Principal Agent

Principal-agent theory examines the challenges of aligning the incentives of a principal (e.g., a company) with those of an agent (e.g., an employee) when information is asymmetric. Current research focuses on dynamic settings, often modeled using Markov Decision Processes (MDPs) and reinforcement learning, to design optimal contracts or incentive schemes that maximize the principal's utility while accounting for the agent's strategic behavior. These models are being applied to diverse areas such as algorithmic contract design, online learning, and multi-agent systems, offering insights into efficient resource allocation and improved decision-making in complex interactions. The resulting algorithms and theoretical frameworks have implications for various fields, including economics, computer science, and operations research.