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  5. Invariants in probabilistic reasoning
 
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Invariants in probabilistic reasoning

Author(s)
Costello, Fintan  
Watts, Paul  
Uri
http://hdl.handle.net/10197/12115
Date Issued
2018-02
Date Available
2021-04-22T15:08:28Z
Abstract
Recent research has identified three invariants or identities that appear to hold in people's probabilistic reasoning: the QQ identity, the addition law identity, and the Bayes rule identity (Costello and Watts, 2014, 2016a, Fisher and Wolfe, 2014, Wang and Busemeyer, 2013, Wang et al., 2014). Each of these identities represent specific agreement with the requirements of normative probability theory; strikingly, these identities seem to hold in people's judgements despite the presence of strong and systematic biases against the requirements of normative probability theory in those very same judgements. These results suggest that the systematic biases seen in people's probabilistic reasoning follow mathematical rules: for these particular identities, these rules cause an overall cancellation of biases and so produce agreement with normative requirements. We assess two competing mathematical models of probabilistic reasoning (the ‘probability theory plus noise’ model and the ‘quantum probability’ model) in terms of their ability to account for this pattern of systematic biases and invariant identities.
Type of Material
Journal Article
Publisher
Elsevier
Journal
Cognitive Psychology
Volume
100
Start Page
1
End Page
16
Copyright (Published Version)
2017 Elsevier
Subjects

Humans

Bayes Theorem

Probability Theory

Rationality

Biases

Probability

DOI
10.1016/j.cogpsych.2017.11.003
Language
English
Status of Item
Peer reviewed
ISSN
0010-0285
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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invariant_identities_preprint.pdf

Size

996.61 KB

Format

Adobe PDF

Checksum (MD5)

4c651c2bd4e6996f925f33b5ca1e1f7b

Owning collection
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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