Growing Strategy Sets in Repeated Games

A (pure) strategy in a repeated game is a mapping from histories, or, more generally, signals, to actions. We view the implementation of such a strategy as a computational procedure and attempt to capture in a formal model the following intuition: as the game proceeds, the amount of information (history) to be taken into account becomes large and the \quo{computational burden} becomes increasingly heavy. The number of strategies in repeated games grows double-exponentially with the number of repetitions. This is due to the fact that the number of histories grows exponentially with the number of repetitions and also that we count strategies that map histories into actions in all possible ways. Any model that captures the intuition mentioned above would impose some restriction on the way the set of strategies available at each stage expands. We point out that existing measures of complexity of a strategy, such as the number of states of an automaton that represents the strategy needs to be refined in order to capture the notion of growing strategy space. Thus we propose a general model of repeated game strategies which are implementable by automata with growing number of states with restrictions on the rate of growth. With such model, we revisit some of the past results concerning the repeated games with finite automata whose number of states are bounded by a constant, e.g., Ben-Porath (1993) in the case of two-person infinitely repeated games. In addition, we study an undiscounted infinitely repeated two-person zero-sum game in which the strategy set of player 1, the maximizer, expands \quo{slowly} while there is no restriction on player 2's strategy space. Our main result is that, if the number of strategies available to player 1 at stage $n$ grows subexponentially with $n$, then player 2 has a pure optimal strategy and the value of the game is the maxmin value of the stage game, the lowest payoff that player 1 can guarantee in one-shot game. This result is independent of whether strategies can be implemented by automaton or not. This is a strong result in that an optimal strategy in an infinitely repeated game has, by definition, a property that, for every $c$, it holds player 1's payoff to at most the value plus $c$ after some stageRepeated Games, Complexity, Entropy

Generalized Shapley Values by Simplicial Sampling

Characteristic function representation of n-person cooperative games precludes the modeling of structural properties of a game other than the relationship between coalition structure and the worth of a game. This means that the Shapley value, a measure of expected return to a player from playing the game, is restricted as a solution concept to only those games satisfying the condition that all coalitions of the same cardinality are equiprobable. By contrast, as we demonstrate below, Shapley's three axioms are satisfied for Shapley-like measures based on richer characterizations of a game. In particular, we extend the Shapley value to a class of abstract games for which the roles that players assume are determinants of the likelihood of particular coalitions and for which the original Shapley value can be found as a special case

Integrality gap analysis for bin packing games

A cooperative bin packing game is an $N$-person game, where the player set $N$ consists of $k$ bins of capacity 1 each and $n$ items of sizes $a_1,\dots,a_n$. The value $v(S)$ of a coalition $S$ of players is defined to be the maximum total size of items in $S$ that can be packed into the bins of $S$. We analyze the integrality gap of the corresponding 0–1 integer program of the value $v(N)$, thereby presenting an alternative proof for the non-emptiness of the 1/3-core for all bin packing games. Further, we show how to improve this bound $\epsilon\leq1/3$ (slightly) and point out that the conclusion in Matsui (2000) [9] is wrong (claiming that the bound 1/3 was tight). We conjecture that the true best possible value is $\epsilon=1/7$. The results are obtained using a new “rounding technique” that we develop to derive good (integral) packings from given fractional ones

Cooperative Strategic Games

We examine a solution concept, called the “value," for n-person strategic games. In applications, the value provides an a-priori assessment of the monetary worth of a player's position in a strategic game, comprising not only the player's contribution to the total payoff but also the player's ability to inflict losses on other players. A salient feature is that the value takes account of the costs that “spoilers" impose on themselves. Our main result is an axiomatic characterization of the value. For every subset, S, consider the zero-sum game played between S and its complement, where the players in each of these sets collaborate as a single player, and where the payoff is the difference between the sum of the payoffs to the players in S and the sum of payoffs to the players not in S. We say that S has an effective threat if the minmax value of this game is positive. The first axiom is that if no subset of players has an effective threat then all players are allocated the same amount. The second axiom is that if the overall payoff to the players in a game is the sum of their payoffs in two unrelated games then the overall value is the sum of the values in these two games. The remaining axioms are the strategic-game analogs of the classical coalitional-games axioms for the Shapley value: efficiency, symmetry, and null player

Group polarisation among location-based game players: an analysis of use and attitudes towards game slang

Purpose – This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang. Design/methodology/approach – The authors performed an exploratory data analysis on an original dataset of n 5 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokemon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players’ attitudes towards the word were further probed with a survey (n 5 185). Finally, selected participants (n 5 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes. Findings – The players’ teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation. Practical implications – Game design with static teams and inter-team conflict influences players’ social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base. Originality/value – The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games. Keywords Location-based games, Polarisation, Social identity theory, Language, Slang Paper type Research paper</p

One for all, all for one---von Neumann, Wald, Rawls, and Pareto

Applications of the maximin criterion extend beyond economics to statistics, computer science, politics, and operations research. However, the maximin criterion---be it von Neumann's, Wald's, or Rawls'---draws fierce criticism due to its extremely pessimistic stance. I propose a novel concept, dubbed the optimin criterion, which is based on (Pareto) optimizing the worst-case payoffs of tacit agreements. The optimin criterion generalizes and unifies results in various fields: It not only coincides with (i) Wald's statistical decision-making criterion when Nature is antagonistic, (ii) the core in cooperative games when the core is nonempty, though it exists even if the core is empty, but it also generalizes (iii) Nash equilibrium in $n$-person constant-sum games, (iv) stable matchings in matching models, and (v) competitive equilibrium in the Arrow-Debreu economy. Moreover, every Nash equilibrium satisfies the optimin criterion in an auxiliary game

Reputation and commitment in two-person repeated games without discounting

Two-person repeated games with no discounting are considered where there is uncertainty about the type of the players. If there is a possibility that a player is an automaton committed to a particular pure or mixed stage-game action, then this provides a lower bound on the Nash equilibrium payoffs to a normal type of this player. The lower bound is the best available and is robust to the existence of other types. The results are extended to the case of two-sided uncertainty. This work extends Schmidt (1993) who analyzed the restricted class of conflicting interest games