자료유형  

 학위논문

Control Number  

0017161147

International Standard Book Number  

9798382742991

Dewey Decimal Classification Number  

330.9

Main Entry-Personal Name  

Monk, Kyle.

Publication, Distribution, etc. (Imprint  

[S.l.] : Georgetown University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

159 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Rust, John.

Dissertation Note  

Thesis (Ph.D.)--Georgetown University, 2024.

Summary, Etc.  

요약This dissertation explores the economics of search algorithms deployed by platforms. Chapters 1 and 2 address challenges in evaluating market power associated with algorithms. Chapter 3 explores the mechanisms that platforms use to alter search outcomes when their access to user data changes.In Chapter 1, I develop a model of search based on competition over algorithms that can be used to evaluate the market power of an algorithm that aids a consumer's search process. In the model, platforms make an endogenous choice about how much their algorithms should favor consumers' preferences versus the platform's profitability-per-consumer. This decision is modeled as a choice to set a distance between realized, consumer-optimal, and platform-optimal search outcomes. Finally, a general framework to apply this model across platform types is presented. In Chapter 2, the model is applied to JD.com e-commerce data. First, I estimate a discrete choice model of consumer purchase decisions given their search outcomes. Then, I estimate the structural search model. I find that the algorithms achieve an outcome almost identical to the outcomes that maximize the platform's profitability-per-user. The finding suggests one of two possibilities: JD.com has tremendous market power in the market studied, or JD.com is using a suboptimal ranking algorithm that maximizes profits-per-consumer rather than total profits. Realigning the incentives of the platform and consumers by banning first-party participation on e-commerce platforms would improve the market outcomes for consumers and sellers, however, it would decrease the platform's profitability-per-consumer by a larger magnitude. In Chapter 3, I calibrate a structural model of consumer search that allows the platform's beliefs about the consumer to be incorrect. The model estimates the weights the platform places on user preferences and expected profits when choosing to display search results. I estimate this model separately for JD.com shoppers that the platform possesses predictive data about and for those without this data. Then, I simulate search and purchase outcomes for the shoppers with predictive data as if this data did not exist. Finally, I decompose the effects into those from price discrimination, those from personalized steering, and those from changes in market power.

Index Term-Uncontrolled  

Algorithms

Index Term-Uncontrolled  

Digital economics

Index Term-Uncontrolled  

Industrial organization

Index Term-Uncontrolled  

Market power

Index Term-Uncontrolled  

Personalized pricing

Added Entry-Corporate Name  

Georgetown University Economics

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

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Control Number  

joongbu:656404