Learning to Lead: Incentivizing Strategic Agents in the Dark

The principal-agent model (Ross, 1973; Grossman and Hart, 1992; Smith, 2004; Laffont and Martimort, 2009) is a fundamental framework for understanding decision-making processes with misaligned incentives and information asymmetry, with wide applications across various disciplines such as economics, finance, and computer science (Ratliff et al., 2018; Kamenica, 2012). In this model, the principal represents an entity such as a service provider, a policy maker, or a firm, whose objective is to maximize certain system-level outcomes, such as revenue, social welfare, or efficiency. On the other hand, an agent, who could be a customer, an employee, or an individual participant, aims to optimize his utility based on his private preferences or information, which is not directly observable by the principal. To induce the optimal outcomes, the principal designs and commits to a mechanism, which could be a contract, an incentive scheme, or a policy, that aligns the agent's incentives with the principal's objectives. The optimal mechanism and the agent's optimal strategy against it constitute the equilibrium of the principal-agent model, in certain settings also known as the Stackelberg equilibrium (Stackelberg, 1934, 2010).