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Optimal allocation with peer information

Author

Listed:
  • Niemeyer, Axel

    (Division of the Humanities and Social Sciences, California Institute of Technology)

  • Preusser, Justus

    (Department of Economics and IGIER, Bocconi University)

Abstract

We study allocation problems without monetary transfers where agents have correlated types, i.e., hold private information about one another. Such peer information is relevant in various settings, including science funding, allocation of targeted aid, or intra-firm allocation. Incentive compatibility requires that agents cannot improve their own allocation by misrepresenting the merits of allocating to others. We characterize optimal incentive-compatible mechanisms using techniques from the theory of perfect graphs. Optimal mechanisms improve on review panels commonly observed in practice by eliciting information directly from eligible agents and by using allocation lotteries to alleviate incentive constraints. Computational hardness results imply that exactly optimal mechanisms are impractically complex. We propose ranking-based mechanisms as a viable alternative and show that they are approximately optimal when agents are informationally small, i.e., when no single agent has information that is crucial for evaluating a large fraction of the other agents.

Suggested Citation

  • Niemeyer, Axel & Preusser, Justus, 0. "Optimal allocation with peer information," Theoretical Economics, Econometric Society.
    • Handle: RePEc:the:publsh:6599
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    References listed on IDEAS

    as
    1. Hart, Sergiu & Nisan, Noam, 2017. "Approximate revenue maximization with multiple items," Journal of Economic Theory, Elsevier, vol. 172(C), pages 313-347.
    2. Axel Niemeyer & Justus Preusser, 2024. "Optimal Allocation with Peer Information," Papers 2410.08954, arXiv.org, revised Mar 2025.
    3. Gul, Faruk & Postlewaite, Andrew, 1992. "Asymptotic Efficiency in Large Exchange Economies with Asymmetric Information," Econometrica, Econometric Society, vol. 60(6), pages 1273-1292, November.
    4. Reshmaan Hussam & Natalia Rigol & Benjamin N. Roth, 2022. "Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field," American Economic Review, American Economic Association, vol. 112(3), pages 861-898, March.
    5. Cremer, Jacques & McLean, Richard P, 1985. "Optimal Selling Strategies under Uncertainty for a Discriminating Monopolist When Demands Are Interdependent," Econometrica, Econometric Society, vol. 53(2), pages 345-361, March.
    6. Deniz Kattwinkel & Axel Niemeyer & Justus Preusser & Alexander Winter, 2022. "Mechanisms without transfers for fully biased agents," Papers 2205.10910, arXiv.org.
    7. Erlanson, Albin & Kleiner, Andreas, 2019. "A note on optimal allocation with costly verification," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 56-62.
    8. Gerardi, Dino & McLean, Richard & Postlewaite, Andrew, 2009. "Aggregation of expert opinions," Games and Economic Behavior, Elsevier, vol. 65(2), pages 339-371, March.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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