Stochastic game

About: Stochastic game is a research topic. Over the lifetime, 9493 publications have been published within this topic receiving 202664 citations.

Sophisticated EWA Learning and Strategic Teaching in Repeated Games

Abstract: Most learning models assume players are adaptive (ie, they respond only to their own previous experience and ignore others' payoff information) and behavior is not sensitive to the way in which players are matched Empirical evidence suggests otherwise In this paper, we extend our adaptive experience-weighted attraction (EWA) learning model to capture sophisticated learning and strategic teaching in repeated games The generalized model assumes there is a mixture of adaptive learners and sophisticated players An adaptive learner adjusts his behavior the EWA way A sophisticated player rationally best-responds to her forecasts of all other behaviors A sophisticated player can be either myopic or farsighted A farsighted player develops multiple-period rather than single-period forecasts of others' behaviors and chooses to "teach" the other players by choosing a strategy scenario that gives her the highest discounted net present value We estimate the model using data from p-beauty contests and repeated trust games with incomplete information The generalized model is better than the adaptive EWA model in describing and predicting behavior Including teaching also allows an empirical learning-based approach to reputation formation which predicts better than a quantal-response extension of the standard type-based approach

Approximation methods for infinite Bayesian Stackelberg games: modeling distributional payoff uncertainty

Abstract: Game theory is fast becoming a vital tool for reasoning about complex real-world security problems, including critical infrastructure protection. The game models for these applications are constructed using expert analysis and historical data to estimate the values of key parameters, including the preferences and capabilities of terrorists. In many cases, it would be natural to represent uncertainty over these parameters using continuous distributions (such as uniform intervals or Gaussians). However, existing solution algorithms are limited to considering a small, finite number of possible attacker types with different payoffs. We introduce a general model of infinite Bayesian Stackelberg security games that allows payoffs to be represented using continuous payoff distributions. We then develop several techniques for finding approximate solutions for this class of games, and show empirically that our methods offer dramatic improvements over the current state of the art, providing new ways to improve the robustness of security game models.

Game theory for cyber security

Abstract: While there are significant advances in information technology and infrastructure which offer new opportunities, cyberspace is still far from completely secured. In many cases, the employed security solutions are ad hoc and lack a quantitative decision framework. While they are effective in solving the particular problems they are designed for, they generally fail to respond well in a dynamically changing scenario. To this end, we propose a holistic security approach in this paper. We find that game theory provides huge potential to place such an approach on a solid analytical setting. We consider the interaction between the attacks and the defense mechanisms as a game played between the attacker and the defender (system administrator). In particular, we propose a game theory inspired defense architecture in which a game model acts as the brain. We focus on one of our recently proposed game models, namely imperfect information stochastic game. Although this game model seems to be promising, it also faces new challenges which warrant future attention. We discuss our current ideas on extending this model to address such challenges.