The Optimal Sample Complexity of Linear Contracts

1. 1Apply the Empirical Utility Maximization algorithm to learn optimal linear contracts
2. 2Analyze the agent types drawn from an unknown distribution
3. 3Design a contract that maximizes the principal's expected utility
4. 4Test the contract using offline data
5. 5Configure the algorithm to yield an ε-approximation of the optimal linear contract
6. 6Run simulations to evaluate the performance of the contract

Data scientists and AI engineers on a team can benefit from this knowledge to improve contract design and utility maximization, while product managers can apply these insights to inform business strategy

💡 The Empirical Utility Maximization algorithm can yield an ε-approximation of the optimal linear contract with high probability

💡 Optimal linear contracts can be learned using Empirical Utility Maximization algorithm