 Playing games with bounded entropyMehrdad Valizadeh and Amin Gohari Games and Economic Behavior, 2019, vol. 115, issue C, 363-380 Abstract: We study zero-sum repeated games in which the maximizer (player or team) is restricted to strategies with limited randomness. Particularly, we analyze the maxmin payoff of the maximizer in two models: the first model forces the maximizer to randomize her actions just by conditioning them to the outcomes of an observed random source. In the second model, the maximizer is a team of players who are free to privately randomize their corresponding actions but do not have access to any explicit source of shared randomness needed for coordination. While prior works adopted the method of types to address these problems, we use the idea of random hashing being the core of randomness extractors. In addition, we use a tool for simulation of a source from another source. Utilizing these tools, we simplify and generalize the earlier results. We also study the computational aspects of the solution for the first model. Keywords: Repeated games; Bounded entropy; Randomness extraction; Source simulation; Entropy minimization; Information theory (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:115:y:2019:i:c:p:363-380 DOI: 10.1016/j.geb.2019.03.013 Access Statistics for this article Games and Economic Behavior is currently edited by E. Kalai More articles in Games and Economic Behavior from Elsevier |
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