Approximately Efficient Two-Sided Combinatorial Auctions

Author:

Colini-Baldeschi Riccardo1,Goldberg Paul W.2,Keijzer Bart de3,Leonardi Stefano4,Roughgarden Tim5,Turchetta Stefano6

Affiliation:

1. Facebook Core Data Science, London, UK

2. University of Oxford, UK

3. University of Essex, Amsterdam, Netherlands

4. Sapienza, University of Rome, Roma, Italy

5. Stanford University, USA

6. NTT Data and University of Oxford, UK

Abstract

We develop and extend a line of recent work on the design of mechanisms for two-sided markets. The markets we consider consist of buyers and sellers of a number of items, and the aim of a mechanism is to improve the social welfare by arranging purchases and sales of the items. A mechanism is given prior distributions on the agents’ valuations of the items, but not the actual valuations; thus, the aim is to maximise the expected social welfare over these distributions. As in previous work, we are interested in the worst-case ratio between the social welfare achieved by a truthful mechanism and the best social welfare possible. Our main result is an incentive compatible and budget balanced constant-factor approximation mechanism in a setting where buyers have XOS valuations and sellers’ valuations are additive. This is the first such approximation mechanism for a two-sided market setting where the agents have combinatorial valuation functions. To achieve this result, we introduce a more general kind of demand query that seems to be needed in this situation. In the simpler case that sellers have unit supply (each having just one item to sell), we give a new mechanism whose welfare guarantee improves on a recent one in the literature. We also introduce a more demanding version of the strong budget balance (SBB) criterion, aimed at ruling out certain “unnatural” transactions satisfied by SBB. We show that the stronger version is satisfied by our mechanisms.

Funder

MIUR PRIN project ALGADIMAR “Algorithms, Games, and Digital Markets”

NSF

ERC

Google Focused Award on “Algorithms for Large-scale Data Analysis”

Publisher

Association for Computing Machinery (ACM)

Subject

Computational Mathematics,Marketing,Economics and Econometrics,Statistics and Probability,Computer Science (miscellaneous)

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5. Liad Blumrosen and Shahar Dobzinski. 2016. (Almost) efficient mechanisms for bilateral trading. ArXiv/CoRR abs/1604.04876 (2016). Liad Blumrosen and Shahar Dobzinski. 2016. (Almost) efficient mechanisms for bilateral trading. ArXiv/CoRR abs/1604.04876 (2016).

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