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• W3165905482 abstract "In this article, we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain, and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of <i>conditional mutual information</i> to evaluate the information being shared among the agents. The challenges that arise due to the interdependence of agents&#x2019; information structure and decision making are exhibited. For the finite horizon game, we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite-horizon case by devising appropriate punishment strategies, which are defined over the agents&#x2019; <i>beliefs</i> on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these <i>equilibrium regions</i>, prove uniqueness of a <i>maximal</i> equilibrium region, and devise an algorithm for its approximate computation." @default.
• W3165905482 created "2021-06-22" @default.
• W3165905482 creator A5008804386 @default.
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• W3165905482 date "2022-04-01" @default.
• W3165905482 modified "2023-09-26" @default.
• W3165905482 title "Dynamic Information Sharing and Punishment Strategies" @default.
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• W3165905482 doi "https://doi.org/10.1109/tac.2021.3087652" @default.
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