Linear Contract Design with Predictions

We study the problem of contract design with predictions, where a principal delegates decision-making to an agent and aims to induce the agent to take actions that lead to outcomes in favor of the principal. The principal designs a contract that ties compensation to these outcomes. Faced with the contract, the agent chooses an action to maximizes his utility. While it is common that only the agent knows his action’s cost and the outcome distribution induced by his action, we consider a special case where the principal has access to prediction of this information. The principal's challenge in contract design is to pursue both consistency, i.e., achieving near-optimal performance if the prediction is highly accurate, and robustness, i.e., maintaining performance under inaccurate prediction. Our main contribution is the development of two algorithms that satisfy both consistency and robustness, along with the technical methodologies employed.