Hi, I'm Tyler Maxey,

a Research Director at Capital Preferences and volunteer math instructor at San Quentin.

ConFortact

I received my Ph.D. in Economics from Princeton University in 2023 and my B.S. in Industrial Engineering and Operations Research from UC Berkeley in 2018. Go Bears!

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Publications

"School Choice with Costly Information Acquisition"

Games and Economic Behavior

Abstract: I study a model of centralized school choice in which students engage in costly search over schools before submitting preference reports to a clearinghouse. I consider three classes of preferences over schools---idiosyncratic, common, and hybrid---and characterize outcomes under two search protocols---simultaneous and sequential. With idiosyncratic preferences, there are no search externalities, and inefficiencies arise only because of uncoordinated search. Common preferences, however, generate search externalities: when high-priority students search, seats available to lower-priority students are adversely selected. Consequently, sequential search generates greater welfare than simultaneous search with idiosyncratic preferences but not necessarily with common. Additionally, with common preferences, welfare is nonmonotonic in search costs. I also show that the search protocol affects outcome inequality in important ways. For both protocols, I provide an instrument by which a designer can break students’ indifferences in search strategies to coordinate search and increase welfare.

"Dynamic Matching with Transfers"

Economics Letters

Abstract: I study a two-sided market of dynamic matching with sequential arrivals. Agents trade off waiting to match with a more desirable agent with immediately matching with a less desirable agent. I study the consequences of transfer availability for two protocols: first-in-first-out (FIFO) and last-in-first-out (LIFO). For FIFO, welfare is weakly greater with transfers than without. For LIFO, there is a range of waiting costs such that welfare is weakly lower with transfers than without. For sufficiently low waiting costs, the equilibrium welfare for FIFO with transfers remains strictly lower than socially optimal. Thus, unlike in a static matching setting, transfers in my dynamic matching setting do not generate utilitarian efficient outcomes.

"When is Anarchy Beneficial?" (with Hakjin Chung, Hyun-Soo Ahn, and Rhonda Righter)

ACM Sigmetrics—MAMA, 2017

Abstract: In many service systems, customers acting to maximize their individual utility (selfish customers) will result in a policy that does not maximize their overall utility; this effect is known as the Price of Anarchy (PoA). More specifically, the PoA, defined to be the ratio of selfish utility (the overall average utility for selfish customers) to collective utility (the overall average utility if customers act to maximize their overall average utility) is generally less than one. Of course, when the environment is fixed, the best case PoA is one, by definition of the maximization problem. However, we show that in systems with feedback, where the environment may change depending on customer behavior, there can be a Benefit of Anarchy, i.e., we can have a PoA that is strictly larger than one. We give an example based on a Stackelberg game between a service provider and customers in a single-server queue.

Working Paper

"Estimating Losses from Online Piracy"

Working Paper

Abstract: The digital release of media on streaming platforms has made it relatively easy to upload and find online pirated version of platforms’ content, resulting in potentially large revenue losses. To assess losses from piracy, I merge novel title-level data on pirated and legal viewing for 660 titles on two popular streaming platforms (Netflix and Disney+) from January 2021 to June 2022. I combine this with subscription data to estimate a structural model of demand for titles and platforms that accounts for households’ heterogeneous disutilites of piracy. I estimate that roughly 11% of U.S. households are willing to pirate though there are substantial disutilities to doing so. During the period I study, pirates lowered platforms’ revenues by 0.3%, equal to about $70 million. Through my counterfactual analysis, I find that making piracy 25% less appealing would have generated only $33 million in displaced revenues while making piracy 25% more appealing would have generated $146 million in displaced revenues. This suggests that slightly easing the policing of piracy could have substantial effects.