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Aggregation mechanisms for crowd predictions
When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts. This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process (“wisdom of crowds”)...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591134/ https://www.ncbi.nlm.nih.gov/pubmed/33132745 http://dx.doi.org/10.1007/s10683-019-09631-0 |
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author | Palan, Stefan Huber, Jürgen Senninger, Larissa |
author_facet | Palan, Stefan Huber, Jürgen Senninger, Larissa |
author_sort | Palan, Stefan |
collection | PubMed |
description | When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts. This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process (“wisdom of crowds”). However, an appropriate aggregation mechanism is considered crucial to reaping the benefits of a “wise crowd”. Of the many possible ways to aggregate individual forecasts, we compare (uncensored and censored) arithmetic and geometric mean and median, continuous double auction market prices and sealed bid-offer call market prices in a controlled experiment. We use an asymmetric information structure, where participants know different sub-sets of the total information needed to exactly calculate the asset value to be estimated. We find that prices from continuous double auction markets clearly outperform all alternative approaches for aggregating dispersed information and that information lets only the best-informed participants generate excess returns. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10683-019-09631-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7591134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-75911342020-10-29 Aggregation mechanisms for crowd predictions Palan, Stefan Huber, Jürgen Senninger, Larissa Exp Econ Original Paper When the information of many individuals is pooled, the resulting aggregate often is a good predictor of unknown quantities or facts. This aggregate predictor frequently outperforms the forecasts of experts or even the best individual forecast included in the aggregation process (“wisdom of crowds”). However, an appropriate aggregation mechanism is considered crucial to reaping the benefits of a “wise crowd”. Of the many possible ways to aggregate individual forecasts, we compare (uncensored and censored) arithmetic and geometric mean and median, continuous double auction market prices and sealed bid-offer call market prices in a controlled experiment. We use an asymmetric information structure, where participants know different sub-sets of the total information needed to exactly calculate the asset value to be estimated. We find that prices from continuous double auction markets clearly outperform all alternative approaches for aggregating dispersed information and that information lets only the best-informed participants generate excess returns. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10683-019-09631-0) contains supplementary material, which is available to authorized users. Springer US 2019-11-09 2020 /pmc/articles/PMC7591134/ /pubmed/33132745 http://dx.doi.org/10.1007/s10683-019-09631-0 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Paper Palan, Stefan Huber, Jürgen Senninger, Larissa Aggregation mechanisms for crowd predictions |
title | Aggregation mechanisms for crowd predictions |
title_full | Aggregation mechanisms for crowd predictions |
title_fullStr | Aggregation mechanisms for crowd predictions |
title_full_unstemmed | Aggregation mechanisms for crowd predictions |
title_short | Aggregation mechanisms for crowd predictions |
title_sort | aggregation mechanisms for crowd predictions |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591134/ https://www.ncbi.nlm.nih.gov/pubmed/33132745 http://dx.doi.org/10.1007/s10683-019-09631-0 |
work_keys_str_mv | AT palanstefan aggregationmechanismsforcrowdpredictions AT huberjurgen aggregationmechanismsforcrowdpredictions AT senningerlarissa aggregationmechanismsforcrowdpredictions |