Essays in Market Design

Citation

Fernandez, Marcelo Ariel (2018) Essays in Market Design. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/PXYF-WS15. https://resolver.caltech.edu/CaltechTHESIS:05312018-141046982

Abstract

This thesis investigates the impact of incomplete information and behavioral biases in the context of market design.

In chapter 2, I analyze centralized matching markets and rationalize why the arguably most heavily used mechanism in applications, the deferred acceptance mechanism, has been so successful in practice, despite the fact that it provides participants with opportunities to “game the system.” Accounting for the lack of information that participants typically have in these markets in practice, I introduce a new notion of behavior under uncertainty that captures participants’ aversion to experience regret. I show that participants optimally choose not to manipulate the deferred acceptance mechanism in order to avoid regret. Moreover, the deferred acceptance mechanism is the unique mechanism within an interesting class (quantile stable) to induce honesty from participants in this way.

In chapter 3, co-authored with Leeat Yariv, we study the impacts of incomplete information on centralized one-to-one matching markets. We focus on the commonly used deferred acceptance mechanism (Gale and Shapley, 1962). We characterize settings in which many of the results known when information is complete are overturned. In particular, small (complete-information) cores may still be associated with multiple outcomes and incentives to misreport, selection of equilibria can affect the set of individuals who are unmatched—i.e., there is no analogue for the Rural Hospital Theorem, and agents might prefer to be on the receiving side of the of the algorithm underlying the mechanism. Nonetheless, when either side of the market has assortative preferences, incomplete information does not hinder stability, and results from the complete-information setting carry through.

In chapter 4, co-authored with Tatiana Mayskaya, we present a dynamic model that illustrates three forces that shape the effect of overconfidence (overprecision of consumed information) on the amount of collected information. The first force comes from overestimating the precision of the next consumed piece of information. The second force is related to overestimating the precision of already collected information. The third force reflects the discrepancy between how much information the agent expects to collect and how much information he actually collects in expectation. The first force pushes an overconfident agent to collect more information, while the second and the third forces work in the other direction. We show that under some symmetry conditions, the second and third force unequivocally dominate the first, leading to underinvestment in information.

Thesis Files