Deep Contract Design via Discontinuous Networks

TL;DR

This paper introduces a novel deep learning approach using Discontinuous ReLU networks to automate and optimize contract design, capturing incentive constraints and utility maximization efficiently.

Contribution

It presents the DeLU network architecture that models discontinuous utility functions and enables scalable, automated contract optimization with minimal training data.

Findings

Successfully approximates principal's utility with few samples

Scales to large action and outcome spaces

Supports efficient optimization via linear programming

Abstract

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. We provide empirical results that demonstrate success in approximating the principal's utility function with a small number of training samples and scaling to find approximately optimal contracts on problems with a large number of actions and outcomes.