_:n21f71838e43942f38d198d09f388ebdexb36
<http://schema.org/description>
"We introduce a new model of combinatorial contracts in which a principal\ndelegates the execution of a costly task to an agent. To complete the task, the\nagent can take any subset of a given set of unobservable actions, each of which\nhas an associated cost. The cost of a set of actions is the sum of the costs of\nthe individual actions, and the principal's reward as a function of the chosen\nactions satisfies some form of diminishing returns. The principal incentivizes\nthe agents through a contract, based on the observed outcome.\n  Our main results are for the case where the task delegated to the agent is a\nproject, which can be successful or not. We show that if the success\nprobability as a function of the set of actions is gross substitutes, then an\noptimal contract can be computed with polynomially many value queries, whereas\nif it is submodular, the optimal contract is NP-hard. All our results extend to\nlinear contracts for higher-dimensional outcome spaces, which we show to be\nrobustly optimal given first moment constraints.\n  Our analysis uncovers a new property of gross substitutes functions, and\nreveals many interesting connections between combinatorial contracts and\ncombinatorial auctions, where gross substitutes is known to be the frontier for\nefficient computation."
<https://api.deepai.org/profile/thomas-kesselheim>