Approximate Revenue Maximization with Multiple Items

Sergiu Hart and Noam Nisan

Papers from arXiv.org

Abstract: Maximizing the revenue from selling _more than one_ good (or item) to a single buyer is a notoriously difficult problem, in stark contrast to the one-good case. For two goods, we show that simple "one-dimensional" mechanisms, such as selling the goods separately, _guarantee_ at least 73% of the optimal revenue when the valuations of the two goods are independent and identically distributed, and at least $50\%$ when they are independent. For the case of $k>2$ independent goods, we show that selling them separately guarantees at least a $c/\log^2 k$ fraction of the optimal revenue; and, for independent and identically distributed goods, we show that selling them as one bundle guarantees at least a $c/\log k$ fraction of the optimal revenue. Additional results compare the revenues from the two simple mechanisms of selling the goods separately and bundled, identify situations where bundling is optimal, and extend the analysis to multiple buyers.

Date: 2012-04, Revised 2017-12
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Citations: View citations in EconPapers (37)

Published in Journal of Economic Theory 172 (2017), 313-347

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http://arxiv.org/pdf/1204.1846 Latest version (application/pdf)

Related works:
Journal Article: Approximate revenue maximization with multiple items (2017)
Working Paper: Approximate Revenue Maximization with Multiple Items (2012)
Working Paper: Approximate Revenue Maximization with Multiple Items (2012)
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