A Simple and Approximately Optimal Mechanism for a Buyer with Complements

Author:

Eden Alon12,Feldman Michal34,Friedler Ophir3ORCID,Talgam-Cohen Inbal5ORCID,Weinberg S. Matthew26

Affiliation:

1. Computer Science Department, Harvard University, Cambridge, Massachusetts 02139;

2. Simons Institute for the Theory of Computing, Berkeley, California 94720;

3. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel;

4. Microsoft Research, Herzliya 4672513, Israel;

5. Computer Science Department, Technion, Haifa 3200003, Israel;

6. Computer Science Department, Princeton University, Princeton, New Jersey 08540

Abstract

Recent literature on approximately optimal revenue maximization has shown that in settings where agent valuations for items are complement free, the better of selling the items separately and bundling them together guarantees a constant fraction of the optimal revenue. However, most real-world settings involve some degree of complementarity among items. The role that complementarity plays in the trade-off of simplicity versus optimality has been an obvious missing piece of the puzzle. In “A Simple and Approximately Optimal Mechanism for a Buyer with Complements,” the authors show that the same simple selling mechanism—the better of selling separately and as a grand bundle—guarantees a $\Theta(d)$ fraction of the optimal revenue, where $d$ is a measure of the degree of complementarity. One key modeling contribution is a tractable notion of “degree of complementarity” that admits meaningful results and insights—they demonstrate that previous definitions fall short in this regard.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Computer Science Applications

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms;Operations Research;2023-12-13

2. Simplicity in Auctions Revisited: The Primitive Complexity;Proceedings of the 24th ACM Conference on Economics and Computation;2023-07-07

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