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Truthful Matching with Online Items and Offline Agents

Authors Michal Feldman , Federico Fusco , Simon Mauras , Rebecca Reiffenhäuser

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Author Details

Michal Feldman
  • Blavatnik School of Computer Science, Tel Aviv University, Israel
  • Microsoft Research, Herzliya, Israel
Federico Fusco
  • Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Italy
Simon Mauras
  • Blavatnik School of Computer Science, Tel Aviv University, Israel
Rebecca Reiffenhäuser
  • Institute for Logic, Language and Computation, University of Amsterdam, The Netherlands

Funding

Michal Feldman was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 866132) and by the NSF-BSF (grant number 2020788). Federico Fusco and Rebecca Reiffenhäuser were supported by the ERC Advanced Grant 788893 AMDROMA “Algorithmic and Mechanism Design Research in Online Markets” and MIUR PRIN grant Algorithms, Games, and Digital Markets (ALGADIMAR). Federico Fusco was also partially supported by PNRR MUR project PE0000013-FAIR” and PNRR MUR project IR0000013-SoBigData.it. Part of the work of Federico Fusco was done while visiting Michal Feldman at Tel Aviv University.

Acknowledgements

The authors are grateful to Amos Fiat and Stefano Leonardi for many useful conversations that have tremendously contributed to this paper.

Cite AsGet BibTex

Michal Feldman, Federico Fusco, Simon Mauras, and Rebecca Reiffenhäuser. Truthful Matching with Online Items and Offline Agents. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 58:1-58:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICALP.2023.58

Abstract

We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multi-parameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. Unlike most online matching settings, where agents arrive online, in our setting the items arrive online in an adversarial order while the buyers are present for the entire duration of the process. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds. We provide an almost full picture of the competitive ratios in different scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments, and private vs. public desired sets. Among other results, we identify the frontier up to which the celebrated e/(e-1) competitive ratio for the vertex-weighted online matching of Karp, Vazirani and Vazirani extends to truthful agents and online items.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
  • Applied computing → Online auctions
Keywords
  • Online matching
  • Karp-Vazirani-Vazirani
  • truthfulness

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