Essays on artificial intelligence in personalized markets
Abstract/Contents
- Abstract
- Personalized markets have become ubiquitous in recent years. Matching platforms such as Uber or Airbnb, social networks like Facebook and Twitter, online marketplaces such as Amazon, all provide individualized experiences to different users based on their unique characteristics and preferences. Analyzing these markets in depth requires a multidisciplinary approach. In particular, it requires a closer relation between Economic Theory and Artificial Intelligence. In this work I'll present two examples that interlink both disciplines in order to analyze these markets. Chapter 1 presents a novel algorithm which we call PBDM that personalizes the BDM mechanism, introduced by Becker, DeGroot, and Marschack. The BDM mechanism has been recently used as a treatment assignment mechanism in order to estimate the treatment effects of policy interventions while simultaneously measuring the demand for the intervention. In this work, we develop a personalized extension of the classic BDM mechanism using modern machine learning algorithms to predict an individual's willingness to pay. This lowers the cost for experimenters, provides better balance over covariates that are correlated with both the outcome and willingness to pay, and eliminates biases induced by ad-hoc boundaries in the classic BDM algorithm. Chapter 2 covers an exercise to estimate the economic value of data in algorithms with an application to ride-sharing. We present a novel approach to estimating an upper bound for the economic value of data due to its role in algorithms. Our method does not assume that users have failed to internalize any costs in data production (such as privacy), and show that the price of data is in great part determined by the power dynamics present in markets. We apply our method to ride-sharing by simulating a market using data from a large ride-sharing platform (Uber). We estimate that in our scenario, with users having full market power, data would contribute up to 47% of Uber's revenue. This would translate to average payments to drivers of up to approximately $30 per day, solely as compensation for the value of the data they generate as drivers, which corresponds to 20 to 40 percent of a average driver's daily earnings. Most of the increase would be absorbed by Uber. However, depending on the conditions of the ride-sharing market, these payments could be passed on to riders through rate increases.
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Arrieta Ibarra, Imanol |
|---|---|
| Degree supervisor | Ugander, Johan |
| Thesis advisor | Ugander, Johan |
| Thesis advisor | Goel, Sharad, 1977- |
| Thesis advisor | Johari, Ramesh, 1976- |
| Degree committee member | Goel, Sharad, 1977- |
| Degree committee member | Johari, Ramesh, 1976- |
| Associated with | Stanford University, Department of Management Science and Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Imanol Arrieta Ibarra. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Imanol Arrieta Ibarra
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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