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Approximately efficient two-sided combinatorial auctions

Colini-Baldeschi, R and Goldberg, PW and De Keijzer, B and Leonardi, S and Roughgarden, T and Turchetta, S (2017) Approximately efficient two-sided combinatorial auctions. In: 2017 ACM Conference on Economics and Computation, 2017-06-26 - 2017-06-30, Cambridge, Massachusetts, USA.

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Abstract

We develop and extend a line of recent work on the design of mechanisms for two-sided markets. .e 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 se.ing where buyers have XOS valuations and sellers' valuations are additive. .is is the first such approximation mechanism for a two-sided market se.ing 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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: EC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 19 Dec 2018 10:41
Last Modified: 19 Dec 2018 10:41
URI: http://repository.essex.ac.uk/id/eprint/23663

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