The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy
The aggregation of many lay judgements can generate surprisingly accurate estimates. This effect, known as the “wisdom of the crowd”, has been demonstrated in domains such as medical decision-making, factchecking news, and financial forecasting. Therefore, understanding the conditions that enhance...
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I57-R163-20.500.13098-121432024-09-09T22:41:56Z The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy Barrera Lermarchand, Federico Balenzuela, Pablo Bahrami,Bahador Deroy, Ophelia Navajas Joaquin Anchoring Collective decision making collective intelligence diversity Forecasting Wisdom of the Crowd The aggregation of many lay judgements can generate surprisingly accurate estimates. This effect, known as the “wisdom of the crowd”, has been demonstrated in domains such as medical decision-making, factchecking news, and financial forecasting. Therefore, understanding the conditions that enhance the wisdom of the crowd has become a crucial issue in the social and behavioral sciences. Previous theoretical research identified two key factors driving this effect: the accuracy of individuals and the diversity of their opinions. Most available strategies to enhance the wisdom of the crowd have exclusively focused on improving individual accuracy while neglecting the potential of increasing opinion diversity. Here, we study a complementary approach to reduce collective error by promoting divergent and extreme opinions, using a cognitive bias called the “anchoring effect”. This method proposes to anchor half of the crowd to an extremely small value and the other half to an extremely large value before eliciting and averaging their estimates. As predicted by our mathematical modeling, three behavioral experiments demonstrate that this strategy concurrently increases individual error, opinion diversity, and collectively accuracy. Most remarkably, we show that this approach works even in a forecasting task where the experimenters did not know the correct answer at the time of testing. Overall, these results not only provide practitioners with a new strategy to forecast and estimate variables but also have strong theoretical implications on the epistemic value of collective decision-making. 2023-11-21T16:58:30Z 2023-11-21T16:58:30Z 2023 info:eu-repo/semantics/preprint info:eu-repo/semantics/submittedVersion https://repositorio.utdt.edu/handle/20.500.13098/12143 spa info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/2.5/ar/ 32 p. application/pdf application/pdf Universidad Torcuato Di Tella |
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Universidad Torcuato Di Tella |
institution_str |
I-57 |
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R-163 |
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Repositorio Digital Universidad Torcuato Di Tella |
language |
Español |
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spa |
topic |
Anchoring Collective decision making collective intelligence diversity Forecasting Wisdom of the Crowd |
spellingShingle |
Anchoring Collective decision making collective intelligence diversity Forecasting Wisdom of the Crowd Barrera Lermarchand, Federico Balenzuela, Pablo Bahrami,Bahador Deroy, Ophelia Navajas Joaquin The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
topic_facet |
Anchoring Collective decision making collective intelligence diversity Forecasting Wisdom of the Crowd |
description |
The aggregation of many lay judgements can generate surprisingly accurate estimates. This effect, known
as the “wisdom of the crowd”, has been demonstrated in domains such as medical decision-making, factchecking
news, and financial forecasting. Therefore, understanding the conditions that enhance the
wisdom of the crowd has become a crucial issue in the social and behavioral sciences. Previous
theoretical research identified two key factors driving this effect: the accuracy of individuals and the
diversity of their opinions. Most available strategies to enhance the wisdom of the crowd have exclusively
focused on improving individual accuracy while neglecting the potential of increasing opinion diversity.
Here, we study a complementary approach to reduce collective error by promoting divergent and extreme
opinions, using a cognitive bias called the “anchoring effect”. This method proposes to anchor half of the
crowd to an extremely small value and the other half to an extremely large value before eliciting and
averaging their estimates. As predicted by our mathematical modeling, three behavioral experiments
demonstrate that this strategy concurrently increases individual error, opinion diversity, and collectively
accuracy. Most remarkably, we show that this approach works even in a forecasting task where the
experimenters did not know the correct answer at the time of testing. Overall, these results not only
provide practitioners with a new strategy to forecast and estimate variables but also have strong
theoretical implications on the epistemic value of collective decision-making. |
format |
info:eu-repo/semantics/preprint submittedVersion |
author |
Barrera Lermarchand, Federico Balenzuela, Pablo Bahrami,Bahador Deroy, Ophelia Navajas Joaquin |
author_facet |
Barrera Lermarchand, Federico Balenzuela, Pablo Bahrami,Bahador Deroy, Ophelia Navajas Joaquin |
author_sort |
Barrera Lermarchand, Federico |
title |
The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
title_short |
The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
title_full |
The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
title_fullStr |
The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
title_full_unstemmed |
The wisdom of extremized crowds: Promoting erroneous divergent opinions increases collective accuracy |
title_sort |
wisdom of extremized crowds: promoting erroneous divergent opinions increases collective accuracy |
publisher |
Universidad Torcuato Di Tella |
publishDate |
2023 |
url |
https://repositorio.utdt.edu/handle/20.500.13098/12143 |
work_keys_str_mv |
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