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Optimal Allocation with Peer Information

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  • Axel Niemeyer
  • Justus Preusser

Abstract

We study allocation problems without monetary transfers where agents hold private information about one another, modeled as a general form of correlated information. Such peer information is relevant in a number of settings, including science funding, allocation of targeted aid, or intra-firm allocation. We characterize optimal dominant-strategy incentive-compatible (DIC) mechanisms using techniques from the theory of perfect graphs. Optimal DIC mechanisms tend to be complex and involve allocation lotteries that cannot be purified without upsetting incentives. In rich type spaces, nearly all extreme points of the set of DIC mechanisms are stochastic. Finding an optimal deterministic DIC mechanism is NP-hard. We propose the simple class of ranking-based mechanisms and show that they are approximately optimal when agents are informationally small. These mechanisms allocate to agents ranked highly by their peers but strategically deny the allocation to agents suspected of having evaluated their peers dishonestly.

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  • Axel Niemeyer & Justus Preusser, 2024. "Optimal Allocation with Peer Information," Papers 2410.08954, arXiv.org.
    • Handle: RePEc:arx:papers:2410.08954
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    References listed on IDEAS

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    Cited by:

    1. Christoph Carnehl & Marco Ottaviani & Justus Preusser, 2024. "Designing Scientific Grants," Papers 2410.12356, arXiv.org.

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