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Multiagent Mechanism Design Without Money

Author

Listed:
  • Santiago R. Balseiro

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Huseyin Gurkan

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • Peng Sun

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

We consider a principal repeatedly allocating a single resource in each period to one of multiple agents, whose values are private, without relying on monetary payments over an infinite horizon with discounting. We design a dynamic mechanism that induces agents to report their values truthfully in each period via promises/threats of future favorable/unfavorable allocations. We show that our mechanism asymptotically achieves the first-best efficient allocation (the welfare-maximizing allocation as if values are public) as agents become more patient and provide sharp characterizations of convergence rates to first best as a function of the discount factor. In particular, in the case of two agents we prove that the convergence rate of our mechanism is optimal—that is, no other mechanism can converge faster to first best.

Suggested Citation

  • Santiago R. Balseiro & Huseyin Gurkan & Peng Sun, 2019. "Multiagent Mechanism Design Without Money," Operations Research, INFORMS, vol. 67(5), pages 1417-1436, September.
    • Handle: RePEc:inm:oropre:v:67:y:2019:i:5:p:1417-1436
    DOI: opre.2018.1820
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    References listed on IDEAS

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    Cited by:

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    10. Manitra A. Rakotoarisoa, 2023. "Optimal mechanism of transfer payments to cost-varying firms," SN Business & Economics, Springer, vol. 3(11), pages 1-23, November.
    11. Julien Combe & Vladyslav Nora & Olivier Tercieux, 2021. "Dynamic assignment without money: Optimality of spot mechanisms," Working Papers 2021-11, Center for Research in Economics and Statistics.
    12. Damien Berriaud & Ezzat Elokda & Devansh Jalota & Emilio Frazzoli & Marco Pavone & Florian Dorfler, 2024. "To Spend or to Gain: Online Learning in Repeated Karma Auctions," Papers 2403.04057, arXiv.org, revised Feb 2025.
    13. Parshan Pakiman & Selvaprabu Nadarajah & Negar Soheili & Qihang Lin, 2025. "Self-Guided Approximate Linear Programs: Randomized Multi-Shot Approximation of Discounted Cost Markov Decision Processes," Management Science, INFORMS, vol. 71(4), pages 3384-3404, April.
    14. Liang, Yong & Sun, Peng & Tang, Runyu & Zhang, Chong, 2023. "Efficient resource allocation contracts to reduce adverse events," Other publications TiSEM 0bcf44d9-d0ac-4231-beaf-8, Tilburg University, School of Economics and Management.
    15. Saša Zorc & Ilia Tsetlin & Sameer Hasija & Stephen E. Chick, 2025. "The When and How of Delegated Search," Operations Research, INFORMS, vol. 73(1), pages 461-482, January.
    16. Moise Blanchard & Patrick Jaillet, 2024. "Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers," Papers 2408.10066, arXiv.org.
    17. Ezzat Elokda & Carlo Cenedese & Kenan Zhang & Andrea Censi & John Lygeros & Emilio Frazzoli & Florian Dörfler, 2025. "CARMA: Fair and Efficient Bottleneck Congestion Management via Nontradable Karma Credits," Transportation Science, INFORMS, vol. 59(2), pages 340-359, March.
    18. Ezzat Elokda & Saverio Bolognani & Andrea Censi & Florian Dorfler & Emilio Frazzoli, 2022. "A self-contained karma economy for the dynamic allocation of common resources," Papers 2207.00495, arXiv.org, revised May 2023.
    19. Vahideh Manshadi & Rad Niazadeh & Scott Rodilitz, 2023. "Fair Dynamic Rationing," Management Science, INFORMS, vol. 69(11), pages 6818-6836, November.
    20. Santiago R. Balseiro & Vahab Mirrokni & Renato Paes Leme & Song Zuo, 2022. "Dynamic Double Auctions: Toward First Best," Operations Research, INFORMS, vol. 70(4), pages 2299-2317, July.
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    24. Th`anh Nguyen & Alexander Teytelboym & Shai Vardi, 2023. "Dynamic Combinatorial Assignment," Papers 2303.13967, arXiv.org.

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