Combinatorial Contracts

TL;DR

This paper introduces a model of combinatorial contracts where a principal incentivizes an agent to perform a costly, unobservable task with a success probability function, analyzing computational complexity and optimality conditions.

Contribution

It establishes polynomial-time algorithms for optimal contracts under gross substitutes success probabilities and proves NP-hardness under submodular functions, extending results to linear contracts.

Findings

Optimal contracts are computable in polynomial time for gross substitutes functions.

NP-hardness of finding optimal contracts when success probability is submodular.

Robust optimality of linear contracts under first moment constraints.

Abstract

We introduce a new model of combinatorial contracts in which a principal delegates the execution of a costly task to an agent. To complete the task, the agent can take any subset of a given set of unobservable actions, each of which has an associated cost. The cost of a set of actions is the sum of the costs of the individual actions, and the principal's reward as a function of the chosen actions satisfies some form of diminishing returns. The principal incentivizes the agents through a contract, based on the observed outcome. Our main results are for the case where the task delegated to the agent is a project, which can be successful or not. We show that if the success probability as a function of the set of actions is gross substitutes, then an optimal contract can be computed with polynomially many value queries, whereas if it is submodular, the optimal contract is NP-hard. All our results extend to linear contracts for higher-dimensional outcome spaces, which we show to be robustly optimal given first moment constraints. Our analysis uncovers a new property of gross substitutes functions, and reveals many interesting connections between combinatorial contracts and combinatorial auctions, where gross substitutes is known to be the frontier for efficient computation.