Identify experts through Revealed Confidence : application to Wisdom of Crowds
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/129085 |
| _version_ | 1811087344011313152 |
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| author | Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology. |
| author2 | Drazen Prelec. |
| author_facet | Drazen Prelec. Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology. |
| author_sort | Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2020 |
| first_indexed | 2024-09-23T13:44:35Z |
| format | Thesis |
| id | mit-1721.1/129085 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:44:35Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1290852023-11-09T14:37:34Z Identify experts through Revealed Confidence : application to Wisdom of Crowds Identify experts through RC : application to WoC Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology. Drazen Prelec. Sloan School of Management. Sloan School of Management Sloan School of Management. Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2020 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 52-54). We propose our Revealed Confidence (RC) algorithm that improves Wisdom of Crowds (WoC) by identifying experts from the crowds. We highlight the important distinction between first- and second-order uncertainty, which also serves as an explanation for rational overconfidence. Under our proposed belief updating mechanism, we analyze the performance of RC algorithm and show the algorithm could identify the more accurate prior estimates even if all agents report the same prior confidence under conventional confidence elicitation, e.g. confidence interval. Our empirical analysis shows that (1) RC improves upon other wisdom of Crowds methods by overweighting the more accurate agents in the aggregation (2) verifies one key prediction of our theoretical result that the distance effect indeed affects belief-updating henceforth RC algorithm's performance, which should be carefully controlled for in order to optimize the algorithm.. by Yunhao Zhang. S.M. in Management Research S.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Management 2021-01-06T17:39:00Z 2021-01-06T17:39:00Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129085 1227097094 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 54 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Sloan School of Management. Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology. Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title | Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title_full | Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title_fullStr | Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title_full_unstemmed | Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title_short | Identify experts through Revealed Confidence : application to Wisdom of Crowds |
| title_sort | identify experts through revealed confidence application to wisdom of crowds |
| topic | Sloan School of Management. |
| url | https://hdl.handle.net/1721.1/129085 |
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