Revisiting the Principal-Agent Problem with Moral Hazard: Theory and Applications
COM2 Level 2
MR3, COM2-02-26
Abstract:
Principal–agent problems with moral hazard study how to design incentive mechanisms when agents take unobservable actions that affect observable outcomes. These problems are central to economics and have become increasingly important in Information Systems and Computer Science. This dissertation revisits the theoretical foundations of these problems and applies the principal–agent framework to study strategic manipulation, such as fake orders, in online commerce.
Principal–agent problems are typically solved by the first-order approach (FOA), which replaces the incentive compatibility constraint in the original problem with its first-order condition, leading to a relaxed problem. The most celebrated condition that validates FOA is the Mirrlees–Rogerson Condition, developed for a risk-averse agent. However, this condition is shown to fail in a widely studied setting with risk-neutral players and limited liability. Instead of requiring FOA to hold globally, a less stringent notion is introduced that requires FOA to be valid only for quota-bonus contracts (FOAVQB). This notion is supported by the result that quota-bonus contracts are optimal for the relaxed problem. Exogenous sufficient conditions are identified that justify FOAVQB, thereby ensuring the optimality of quota-bonus contracts for the original problem. These sufficient conditions are economically interpretable, reasonably easy to verify, and sufficiently flexible to accommodate common environments in which the agent’s expected utility is non-unimodal in effort under optimal contracts, in contrast to the existing literature that typically requires unimodality and concavity of the agent’s utility function.
Managing a sales force and product returns presents a fundamental challenge. While generous return policies attract consumers, they may also create incentives for sales agents to earn commissions through fake orders that are subsequently returned. This concern is particularly acute in online retail environments, such as livestream commerce, where fraudulent orders can be easily placed but difficult to verify. A game-theoretic framework is developed to analyze how firms optimally design compensation and return policies in response to this manipulation risk. When the agent is scrupulous, commissions based on net sales (after returns) are shown to be optimal if return volumes are highly uncertain, whereas commissions based on gross sales (before returns) can be paired with generous return policies otherwise. However, such arrangements may induce manipulation by unscrupulous agents. To eliminate this incentive, several precautionary mechanisms are characterized, including prohibiting returns, compensating based on net sales, or issuing partial refunds. These mechanisms have nuanced implications for pricing and return policy design. Higher salvage values may lead to less generous return policies, while higher consumer valuations may justify lower prices. Furthermore, even when agents can resolve match uncertainty before purchase, return policies may remain optimal if compensation is based on gross sales rather than net sales.