Learning preferences from choices and rankings
Abstract/Contents
- Abstract
- A large and growing experimental literature has shown that individual choices and judgements can be affected by irrelevant aspects of the context in which they are made. Despite these findings, much of the existing modeling work in preference learning still relies on the simplifying assumption that choices come from the maximization of a stable utility function. In this dissertation, we discuss our progress in tractably modeling violations of utility-based reasoning in choices and rankings at scale. First, we describe the context dependent random utility model (CDM), our choice model that captures a broad class of context effects while remaining inferentially tractable. Second, we consider testing when violations of a popular notion of rationality, the Independence of Irrelevant Alternatives (IIA), exist in practice. Our work contributes effective methods for testing IIA and characterizes the fundamental statistical limitations of doing so. Third, we show how our advances in choice modeling can be leveraged to develop the contextual repeated selection (CRS) model of ranking, a model that brings a natural multimodality and richness to the rankings space along with strong statistical guarantees.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Seshadri, Arjun |
|---|---|
| Degree supervisor | Ugander, Johan |
| Thesis advisor | Ugander, Johan |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Van Roy, Benjamin |
| Degree committee member | Boyd, Stephen P |
| Degree committee member | Van Roy, Benjamin |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Arjun Seshadri. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/zv003vd9732 |
Access conditions
- Copyright
- © 2021 by Arjun Seshadri
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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