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We consider the problem of designing a revenue-maximizing auction for a single item, when the values of the bidders are drawn from a correlated distribution. We observe that there exists an algorithm that finds the optimal randomized…

Computer Science and Game Theory · Computer Science 2015-03-17 Shahar Dobzinski , Hu Fu , Robert Kleinberg

The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with linear utility and single-dimensional preferences, Bulow and Roberts (1989) show…

Computer Science and Game Theory · Computer Science 2014-06-06 Saeed Alaei , Hu Fu , Nima Haghpanah , Jason Hartline

We study revenue maximization in a buyer-seller setting where the seller has a single object and the buyer has both a private valuation and a private budget. Private budgets complicate the classic single-product monopoly problem, making…

Computer Science and Game Theory · Computer Science 2026-04-30 Juan Carlos Carbajal , Ahuva Mualem

We consider a revenue-maximizing seller with a single item for sale to multiple buyers with i.i.d. valuations. Akbarpour and Li (2020) show that the only optimal, credible, strategyproof auction is the ascending price auction with reserves…

Computer Science and Game Theory · Computer Science 2023-10-31 Meryem Essaidi , Matheus V. X. Ferreira , S. Matthew Weinberg

We study probabilistic single-item second-price auctions where the item is characterized by a set of attributes. The auctioneer knows the actual instantiation of all the attributes, but he may choose to reveal only a subset of these…

Computer Science and Game Theory · Computer Science 2013-02-22 Mingyu Guo , Argyrios Deligkas

The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the…

Machine Learning · Computer Science 2016-06-15 Maria-Florina Balcan , Tuomas Sandholm , Ellen Vitercik

We study dynamic mechanisms for optimizing revenue in repeated auctions, that are robust to heterogeneous forward-looking and learning behavior of the buyers. Typically it is assumed that the buyers are either all myopic or are all infinite…

Computer Science and Game Theory · Computer Science 2019-06-11 Shipra Agrawal , Eric Balkanski , Vahab Mirrokni , Balasubramanian Sivan

We consider the design of a revenue-optimal mechanism when two items are available to be sold to a single buyer whose valuation is uniformly distributed over an arbitrary rectangle $[c_1,c_1+b_1]\times[c_2,c_2+b_2]$ in the positive…

Computer Science and Game Theory · Computer Science 2019-03-06 D. Thirumulanathan , Rajesh Sundaresan , Y Narahari

We consider the fundamental problem of designing a truthful single-item auction with the challenging objective of extracting a large fraction of the highest agent valuation as revenue. Following a recent trend in algorithm design, we assume…

Computer Science and Game Theory · Computer Science 2024-01-25 Ioannis Caragiannis , Georgios Kalantzis

We compare the expected efficiency of revenue maximizing (or {\em optimal}) mechanisms with that of efficiency maximizing ones. We show that the efficiency of the revenue maximizing mechanism for selling a single item with k + log_{e/(e-1)}…

Computer Science and Game Theory · Computer Science 2009-06-08 Gagan Aggarwal , Gagan Goel , Aranyak Mehta

Optimal mechanism design enjoys a beautiful and well-developed theory, and also a number of killer applications. Rules of thumb produced by the field influence everything from how governments sell wireless spectrum licenses to how the major…

Computer Science and Game Theory · Computer Science 2014-09-23 Tim Roughgarden

We study the problem of learning revenue-optimal multi-bidder auctions from samples when the samples of bidders' valuations can be adversarially corrupted or drawn from distributions that are adversarially perturbed. First, we prove tight…

Computer Science and Game Theory · Computer Science 2021-07-14 Wenshuo Guo , Michael I. Jordan , Manolis Zampetakis

Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies, however devising mechanisms for auctions with multiple bidders and multiple items can be complicated. It has…

Machine Learning · Computer Science 2023-03-02 Alex Stein , Avi Schwarzschild , Michael Curry , Tom Goldstein , John Dickerson

We consider the problem of designing a revenue-optimal mechanism in the two-item, single-buyer, unit-demand setting when the buyer's valuations, $(z_1, z_2)$, are uniformly distributed in an arbitrary rectangle $[c,c+b_1]\times[c,c+b_2]$ in…

Computer Science and Game Theory · Computer Science 2019-03-06 D. Thirumulanathan , Rajesh Sundaresan , Y Narahari

Many auction settings implicitly or explicitly require that bidders are treated equally ex-ante. This may be because discrimination is philosophically or legally impermissible, or because it is practically difficult to implement or…

Computer Science and Game Theory · Computer Science 2014-11-06 Christos Tzamos , Christopher A. Wilkens

We study the efficiency of simple combinatorial auctions for the allocation of a set of items to a set of agents, with private subadditive valuation functions and budget constraints. The class we consider includes all auctions that allocate…

Computer Science and Game Theory · Computer Science 2020-07-29 Alexandros A. Voudouris

We efficiently solve the optimal multi-dimensional mechanism design problem for independent bidders with arbitrary demand constraints when either the number of bidders is a constant or the number of items is a constant. In the first…

Computer Science and Game Theory · Computer Science 2011-12-20 Constantinos Daskalakis , S. Matthew Weinberg

We study simple and approximately optimal auctions for agents with a particular form of risk-averse preferences. We show that, for symmetric agents, the optimal revenue (given a prior distribution over the agent preferences) can be…

Computer Science and Game Theory · Computer Science 2013-01-04 Hu Fu , Jason Hartline , Darrell Hoy

We consider the problem of repeatedly auctioning a single item to multiple i.i.d buyers who each use a no-regret learning algorithm to bid over time. In particular, we study the seller's optimal revenue, if they know that the buyers are…

Computer Science and Game Theory · Computer Science 2023-07-11 Linda Cai , S. Matthew Weinberg , Evan Wildenhain , Shirley Zhang

In settings where players have a limited access to liquidity, represented in the form of budget constraints, efficiency maximization has proven to be a challenging goal. In particular, the social welfare cannot be approximated by a better…

Computer Science and Game Theory · Computer Science 2013-04-29 Shahar Dobzinski , Renato Paes Leme