UCLA

This dissertation comprises three chapters that study information and learning aspects in various game-theoretical models.

The first chapter studies how firms manage their reputation for quality via price-dependent consumer reviews. Pricing decisions are crucial for managing a firm's reputation and maximizing profits. Consumer reviews reflect both the product quality and its price, with more favorable reviews being left when a product is priced lower. We study whether such review behavior can induce a firm to manipulate the review process by underpricing its product, or pricing it below current consumers' willingness to pay. We introduce an equilibrium model with a privately informed firm repeatedly selling its product to uninformed but rational consumers who learn about the quality of the product from past reviews and current prices. We show that underpricing can arise only when the firm reputation is low and then only under a specific condition on consumers' taste shock distribution, which we fully characterize. Rating manipulation unambiguously benefits consumers, because it operates via underpricing.

The second chapter studies how delegated recruitment shapes talent selection. Firms typically pay recruiters via refund contracts, which specify a payment upon the hire of a suggested candidate and a refund if a candidate is hired but terminated during an initial period of employment. We develop a model where refund contracts naturally arise and show that delegation leads to statistical discrimination, where the recruiter favors candidates with more precise productivity information. This is misaligned with direct hiring, where the firm has option value from uncertain candidates. Under tractable parametric assumptions, we characterize the unique equilibrium in which candidates with lower expected productivity but more informative signals (``safe bets") are hired over candidates with higher expected productivity but less informative signals (``diamonds in the rough").

The third chapter studies the efficiency of information aggregation in the DeGroot learning model. We introduce a social planner in the DeGroot model who aims to improve the time asymptotic information aggregation in finite observational networks. We show that in any connected network, it is possible to achieve the best information aggregation by reassigning the attention individuals pay to each others' opinions. We provide an algorithm that constructs a solution to this problem. We also identify the necessary and sufficient condition on the network for achieving the best information aggregation in the average-based updating learning model for homogeneous private signals. Finally, we demonstrate an approach to increasing the speed of learning.