Simple versus Optimal Contracts

TL;DR

This paper analyzes the effectiveness of simple linear contracts in principal-agent models, showing they are worst-case optimal when only the first reward moment is known and providing tight approximation bounds.

Contribution

It introduces a worst-case optimality analysis for linear contracts with limited distributional knowledge, bridging contract theory and theoretical computer science.

Findings

Linear contracts are worst-case optimal with only first moment knowledge.

Several tight bounds on the approximation quality of linear contracts.

Theoretical insights explaining the prevalence of simple contracts.

Abstract

We consider the classic principal-agent model of contract theory, in which a principal designs an outcome-dependent compensation scheme to incentivize an agent to take a costly and unobservable action. When all of the model parameters---including the full distribution over principal rewards resulting from each agent action---are known to the designer, an optimal contract can in principle be computed by linear programming. In addition to their demanding informational requirements, such optimal contracts are often complex and unintuitive, and do not resemble contracts used in practice. This paper examines contract theory through the theoretical computer science lens, with the goal of developing novel theory to explain and justify the prevalence of relatively simple contracts, such as linear (pure commission) contracts. First, we consider the case where the principal knows only the first moment of each action's reward distribution, and we prove that linear contracts are guaranteed to be worst-case optimal, ranging over all reward distributions consistent with the given moments. Second, we study linear contracts from a worst-case approximation perspective, and prove several tight parameterized approximation bounds.