Deep Contract Design via Discontinuous Networks

Understand

Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent.

• In this paper, we initiate the study of deep learning for the automated design of optimal contracts.
• We introduce a novel representation: the Discontinuous ReLU (DeLU) network, which models the principal's utility as a discontinuous piecewise affine function of the design of a contract where each piece corresponds to the agent taking a particular action.
• DeLU networks implicitly learn closed-form expressions for the incentive compatibility constraints of the agent and the utility maximization objective of the principal, and support parallel inference on each piece through linear programming or interior-point methods that solve for optimal contracts.