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Quantum Models of Human Causal Reasoning

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Abstract

Throughout our lives, we are constantly faced with a variety of causal reasoning problems. A challenge for cognitive modelers is developing a comprehensive framework for modeling causal reasoning across different types of tasks and levels of causal complexity. Causal graphical models, based on Bayes’ calculus, have perhaps been the most successful at explaining and predicting judgments of causal attribution. However, some recent empirical studies have reported violations of the predictions of these models, such as the local Markov condition. In this chapter, the authors suggest an alternative approach to modeling human causal reasoning using quantum Bayes nets. They show that their approach can account for a variety of behavioral phenomena including order effects, violations of the local Markov condition, anti-discounting behavior, and reciprocity.

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Acknowledgements

Jennifer S. Trueblood and Percy K. Mistry were supported by NSF grant SES-1556415.

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Authors and Affiliations

  1. Department of Psychology, Vanderbilt University, Nashville, TN, USA

    Jennifer S. Trueblood

  2. University of California, Irvine, CA, USA

    Percy K. Mistry

Authors
  1. Jennifer S. Trueblood
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  2. Percy K. Mistry
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Correspondence to Jennifer S. Trueblood .

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Editors and Affiliations

  1. School of Management, University of Leicester School of Management, Leicester, United Kingdom

    Emmanuel Haven

  2. Vaexjoe-Kalmar, Sweden

    Andrei Khrennikov

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Trueblood, J.S., Mistry, P.K. (2017). Quantum Models of Human Causal Reasoning. In: Haven, E., Khrennikov, A. (eds) The Palgrave Handbook of Quantum Models in Social Science. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-49276-0_12

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  • DOI: https://doi.org/10.1057/978-1-137-49276-0_12

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  • Publisher Name: Palgrave Macmillan, London

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