The hot stove effect

People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) wi...

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Detalhes bibliográficos
Autores: Denrell, Jerker, Le Mens, Gaël
Tipo de documento: capítulo de livro
Estado:Versión aceptada para publicación
Data de publicação:2023
País:España
Recursos:Universitat Pompeu Fabra
Repositório:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/59812
Acesso em linha:http://hdl.handle.net/10230/59812
http://dx.doi.org/10.1017/9781009002042.005
Access Level:Acceso aberto
Palavra-chave:sampling
negativity bias
Hot Stove Effect
learning
Bayesian models
rationality
exploration
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spelling The hot stove effectDenrell, JerkerLe Mens, Gaëlsamplingnegativity biasHot Stove EffectlearningBayesian modelsrationalityexplorationPeople revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) will be less likely to be corrected than errors of overestimation (an alternative is believed to be better than it is). Denrell & March (2001) called this asymmetry in error correction the “Hot Stove Effect.” This chapter explains the basic logic behind the Hot Stove Effect and how this bias can explain a range of judgment biases. We review empirical studies that illustrate how risk aversion and mistrust can be explained by the Hot Stove Effect. We also explain why even a rational algorithm can be subject to the same bias.Cambridge University Press202420242023info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/59812http://dx.doi.org/10.1017/9781009002042.005reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésFiedler K, Juslin P, Debrell J, editors. Sampling in judgment and decision making. Cambridge: Cambridge University Press; 2023. p. 90-112.© Cambridge University Pressinfo:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/598122026-06-12T07:21:37Z
dc.title.none.fl_str_mv The hot stove effect
title The hot stove effect
spellingShingle The hot stove effect
Denrell, Jerker
sampling
negativity bias
Hot Stove Effect
learning
Bayesian models
rationality
exploration
title_short The hot stove effect
title_full The hot stove effect
title_fullStr The hot stove effect
title_full_unstemmed The hot stove effect
title_sort The hot stove effect
dc.creator.none.fl_str_mv Denrell, Jerker
Le Mens, Gaël
author Denrell, Jerker
author_facet Denrell, Jerker
Le Mens, Gaël
author_role author
author2 Le Mens, Gaël
author2_role author
dc.subject.none.fl_str_mv sampling
negativity bias
Hot Stove Effect
learning
Bayesian models
rationality
exploration
topic sampling
negativity bias
Hot Stove Effect
learning
Bayesian models
rationality
exploration
description People revisit the restaurants they like and avoid the restaurants with which they had a poor experience. This tendency to approach alternatives believed to be good is usually adaptive but can lead to a systematic bias. Errors of underestimation (an alternative is believed to be worse than it is) will be less likely to be corrected than errors of overestimation (an alternative is believed to be better than it is). Denrell & March (2001) called this asymmetry in error correction the “Hot Stove Effect.” This chapter explains the basic logic behind the Hot Stove Effect and how this bias can explain a range of judgment biases. We review empirical studies that illustrate how risk aversion and mistrust can be explained by the Hot Stove Effect. We also explain why even a rational algorithm can be subject to the same bias.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/acceptedVersion
format bookPart
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/59812
http://dx.doi.org/10.1017/9781009002042.005
url http://hdl.handle.net/10230/59812
http://dx.doi.org/10.1017/9781009002042.005
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Fiedler K, Juslin P, Debrell J, editors. Sampling in judgment and decision making. Cambridge: Cambridge University Press; 2023. p. 90-112.
dc.rights.none.fl_str_mv © Cambridge University Press
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Cambridge University Press
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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