
Learning Equilibria of SimulationBased Games
We tackle a fundamental problem in empirical gametheoretic analysis (EG...
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A General Framework for Endowment Effects in Combinatorial Markets
The endowment effect, coined by Nobel Laureate Richard Thaler, posits th...
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Memoryless Algorithms for the Generalized kserver Problem on Uniform Metrics
We consider the generalized kserver problem on uniform metrics. We stud...
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Computing Equilibria of Prediction Markets via Persuasion
We study the computation of equilibria in prediction markets in perhaps ...
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Counteracting Inequality in Markets via Convex Pricing
We study market mechanisms for allocating divisible goods to competing a...
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Sharp indistinguishability bounds from nonuniform approximations
We study the problem of distinguishing between two symmetric probability...
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How to Sell Hard Information
The seller of an asset has the option to buy hard information about the ...
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Learning Competitive Equilibria in Noisy Combinatorial Markets
We present a methodology to robustly estimate the competitive equilibria (CE) of combinatorial markets under the assumption that buyers do not know their precise valuations for bundles of goods, but instead can only provide noisy estimates. We first show tight lower and upperbounds on the buyers' utility loss, and hence the set of CE, given a uniform approximation of one market by another. We then develop a learning framework for our setup, and present two probablyapproximatelycorrect algorithms for learning CE, i.e., producing uniform approximations that preserve CE, with finitesample guarantees. The first is a baseline that uses Hoeffding's inequality to produce a uniform approximation of buyers' valuations with high probability. The second leverages a connection between the first welfare theorem of economics and uniform approximations to adaptively prune value queries when it determines that they are provably not part of a CE. We experiment with our algorithms and find that the pruning algorithm achieves better estimates than the baseline with far fewer samples.
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