Learning Cooperative Games

This paper explores a PAC (probably approximately correct) learning model in cooperative games. Specifically, we are given $m$ random samples of coalitions and their values, taken from some unknown cooperative game; can we predict the values of unseen coalitions? We study the PAC learnability of several well-known classes of cooperative games, such as network flow games, threshold task games, and induced subgraph games. We also establish a novel connection between PAC learnability and core stability: for games that are efficiently learnable, it is possible to find payoff divisions that are likely to be stable using a polynomial number of samples.Comment: accepted to IJCAI 201

The shared assignment game and applications to pricing in cloud computing

ABSTRACT We propose an extension to the Assignment Gam

Reliability Weighted Voting Games

Abstract. We examine agent failures in weighted voting games. In our coopera-tive game model, R-WVG, each agent has a weight and a survival probability, and the value of an agent coalition is the probability that its surviving members would have a total weight exceeding a threshold. We propose algorithms for comput-ing the value of a coalition, finding stable payoff allocations, and estimating the power of agents. We provide simulation results showing that on average the sta-bility level of a game increases as the failure probabilities of the agents increase. This conforms to several recent results showing that failures increase stability in cooperative games