Truthful Matching with Online Items and Offline Agents
Document Open Access Logo

Truthful Matching with Online Items and Offline Agents

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

PDF
Thumbnail PDF

File

LIPIcs.ICALP.2023.58.pdf
  • Filesize: 0.77 MB
  • 20 pages

Document Identifiers

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 As Get 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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Gagan Aggarwal, Gagan Goel, Chinmay Karande, and Aranyak Mehta. Online vertex-weighted bipartite matching and single-bid budgeted allocations. In SODA, pages 1253-1264. SIAM, 2011. Google Scholar
  2. Susan Athey and Ilya Segal. An efficient dynamic mechanism. Econometrica, 81(6):2463-2485, 2013. Google Scholar
  3. Yossi Azar and Ety Khaitzin. Prompt mechanism for ad placement over time. In International Symposium on Algorithmic Game Theory SAGT, pages 19-30. Springer, 2011. Google Scholar
  4. Moshe Babaioff, Liad Blumrosen, and Aaron Roth. Auctions with online supply. Games Econ. Behav., 90:227-246, 2015. Google Scholar
  5. Dirk Bergemann and Juuso Välimäki. Dynamic mechanism design: An introduction. Journal of Economic Literature, 57(2):235-74, June 2019. Google Scholar
  6. Benjamin Birnbaum and Claire Mathieu. On-line bipartite matching made simple. Acm Sigact News, 39(1):80-87, 2008. Google Scholar
  7. Constantine Caramanis, Paul Dütting, Matthew Faw, Federico Fusco, Philip Lazos, Stefano Leonardi, Orestis Papadigenopoulos, Emmanouil Pountourakis, and Rebecca Reiffenhäuser. Single-sample prophet inequalities via greedy-ordered selection. In SODA, pages 1298-1325. SIAM, 2022. Google Scholar
  8. Richard Cole, Shahar Dobzinski, and Lisa Fleischer. Prompt mechanisms for online auctions. In International Symposium on Algorithmic Game Theory, pages 170-181. Springer, 2008. Google Scholar
  9. Yuan Deng, Debmalya Panigrahi, and Hanrui Zhang. Online combinatorial auctions. In SODA, pages 1131-1149. SIAM, 2021. Google Scholar
  10. Nikhil R. Devanur, Kamal Jain, and Robert D. Kleinberg. Randomized primal-dual analysis of RANKING for online bipartite matching. In SODA, pages 101-107. SIAM, 2013. Google Scholar
  11. Paul Dütting, Federico Fusco, Philip Lazos, Stefano Leonardi, and Rebecca Reiffenhäuser. Efficient two-sided markets with limited information. In STOC, pages 1452-1465. ACM, 2021. Google Scholar
  12. Alon Eden, Michal Feldman, Amos Fiat, and Kineret Segal. An economics-based analysis of RANKING for online bipartite matching. In SOSA, pages 107-110. SIAM, 2021. Google Scholar
  13. Tomer Ezra, Michal Feldman, Nick Gravin, and Zhihao Gavin Tang. Online stochastic max-weight matching: Prophet inequality for vertex and edge arrival models. In EC, pages 769-787. ACM, 2020. Google Scholar
  14. Uriel Feige. Tighter bounds for online bipartite matching. In Building Bridges II, pages 235-255. Springer, 2019. Google Scholar
  15. Buddhima Gamlath, Sagar Kale, and Ola Svensson. Beating greedy for stochastic bipartite matching. In SODA, pages 2841-2854. SIAM, 2019. Google Scholar
  16. Buddhima Gamlath, Michael Kapralov, Andreas Maggiori, Ola Svensson, and David Wajc. Online matching with general arrivals. In FOCS, pages 26-37. IEEE Computer Society, 2019. Google Scholar
  17. Nick Gravin, Zhihao Gavin Tang, and Kangning Wang. Online stochastic matching with edge arrivals. In ICALP, volume 198 of LIPIcs, pages 74:1-74:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. Google Scholar
  18. Richard M. Karp, Umesh V. Vazirani, and Vijay V. Vazirani. An optimal algorithm for on-line bipartite matching. In STOC, pages 352-358. ACM, 1990. Google Scholar
  19. Thomas Kesselheim, Klaus Radke, Andreas Tönnis, and Berthold Vöcking. An optimal online algorithm for weighted bipartite matching and extensions to combinatorial auctions. In ESA, volume 8125 of Lecture Notes in Computer Science, pages 589-600. Springer, 2013. Google Scholar
  20. Nitish Korula and Martin Pál. Algorithms for secretary problems on graphs and hypergraphs. In ICALP (2), volume 5556 of Lecture Notes in Computer Science, pages 508-520. Springer, 2009. Google Scholar
  21. Piotr Krysta and Berthold Vöcking. Online mechanism design (randomized rounding on the fly). In ICALP (2), volume 7392 of Lecture Notes in Computer Science, pages 636-647. Springer, 2012. Google Scholar
  22. Aranyak Mehta. Online matching and ad allocation. Found. Trends Theor. Comput. Sci., 8(4):265-368, 2013. Google Scholar
  23. Vahab S. Mirrokni, Renato Paes Leme, Pingzhong Tang, and Song Zuo. Non-clairvoyant dynamic mechanism design. In EC, page 169. ACM, 2018. Google Scholar
  24. Roger B Myerson. Optimal auction design. Mathematics of operations research, 6(1):58-73, 1981. Google Scholar
  25. Rebecca Reiffenhäuser. An optimal truthful mechanism for the online weighted bipartite matching problem. In SODA, pages 1982-1993. SIAM, 2019. Google Scholar
  26. Tim Roughgarden. Twenty lectures on algorithmic game theory. Cambridge University Press, 2016. Google Scholar
  27. William Vickrey. Counterspeculation, auctions, and competitive sealed tenders. The Journal of finance, 16(1):8-37, 1961. Google Scholar
  28. Xiangzhong Xiang. Prompt mechanism for online auctions with multi-unit demands. Journal of Combinatorial Optimization, 30(2):335-346, 2015. Google Scholar
  29. Andrew Chi-Chih Yao. Probabilistic computations: Toward a unified measure of complexity (extended abstract). In FOCS, pages 222-227. IEEE Computer Society, 1977. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact