2012/07/11 by Daniel Reeves, Reeves, Daniel, Michael P. Wellman +1 · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Auction Theory and Applications #Bayesian game #Best response #Class (philosophy) #Combinatorial game theory #Common value auction #Complete information #Computer Science and Game Theory (cs.GT) #Computer science #Extensive-form game #FOS: Computer and information sciences #Fictitious play #Game Theory and Applications #Game Theory and Voting Systems #Game theory #Iterated function #Mathematical economics #Mathematical optimization #Mathematics #Nash equilibrium #Piecewise linear function #Sequential game #Variety (cybernetics) #cs.GT
paper · pdf · doi:10.48550/arxiv.1207.4171
published in arXiv (Cornell University) (Cornell University) · Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
arxiv created 2012/07/11 · openalex publication_date 2012/07/11 · arxiv updated 2012/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We describe an algorithm for computing best response strategies in a class of two-player infinite games of incomplete information, defined by payoffs piecewise linear in agents' types and actions, conditional on linear comparisons of agents' actions. We show that this class includes many well-known games including a variety of auctions and a novel allocation game. In some cases, the best-response algorithm can be iterated to compute Bayes-Nash equilibria. We demonstrate the efficiency of our approach on existing and new games.