Work

Minimizing Regret with Multiple Reserves

Year: 2016

Type: conference-paper

Abstract: We study the problem of computing and learning non-anonymous reserve prices to maximize revenue. We first define the {\sc Maximizing Multiple Reserves (MMR)} problem in single-parameter matroid environments, where the input is $m$ valuation profiles v^1,...,v^m, indexed by the same n bidders, and the goal is to compute the vector r of (non-anonymous) reserve prices that maximizes the total revenue obtained on these profiles by the VCG mechanism with reserves r. We prove that the problem is APX-hard, even in the special case of single-item environments, and give a polynomial-time 1/2-approximation algorithm for it in arbitrary matroid environments.

Source: ACM Conference on Economics and Computation (EC)

DOI: 10.1145/2940716.2940792

Authors: Tim Roughgarden, Joshua R. Wang

Institution: Stanford University

Language: English

Topic: Auction Theory and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: AF: Small: Connections Between Algorithmic Game Theory, Complexity Theory, and Learning Theory, ICES: Small: Frontiers in Mechanism Design