Date of Award

Spring 1-1-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Economics

First Advisor

Samuelson, Larry

Abstract

This dissertation consists of three papers on microeconomic theory. The first paper studies repeated zero-sum games with incomplete information.In contrast to the canonical setting of Aumann and Maschler (1995), the paper assumes that the uninformed player is a sequence of short–lived players. When monitoring of past actions is perfect, Aumann and Maschler’s (1995) “ Cav u”-result extends. When monitoring is imperfect, the payoff of the informed player can be strictly higher when facing a sequence of short–lived players than in the canonical setting, depending on parameters. The paper provides a partial characterization of equilibrium payoffs when monitoring is imperfect. The second paper, coauthored with Amirreza Ahmadzadeh, studies how tocombine screening menus and inspection in mechanism design. A Principal procures a good from an Agent whose cost is his private information. The Principal has two instruments: screening menus —- i.e., quantities and transfers –– and (ex-ante) inspection. Inspection is costly but reveals the Agent’s cost. The combination of inspection and screening menus mitigates inefficiencies: the optimal mechanism procures an efficient quantity from all Agents whose cost of production is sufficiently low, regardless of whether inspection has taken place. However, quantity distortions still necessarily occur in optimal regulation; the quantity procured from Agents with higher production costs is inefficiently low. A cost report triggers inspection only if the quantity procured from Agents at the reported cost is inefficiently low. In contrast to settings without inspection, incentive compatibility constraints never bind locally, but only globally. Nonetheless, the paper characterizes which incentive constraints bind. The third paper investigates the impact of artificial intelligence on the interactionbetween firms and consumers. It focuses on the use of learning algorithms in environments with strategic consumers — where learning must occur in the face of consumers who best-respond and adapt their behavior. An algorithm is transparent if consumers observe its inputs, whereas it is opaque if consumers do not observe its inputs. The main results are as follows. First, opaque algorithms perform better for the firm than transparent ones. In contrast to a transparent algorithm, an opaque algorithm learns the optimal policy and maximizes long-run profits. Second, opaque algorithms outperform transparent ones in terms of consumer welfare in important applications. That is, consumers may benefit from having less information about the algorithm’s inputs. Third, whether the firm benefits from using an algorithm instead of behaving strategically depends on consumers’ information about the algorithm’s inputs. When the algorithm is opaque, it yields higher payoffs than a fully strategic firm.

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