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• W2194229192 abstract "We study the optimization problem faced by a perfectly informed principal in a Bayesian game, who reveals information to the players about the state of nature to obtain a desirable equilibrium. This signaling problem is the natural design question motivated by uncertainty in games and has attracted much recent attention. We present almost-optimal hardness results for signaling problems in (a) Bayesian two-player zero-sum games, and (b) Bayesian network routing games. For Bayesian zero-sum games, when the principal seeks to maximize the equilibrium utility of a player, we show that it is nphard to obtain an FPTAS. Previous results ruled out an FPTAS assuming the hardness of the planted clique problem, which is an average-case assumption about the hardness of recovering a planted clique from an Erdos-R'enyi random graph. Our hardness proof exploits duality and the equivalence of separation and optimization in a novel, unconventional way. Further, we preclude a PTAS assuming planted-clique hardness; previously, a PTAS was ruled out (assuming planted-clique hardness) only for games with quasi-polynomial-size strategy sets. Complementing these, we obtain a PTAS for a structured class of zero-sum games (where signaling is still NP-hard) when the payoff matrices obey a Lipschitz condition. For Bayesian network routing games, wherein the principal seeks to minimize the average latency of the Nash flow, NP-hard to obtain an approximation ratio better than 4/3, even for linear latency functions. This is the optimal inapproximability result for linear latencies, since we show that full revelation achieves a 4/3-approximation for linear latencies." @default.
• W2194229192 created "2016-06-24" @default.
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• W2194229192 date "2015-12-11" @default.
• W2194229192 modified "2023-09-27" @default.
• W2194229192 title "Near-Optimal Hardness Results for Signaling in Bayesian Games." @default.
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