Reward Design in Risk-Taking Contests

TL;DR

This paper analyzes how to design optimal reward schemes in risk-taking contests where players decide when to stop a stochastic process, aiming to influence their equilibrium performance and ranking outcomes.

Contribution

It derives optimal reward schemes for principals to shape players' performance, considering both average and rank-specific objectives, revealing novel reward structures.

Findings

Optimal reward schemes depend on the principal's performance goals.

Reward schemes for average performance relate to Lorenz inequality.

Rank-specific rewards can have unexpected, non-monotonic shapes.

Abstract

Following the risk-taking model of Seel and Strack, $n$ players decide when to stop privately observed Brownian motions with drift and absorption at zero. They are then ranked according to their level of stopping and paid a rank-dependent reward. We study the problem of a principal who aims to induce a desirable equilibrium performance of the players by choosing how much reward is attributed to each rank. Specifically, we determine optimal reward schemes for principals interested in the average performance and the performance at a given rank. While the former can be related to reward inequality in the Lorenz sense, the latter can have a surprising shape.