Inferring noncompensatory choice heuristics
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2007
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| Online Access: | http://hdl.handle.net/1721.1/36226 |
| _version_ | 1826206794686398464 |
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| author | Yee, Michael |
| author2 | James B. Orlin. |
| author_facet | James B. Orlin. Yee, Michael |
| author_sort | Yee, Michael |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. |
| first_indexed | 2024-09-23T13:38:24Z |
| format | Thesis |
| id | mit-1721.1/36226 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:38:24Z |
| publishDate | 2007 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/362262023-03-01T02:08:04Z Inferring noncompensatory choice heuristics Yee, Michael James B. Orlin. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management Operations Research Center. Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. Includes bibliographical references (p. 121-128). Human decision making is a topic of great interest to marketers, psychologists, economists, and others. People are often modeled as rational utility maximizers with unlimited mental resources. However, due to the structure of the environment as well as cognitive limitations, people frequently use simplifying heuristics for making quick yet accurate decisions. In this research, we apply discrete optimization to infer from observed data if a person is behaving in way consistent with a choice heuristic (e.g., a noncompensatory lexicographic decision rule). We analyze the computational complexity of several inference related problems, showing that while some are easy due to possessing a greedoid language structure, many are hard and likely do not have polynomial time solutions. For the hard problems we develop an exact dynamic programming algorithm that is robust and scalable in practice, as well as analyze several local search heuristics. We conduct an empirical study of SmartPhone preferences and find that the behavior of many respondents can be explained by lexicographic strategies. (cont.) Furthermore, we find that lexicographic decision rules predict better on holdout data than some standard compensatory models. Finally, we look at a more general form of noncompensatory decision process in the context of consideration set formation. Specifically, we analyze the computational complexity of rule-based consideration set formation, develop solution techniques for inferring rules given observed consideration data, and apply the techniques to a real dataset. by Michael J. Yee. Ph.D. 2007-02-21T13:10:01Z 2007-02-21T13:10:01Z 2006 2006 Thesis http://hdl.handle.net/1721.1/36226 76954063 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 128 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Yee, Michael Inferring noncompensatory choice heuristics |
| title | Inferring noncompensatory choice heuristics |
| title_full | Inferring noncompensatory choice heuristics |
| title_fullStr | Inferring noncompensatory choice heuristics |
| title_full_unstemmed | Inferring noncompensatory choice heuristics |
| title_short | Inferring noncompensatory choice heuristics |
| title_sort | inferring noncompensatory choice heuristics |
| topic | Operations Research Center. |
| url | http://hdl.handle.net/1721.1/36226 |
| work_keys_str_mv | AT yeemichael inferringnoncompensatorychoiceheuristics |