Improving Bayesian Reasoning: What Works and Why?
We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal...
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| Үндсэн зохиолчид: | , |
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| Формат: | Online |
| Хэл сонгох: | англи |
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Frontiers Media SA
2021
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| Нөхцлүүд: | |
| Онлайн хандалт: | 18865 |
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Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869531318515138560 |
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| author | Gorka Navarrete David R. Mandel |
| author_browse | David R. Mandel Gorka Navarrete |
| author_facet | Gorka Navarrete David R. Mandel |
| author_sort | Gorka Navarrete |
| collection | Directory of Open Access Books |
| description | We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types. |
| format | Online |
| id | doab-20.500.12854ir-50064 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Frontiers Media SA |
| publisherStr | Frontiers Media SA |
| record_format | ojs |
| spelling | doab-20.500.12854ir-500642024-03-29T08:01:53Z Improving Bayesian Reasoning: What Works and Why? Gorka Navarrete David R. Mandel BF1-990 Q1-390 Risk Communication human judgment probabilistic judgment subjective probability psychological methods belief revision Bayesianism Bayesian reasoning individual differences bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology We confess that the first part of our title is somewhat of a misnomer. Bayesian reasoning is a normative approach to probabilistic belief revision and, as such, it is in need of no improvement. Rather, it is the typical individual whose reasoning and judgments often fall short of the Bayesian ideal who is the focus of improvement. What have we learnt from over a half-century of research and theory on this topic that could explain why people are often non-Bayesian? Can Bayesian reasoning be facilitated, and if so why? These are the questions that motivate this Frontiers in Psychology Research Topic. Bayes' theorem, named after English statistician, philosopher, and Presbyterian minister, Thomas Bayes, offers a method for updating one’s prior probability of an hypothesis H on the basis of new data D such that P(H|D) = P(D|H)P(H)/P(D). The first wave of psychological research, pioneered by Ward Edwards, revealed that people were overly conservative in updating their posterior probabilities (i.e., P(D|H)). A second wave, spearheaded by Daniel Kahneman and Amos Tversky, showed that people often ignored prior probabilities or base rates, where the priors had a frequentist interpretation, and hence were not Bayesians at all. In the 1990s, a third wave of research spurred by Leda Cosmides and John Tooby and by Gerd Gigerenzer and Ulrich Hoffrage showed that people can reason more like a Bayesian if only the information provided takes the form of (non-relativized) natural frequencies. Although Kahneman and Tversky had already noted the advantages of frequency representations, it was the third wave scholars who pushed the prescriptive agenda, arguing that there are feasible and effective methods for improving belief revision. Most scholars now agree that natural frequency representations do facilitate Bayesian reasoning. However, they do not agree on why this is so. The original third wave scholars favor an evolutionary account that posits human brain adaptation to natural frequency processing. But almost as soon as this view was proposed, other scholars challenged it, arguing that such evolutionary assumptions were not needed. The dominant opposing view has been that the benefit of natural frequencies is mainly due to the fact that such representations make the nested set relations perfectly transparent. Thus, people can more easily see what information they need to focus on and how to simply combine it. This Research Topic aims to take stock of where we are at present. Are we in a proto-fourth wave? If so, does it offer a synthesis of recent theoretical disagreements? The second part of the title orients the reader to the two main subtopics: what works and why? In terms of the first subtopic, we seek contributions that advance understanding of how to improve people’s abilities to revise their beliefs and to integrate probabilistic information effectively. The second subtopic centers on explaining why methods that improve non-Bayesian reasoning work as well as they do. In addressing that issue, we welcome both critical analyses of existing theories as well as fresh perspectives. For both subtopics, we welcome the full range of manuscript types. 2021-02-11T16:02:00Z 2021-02-11T16:02:00Z 2016-04-07 11:22:02 2016 book 18865 16648714 9782889197453 https://directory.doabooks.org/handle/20.500.12854/50064 eng Frontiers Research Topics image/jpeg Attribution 4.0 International http://www.frontiersin.org/books/Improving_Bayesian_Reasoning_What_Works_and_Why_/792#nogo http://journal.frontiersin.org/researchtopic/2963/improving-bayesian-reasoning-what-works-and-why Frontiers Media SA 10.3389/978-2-88919-745-3 10.3389/978-2-88919-745-3 bf5ce210-e72e-4860-ba9b-c305640ff3ae 9782889197453 207 open access |
| spellingShingle | BF1-990 Q1-390 Risk Communication human judgment probabilistic judgment subjective probability psychological methods belief revision Bayesianism Bayesian reasoning individual differences bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology Gorka Navarrete David R. Mandel Improving Bayesian Reasoning: What Works and Why? |
| title | Improving Bayesian Reasoning: What Works and Why? |
| title_full | Improving Bayesian Reasoning: What Works and Why? |
| title_fullStr | Improving Bayesian Reasoning: What Works and Why? |
| title_full_unstemmed | Improving Bayesian Reasoning: What Works and Why? |
| title_short | Improving Bayesian Reasoning: What Works and Why? |
| title_sort | improving bayesian reasoning what works and why |
| topic | BF1-990 Q1-390 Risk Communication human judgment probabilistic judgment subjective probability psychological methods belief revision Bayesianism Bayesian reasoning individual differences bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology |
| topic_facet | BF1-990 Q1-390 Risk Communication human judgment probabilistic judgment subjective probability psychological methods belief revision Bayesianism Bayesian reasoning individual differences bic Book Industry Communication::J Society & social sciences::JM Psychology thema EDItEUR::J Society and Social Sciences::JM Psychology |
| url | 18865 |
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