Approximate Revenue Maximization with Multiple Items

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.

Presented in ACM EC conference, 2012

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• The Better Half of Selling Separately
• Selling Two Identical Objects
• Simple Mechanisms for Profit Maximization in Multi-item Auctions
• Obtaining Costly Unverifiable Valuations from a Single Agent
• Selling a Single Item with Negative Externalities
• Third-Party Data Providers Ruin Simple Mechanisms
• Revenue Maximization of Airbnb Marketplace using Search Results
• A Note on the Ratio of Revenues Between Selling in a Bundle and Separately
• Approximately Maximizing the Broker's Profit in a Two-sided Market
• Costly Multidimensional Screening
• Optimal Pricing Schemes for an Impatient Buyer
• Optimal (and Benchmark-Optimal) Competition Complexity for Additive Buyers over Independent Items
• Revenue Maximization for Selling Multiple Correlated Items
• The Randomized Communication Complexity of Randomized Auctions