Learning Approximately Optimal Contracts

TL;DR

This paper introduces an algorithm for principals to learn approximately optimal contracts in principal-agent models without prior knowledge of the agent's utility or action space, ensuring near-optimal profit with limited samples.

Contribution

It proposes a novel learning algorithm for optimal contracts in uncertain principal-agent settings, with guarantees on sample complexity and robustness to risk preferences.

Findings

Algorithm achieves near-optimal contracts with bounded samples

Results hold even for risk-averse agents

Special cases recover classical optimal contracts

Abstract

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agent's utility and action space: she sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal to obtain a contract that is within $\eps$ of her optimal net profit for every $\eps>0$. Our results are robust even when considering risk-averse agents. Furthermore, we show that when there are only two possible outcomes or the agent is risk-neutral, the algorithm's outcome approximates the optimal contract described in the classical theory.