Optimal allocation with peer information

Axel Niemeyer () and Justus Preusser ()
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Axel Niemeyer: Division of the Humanities and Social Sciences, California Institute of Technology
Justus Preusser: Department of Economics and IGIER, Bocconi University

Theoretical Economics, Forthcoming

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.

Keywords: Mechanism design without transfers; correlated types; peer information; extreme points; informational size; perfect graphs; peer selection (search for similar items in EconPapers)
JEL-codes: D71 D82 (search for similar items in EconPapers)
Date: 2026-08-18
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