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Does the “surprisingly popular” method yield accurate crowdsourced predictions?
The “surprisingly popular” method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods—situations in which the majority is incorrect. This method relies on participants’ estimates of other participants’ judgments; when an option is chosen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658271/ https://www.ncbi.nlm.nih.gov/pubmed/33175285 http://dx.doi.org/10.1186/s41235-020-00256-z |
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author | Rutchick, Abraham M. Ross, Bryan J. Calvillo, Dustin P. Mesick, Catherine C. |
author_facet | Rutchick, Abraham M. Ross, Bryan J. Calvillo, Dustin P. Mesick, Catherine C. |
author_sort | Rutchick, Abraham M. |
collection | PubMed |
description | The “surprisingly popular” method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods—situations in which the majority is incorrect. This method relies on participants’ estimates of other participants’ judgments; when an option is chosen more often than the average metacognitive judgments of that option, it is “surprisingly popular” and is selected by the method. Although SP has been shown to improve group decision making about factual propositions (e.g., state capitals), its application to future outcomes has been limited. In three preregistered studies, we compared SP to other methods of aggregating individual predictions about future events. Study 1 examined predictions of football games, Study 2 examined predictions of the 2018 US midterm elections, and Study 3 examined predictions of basketball games. When applied to judgments made by objectively assessed experts, SP performed slightly better than other aggregation methods. Although there is still more to learn about the conditions under which SP is effective, it shows promise as a means of crowdsourcing predictions of future outcomes. |
format | Online Article Text |
id | pubmed-7658271 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-76582712020-11-16 Does the “surprisingly popular” method yield accurate crowdsourced predictions? Rutchick, Abraham M. Ross, Bryan J. Calvillo, Dustin P. Mesick, Catherine C. Cogn Res Princ Implic Brief Report The “surprisingly popular” method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods—situations in which the majority is incorrect. This method relies on participants’ estimates of other participants’ judgments; when an option is chosen more often than the average metacognitive judgments of that option, it is “surprisingly popular” and is selected by the method. Although SP has been shown to improve group decision making about factual propositions (e.g., state capitals), its application to future outcomes has been limited. In three preregistered studies, we compared SP to other methods of aggregating individual predictions about future events. Study 1 examined predictions of football games, Study 2 examined predictions of the 2018 US midterm elections, and Study 3 examined predictions of basketball games. When applied to judgments made by objectively assessed experts, SP performed slightly better than other aggregation methods. Although there is still more to learn about the conditions under which SP is effective, it shows promise as a means of crowdsourcing predictions of future outcomes. Springer International Publishing 2020-11-11 /pmc/articles/PMC7658271/ /pubmed/33175285 http://dx.doi.org/10.1186/s41235-020-00256-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Brief Report Rutchick, Abraham M. Ross, Bryan J. Calvillo, Dustin P. Mesick, Catherine C. Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title | Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title_full | Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title_fullStr | Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title_full_unstemmed | Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title_short | Does the “surprisingly popular” method yield accurate crowdsourced predictions? |
title_sort | does the “surprisingly popular” method yield accurate crowdsourced predictions? |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658271/ https://www.ncbi.nlm.nih.gov/pubmed/33175285 http://dx.doi.org/10.1186/s41235-020-00256-z |
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