7,802 research outputs found

    How much can we identify from repeated games?

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    I propose a strategy to identify structural parameters in infinitely repeated games without relying on equilibrium selection assumptions. Although Folk theorems tell us that almost any individually rational payoff can be an equilibrium payoff for sufficiently patient players, Folk theorems also provide tools to explicitly characterize this set of payoffs. I exploit the extreme points of this set to bound unobserved equilibrium continuation payoffs and then use these to generate informative bounds on structural parameters. I illustrate the identification strategy using (1) an infinitely repeated Prisoner's dilemma to get bounds on a utility parameter, and (2) an infinitely repeated quantity-setting game to get bounds on marginal cost and provide a robust test of firm conduct

    How much can we identify from repeated games?

    Get PDF
    I propose a strategy to identify structural parameters in infinitely repeated games without relying on equilibrium selection assumptions. Although Folk theorems tell us that almost any individually rational payoff can be an equilibrium payoff for sufficiently patient players, Folk theorems also provide tools to explicitly characterize this set of payoffs. I exploit the extreme points of this set to bound unobserved equilibrium continuation payoffs and then use these to generate informative bounds on structural parameters. I illustrate the identification strategy using (1) an infinitely repeated Prisoner's dilemma to get bounds on a utility parameter, and (2) an infinitely repeated quantity-setting game to get bounds on marginal cost and provide a robust test of firm conduct

    Disappearing private reputations in long-run relationships

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    For games of public reputation with uncertainty over types and imperfect public monitoring, Cripps et al. [Imperfect monitoring and impermanent reputations, Econometrica 72 (2004) 407–432] showed that an informed player facing short-lived uninformed opponents cannot maintain a permanent reputation for playing a strategy that is not part of an equilibrium of the game without uncertainty over types. This paper extends that result to games in which the uninformed player is long-lived and has private beliefs, so that the informed player's reputation is private. The rate at which reputations disappear is uniform across equilibria and reputations also disappear in sufficiently long discounted finitely repeated games

    Experience-weighted Attraction Learning in Normal Form Games

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    In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter δ that weights the strength of hypothetical reinforcement of strategies that were not chosen according to the payoff they would have yielded, relative to reinforcement of chosen strategies according to received payoffs. The other key features are two discount rates, φ and ρ, which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, and hybridizes their key elements. When δ= 0 and ρ= 0, cumulative choice reinforcement results. When δ= 1 and ρ=φ, levels of reinforcement of strategies are exactly the same as expected payoffs given weighted fictitious play beliefs. Using three sets of experimental data, parameter estimates of the model were calibrated on part of the data and used to predict a holdout sample. Estimates of δ are generally around .50, φ around .8 − 1, and ρ varies from 0 to φ. Reinforcement and belief-learning special cases are generally rejected in favor of EWA, though belief models do better in some constant-sum games. EWA is able to combine the best features of previous approaches, allowing attractions to begin and grow flexibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief-based models implicitly do

    Why and How Identity Should Influence Utility

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    This paper provides an argument for the advantage of a preference for identity-consistent behaviour from an evolutionary point of view. Within a stylised model of social interaction, we show that the development of cooperative social norms is greatly facilitated if the agents of the society possess a preference for identity consistent behaviour. As cooperative norms have a positive impact on aggregate outcomes, we conclude that such preferences are evolutionarily advantageous. Furthermore, we discuss how such a preference can be integrated in the modelling of utility in order to account for the distinctive cooperative trait in human behaviour and show how this squares with the evidence

    Learning and innovative elements of strategy adoption rules expand cooperative network topologies

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    Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference
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