{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/129085"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/129085","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Identify experts through Revealed Confidence : application to Wisdom of Crowds","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_html":"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_has_math":false,"creators":["Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Drazen Prelec."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:53Z","subjects":["Sloan School of Management."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. 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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.."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Management Research"]},{"key":"dc:title","label":"Title","values":["Identify experts through Revealed Confidence : application to Wisdom of Crowds"]}]}],"canonical_facts":{"dc:contributor.advisor":["Drazen Prelec."],"dc:contributor.department":["Sloan School of Management","Sloan"],"dc:contributor.other":["Sloan School of Management."],"dc:creator":["Zhang, Yunhao(Business management scientist)Massachusetts Institute of Technology."],"dc:date.accessioned":["2021-01-06T17:39:00Z"],"dc:date.available":["2021-01-06T17:39:00Z"],"dc:date.issued":["2020"],"dc:description":["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)."],"dc:description.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.."],"dc:description.degree":["S.M. in Management Research"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/129085"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. 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