Invariants in probabilistic reasoning

Files in This Item:
File Description SizeFormat 
invariant_identities_preprint.pdf996.61 kBAdobe PDFDownload
Title: Invariants in probabilistic reasoning
Authors: Costello, FintanWatts, Paul
Permanent link: http://hdl.handle.net/10197/12115
Date: Feb-2018
Online since: 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
Keywords: HumansBayes TheoremProbability TheoryRationalityBiasesProbability
DOI: 10.1016/j.cogpsych.2017.11.003
Language: en
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/
Appears in Collections:Computer Science Research Collection

Show full item record

Page view(s)

129
Last Week
11
Last month
checked on May 10, 2021

Download(s)

10
checked on May 10, 2021

Google ScholarTM

Check

Altmetric


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.