Optimal Allocation with Peer Information

TL;DR

This paper investigates optimal allocation mechanisms in settings with peer information, proposing ranking-based approaches that are approximately optimal under certain informational conditions.

Contribution

It characterizes optimal incentive-compatible mechanisms using perfect graph theory and introduces practical ranking-based mechanisms as efficient approximations.

Findings

Optimal mechanisms improve review panel processes.

Ranking-based mechanisms are approximately optimal for small informational agents.

Computational complexity limits exact optimal mechanism implementation.

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.