Online Contract Design With Unknown Technology

*Hidden-action principal-agent problems* model scenarios in which a principal induces an agent to take a costly and *unobservable* action through the provision of outcome-dependent payments. These problems find application in a variety of real-world settings, such as crowdsourcing, online labor platforms, and machine learning task delegation. Recently, much of the literature has focused on how to handle the principal’s *uncertainty* about the agent and the surrounding environment, which is often the main challenge in practice. One prominent approach is to adopt an *online learning* framework, where the principal repeatedly interacts with the agent to learn optimal payments from experience. However, existing learning algorithms, while achieving regret that scales sublinearly in the number of interaction rounds $T$, typically suffer from an exponential dependence on the size of the problem instance. In this paper, we show that this problematic exponential growth can be avoided by assuming that the principal has knowledge of a set of possible actions of the agent, while remaining unaware of which actions are actually available---an assumption that is reasonable in many real-world settings.