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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”)...

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Detalles Bibliográficos
Autores principales: Palan, Stefan, Huber, Jürgen, Senninger, Larissa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
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.
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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
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