Journal ArticleDOI
Applications of α-Strongly Regular Distributions to Bayesian Auctions
Richard Cole,Shravas Rao +1 more
- Vol. 5, Iss: 4, pp 18
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This article investigates five distinct auction settings for which good expected revenue bounds are known when the bidders’ valuations are given by MHR distributions, and shows that these bounds degrade gracefully when extended to α-SR distributions.Abstract:
Two classes of distributions that are widely used in the analysis of Bayesian auctions are the monotone hazard rate (MHR) and regular distributions. They can both be characterized in terms of the rate of change of the associated virtual value functions: for MHR distributions, the condition is that for values vread more
Citations
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Proceedings ArticleDOI
Prior-Independent Optimal Auctions
Amine Allouah,Omar Besbes +1 more
TL;DR: This work studies the design of optimal prior-independent selling mechanisms, in which the seller faces buyers whose values are drawn from an unknown distribution, and only knows that the distribution belongs to a particular class.
Profit maximization in mechanism design: beyond the bayesian approach
Anna R. Karlin,Cam Thach Nguyen +1 more
TL;DR: In this paper, the problem of designing truthful mechanisms to maximize a seller's profit without prior knowledge of buyers' valuations has been studied in a variety of settings that go beyond the traditional Bayesian approach from economics.
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The Quantitative View of Myerson Regularity
Nikolaus Schweizer,Nora Szech +1 more
TL;DR: In this article, the concept of λ-regularity is introduced to measure how Myerson regular a distribution is, i.e., the ratio between revenue and welfare, or sales probabilities may vanish at the boundary of myerson regularity.
Journal ArticleDOI
Performance bounds for optimal sales mechanisms beyond the monotone hazard rate condition
Nikolaus Schweizer,Nora Szech +1 more
TL;DR: In this article, the authors explore performance bounds for sales mechanisms that follow from a quantitative version of Myerson regularity, which they call λ -regularity, and demonstrate the usefulness of λ-regularity for quantitative mechanism design by proving various performance bounds.
Posted Content
Robust Revenue Maximization Under Minimal Statistical Information.
TL;DR: The tight analysis for the deterministic case resolves an open gap from the work of Azar and Micali and shows how one can directly use the upper bounds to improve and extend previous results related to the parametric auctions ofAzar et al. [SODA'13].
References
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Optimal Auction Design
TL;DR: Optimal auctions are derived for a wide class of auction design problems when the seller has imperfect information about how much the buyers might be willing to pay for the object.
Journal ArticleDOI
Aggregation and imperfect competition: on the existence of equilibrium
Andrew Caplin,Barry Nalebuff +1 more
TL;DR: In this paper, the authors present an approach to the theory of imperfect competition and apply it to study price competition among differentiated products, including products with multi-dimen-sional attributes.
Proceedings ArticleDOI
Optimal marketing strategies over social networks
TL;DR: This work identifies a family of strategies called influence-and-exploit strategies that are based on the following idea: Initially influence the population by giving the item for free to carefully a chosen set of buyers, then extract revenue from the remaining buyers using a 'greedy' pricing strategy.
Journal ArticleDOI
Aggregation and social choice: a mean voter theorem
Andrew Caplin,Barry Nalebuff +1 more
TL;DR: This article provided conditions under which the mean voter's most preferred outcome is unbeatable according to a 64%-majority rule. But this result does not extend to elections in which candidates differ in more than one dimension.
Proceedings ArticleDOI
The sample complexity of revenue maximization
Richard Cole,Tim Roughgarden +1 more
TL;DR: In this article, the authors consider a single-item auction where bidders' valuations are drawn independently from unknown and non-identical distributions and the seller is given m samples from each of these distributions "for free" and chooses an auction to run on a fresh sample.