We study the robust regulation of incentive contracts. We consider the problem of a regulator choosing what contracts to authorize in the canonical principal-agent model with moral hazard. A firm (she) contracts with a worker (he), who then takes a costly productive action that yields a stochastic output. Hiring the worker imposes a fixed cost for the firm. The set of productive actions and the fixed cost define the firm's technology. The worker's actions being non-contractible, the firm incentivises production by offering an (authorised) contract that maps realised outputs to payments. We assume the firm and the worker are protected by limited liability, they are risk-neutral, and they maximise their profit and surplus. We add a third player to that standard model: the regulator (they). They choose the regulation, which is the set of authorised contracts the firm can offer. We assume that the regulator's payoff is a weighted sum of the firm's profit and the worker's surplus, where the latter has a (weakly) greater weight α ≥ 1. While the firm and the worker know the technology, the regulator has no information. They choose a regulation that minimises their worst-case regret.

How should a principal incentivize an agent to explore risky alternatives when the principal has limited knowledge of these alternatives? We study this question in a robust moral hazard model in which the agent sequentially searches over projects to generate a prize. At the outset, the principal only knows one of these projects, and evaluates contracts by their worst-case performance.