MURAL - Maynooth University Research Archive Library



    Invariants in probabilistic reasoning


    Costello, Fintan and Watts, Paul (2018) Invariants in probabilistic reasoning. Cognitive Pscychology, 100. pp. 1-16. ISSN 0010-0285

    [img]
    Preview
    Download (775kB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    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.

    Item Type: Article
    Keywords: Rationality; Biases; Probability;
    Academic Unit: Faculty of Science and Engineering > Theoretical Physics
    Item ID: 13096
    Identification Number: https://doi.org/10.1016/j.cogpsych.2017.11.003
    Depositing User: Paul Watts
    Date Deposited: 23 Jun 2020 15:26
    Journal or Publication Title: Cognitive Pscychology
    Publisher: Elsevier
    Refereed: Yes
    URI:
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads