest in characterizing the set of feasible payoffs for both players when one player uses a fixed strategy and the other player is free to switch. Such characterizations have led to extortionists, equalizers, partners, and rivals. Most of those studies use memory-one strategies, which specify the probabilities to take actions depending on the outcome of the previous round. Here, we consider "reactive learning strategies," which gradually modify their propensity to take certain actions based on past actions of the opponent. Every linear reactive learning strategy, $\mathbf{p}^{\ast}$, corresponds to a memory one-strategy, $\mathbf{p}$, and vice versa. We prove that for evaluating the region of feasible payoffs against a memory-one strategy, $\mathcal{C}\left(\mathbf{p}\right)$, we need to check its performance against at most $11$ other strategies. Thus, $\mathcal{C}\left(\mathbf{p}\right)$ is the convex hull in $\mathbb{R}^{2}$ of at most $11$ points. Furthermore, if $\mathbf{p}$ is a memory-one strategy, with feasible payoff region $\mathcal{C}\left(\mathbf{p}\right)$, and $\mathbf{p}^{\ast}$ is the corresponding reactive learning strategy, with feasible payoff region $\mathcal{C}\left(\mathbf{p}^{\ast}\right)$, then $\mathcal{C}\left(\mathbf{p}^{\ast}\right)$ is a subset of $\mathcal{C}\left(\mathbf{p}\right)$. Reactive learning strategies are therefore powerful tools in restricting the outcomes of iterated games.Comment: 18 page

Variable Temptations and Black Mark Reputations

In a world of imperfect information, reputations often guide the sequential decisions to trust and to reward trust. We consider two-player situations, where the players meet but once. One player – the truster – decides whether to trust, and the other player – the temptee – has a temptation to betray when trusted. The strength of the temptation to betray may vary from encounter to encounter, and is independently distributed over time and across temptees. We refer to a recorded betrayal as a black mark. We study how trusters and temptees interact in equilibrium when past influences current play only through its effect on certain summary statistics. We first focus on the case that players only condition on the number of black marks of a temptee and study the different equilibria that emerge, depending on whether the trusters, the temptees, or a social planner has the ability to specify the equilibrium. We then show that conditioning on the number of interactions as well as on the number of black marks does not prolong trust beyond black marks alone. Finally, we consider more general summary statistics of a temptee’s past and identify conditions under which there exist equilibria where trust is possibly suspended only temporarily

A non-cooperation result in a repeated discounted prisoners' dilemma with long and short run players

This study presents a modified version of the repeated discounted prisoners' dilemma with long and short-run players. In our setting a short-run player does not observe the history that has occurred before he was born, and survives into next phases of the game with a probability given by the current action profile in the stage game. Thus, even though it is improbable, a short-run player may live and interact with the long-run player for infinitely long amounts of time. In this model we prove that under a mild incentive condition on the stage game payoffs, the cooperative outcome path is not subgame perfect no matter how patient the players are. Moreover with an additional technical assumption aimed to provide a tractable analysis, we also show that payoffs arbitrarily close to that of the cooperative outcome path, cannot be obtained in equilibrium even with patient players