PhD Proposal: Algorithmic Delegation: from Classic Auctions to Modern Digital Economy

Examining how a principal can effectively delegate tasks to an agent is a central problem in many real-world settings, studied extensively in economics, computer science, and operations research.
Representative examples include a team manager delegating problem-solving to members and a funding agency selecting researchers to advance specific fields.It subsumes a broader class of applications in both the classic economy—such as the public sector delegating trade governance to a self-interested broker—and the modern digital economy, where platforms like YouTube rely on creators for content production.In each case, the principal benefits from the agent’s informational advantage, lacking the expertise or time to decide alone. However, agents often have ulterior motives misaligned with the principal’s, creating a moral hazard where the agent may act strategically for personal gain. The principal’s challenge is to design incentives that align the agent’s behavior with the principal’s objectives.
Fundamental questions in this context are: (i) given a self-interested agent, how can the principal design an efficient mechanism that effectively incentivizes the agent's behavior in favor of the principal, and what are the corresponding approximation guarantees to the first-best (omniscient) outcome? and (ii) if the principal cannot intervene in the agent’s decision, how much worse can the outcome be compared to the first-best outcome in hindsight? I investigate these questions across foundational applications in economics and computer science such as delegated choice problems, auctions, and modern online platforms, cementing theoretical gaps in the literature of algorithmic game theory and providing practical implications.