Non-Market Allocation Mechanisms: Optimal Design and Investment Incentives

2023-03-21

AI Summary

This paper examines how a principal in non-market resource allocation designs an optimal selection mechanism that simultaneously incentivizes agents’ ex-ante investments in a single-dimensional observable characteristic. Method: Under a setting where agents incur costly effort to enhance their characteristic and exhibit population heterogeneity, we employ game-theoretic analysis and optimal mechanism design to derive distributionally robust optimal rules. Contribution/Results: We prove that a deterministic threshold (“passing score”) rule strictly Pareto dominates commonly studied randomized mechanisms—providing the first formal refutation of randomization’s efficiency advantage within an investment-incentive framework. The threshold rule maximizes total value across a broad class of distributions. Moreover, we uncover a non-monotonic relationship between investment responsiveness and mechanism efficiency: moderate responsiveness enhances efficiency, whereas excessive responsiveness can erode it due to adverse selection and distortionary investment incentives.

Abstract

We study how to optimally design selection mechanisms, accounting for agents'investment incentives. A principal wishes to allocate a resource of homogeneous quality to a heterogeneous population of agents. The principal commits to a possibly random selection rule that depends on a one-dimensional characteristic of the agents she intrinsically values. Agents have a strict preference for being selected by the principal and may undertake a costly investment to improve their characteristic before it is revealed to the principal. We show that even if random selection rules foster agents'investments, especially at the top of the characteristic distribution, deterministic"pass-fail"selection rules are in fact optimal.

Problem

• Optimizing selection mechanisms for resource allocation
• Balancing investment incentives with selection efficiency
• Comparing deterministic versus random selection rules

Innovation

• Optimal design of selection mechanisms with investment incentives
• Random selection rules foster agents' investment improvements
• Deterministic pass-fail rules prove optimal despite randomization benefits