Practical mechanisms for platforms in modern markets
Abstract/Contents
- Abstract
- The emergence of modern online marketplaces has certainly disrupted the way we trade goods and services. It has eliminated the need for intermediaries in numerous value chains and driven the growth and prosperity of the gig economy. Endowing (professional and nonprofessional) suppliers with the capability to directly participate in the market has allowed online platforms to flourish with ever-more diverse sets of listings. In the absence of other intermediaries, these platforms have taken an increasingly critical role in facilitating trades. Online platforms seek to create competitive markets where buyers can easily navigate through the wide sea of listings. Meanwhile, market mechanisms utilized in these marketplaces must be simple and transparent so that anyone can comfortably understand and trust them. Motivated by these challenges, I leverage techniques from game theory, optimization, algorithm design, and Bayesian statistics to better design practical market mechanisms for platforms in modern markets. In particular, I study how to optimally design assortment mechanisms---deciding which of the listings are showcased to buyers---for online marketplaces in practice. The first chapter of my dissertation looks at the mechanism design problem of finding an optimal pay-as-bid mechanism in which a platform chooses an assortment of suppliers in order to balance the trade-off between two objectives: providing enough variety to accommodate heterogeneous buyers, yet at low prices. The second chapter of my dissertation investigates the effect of quality uncertainty on the choice of ranking mechanisms in settings that mimic online consumer-to-consumer marketplaces.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Choi, Je-ok |
|---|---|
| Degree supervisor | Saban, Daniela |
| Degree supervisor | Weintraub, Gabriel |
| Thesis advisor | Saban, Daniela |
| Thesis advisor | Weintraub, Gabriel |
| Thesis advisor | Johari, Ramesh, 1976- |
| Degree committee member | Johari, Ramesh, 1976- |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Je-ok Choi. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/fq882cb9157 |
Access conditions
- Copyright
- © 2022 by Je-ok Choi
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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