2026-09-18T18:16:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1290852023-11-09T14:37:34Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Drazen Prelec.
Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology.
Sloan School of Management.
Sloan School of Management
2021-01-06T17:39:00Z
2021-01-06T17:39:00Z
2020
2020
https://hdl.handle.net/1721.1/129085
1227097094
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
54 pages
eng
Massachusetts Institute of Technology
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
Sloan School of Management.
Identify experts through Revealed Confidence : application to Wisdom of Crowds
Identify experts through RC : application to WoC
Thesis
application/pdf
2021-01-06T17:38:59Z
Publication
Master
Sloan
unknown
unknown
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<Abstract>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..</Abstract>
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