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Gambling habits and Probability Judgements in a Bayesian Task Environment

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  • David L. Dickinson
  • Parker Reid

Abstract

Little is known about how gamblers estimate probabilities from multiple information sources. This paper reports on a preregistered study that administered an incentivized Bayesian choice task to n=465 participants (self-reported gamblers and non-gamblers). The task elicits subjective probability estimates for a particular event given the base rate probability and new evidence information for that event, which allows for an assessment of one’s probability assessment accuracy. Furthermore, we also estimate the degree to which both sources of information are weighted in forming subjective probability estimates. Our data failed to support our main hypotheses that experienced online gamblers would be more accurate Bayesian decision-makers compared to non-gamblers, that gamblers experienced in games of skill (e.g., poker) would be more accurate than gamblers experienced only in non-skill games (e.g., slots), or that accuracy would differ in females compared to males. Pairwise comparisons between these types of participants also failed to show any difference in decision weights placed on the two information sources. Exploratory analysis, however, revealed interesting effects related to self-reported gambling frequency. Specifically, more frequent online gamblers had lower Bayesian accuracy than infrequent gamblers. Also, those scoring higher in a cognitive reflection task were more Bayesian in weighting information sources when making belief assessments. While we report no main effect of sex on Bayesian accuracy, exploratory analysis found that the decline in accuracy linked to self-reported gambling frequency was stronger for females. Decision modeling finds a decreased weight place on new evidence (over base rate odds) in those who showed decreased accuracy, which suggests a proper incorporation of new information into one’s probability assessments is important for more accurate assessment of probabilities in uncertain environments. Our results link frequency of gambling to worse performance in the critical probability assessment skills that should benefit gambling success (i.e., in skill-based games). Additional research is needed to better understand why a higher frequency of gambling is associated with lower Bayesian accuracy and why this association is greater in females compared to males. Key Words: Gambling, Bayes Rule, Probability Judgements, Cognitive Reflection

Suggested Citation

  • David L. Dickinson & Parker Reid, 2023. "Gambling habits and Probability Judgements in a Bayesian Task Environment," Working Papers 23-03, Department of Economics, Appalachian State University.
  • Handle: RePEc:apl:wpaper:23-03
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    File URL: http://econ.appstate.edu/RePEc/pdf/wp2303.pdf
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    References listed on IDEAS

    as
    1. Oechssler, Jörg & Roider, Andreas & Schmitz, Patrick W., 2009. "Cognitive abilities and behavioral biases," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 147-152, October.
    2. Goodwin, Paul & Önkal, Dilek & Stekler, Herman O., 2018. "What if you are not Bayesian? The consequences for decisions involving risk," European Journal of Operational Research, Elsevier, vol. 266(1), pages 238-246.
    3. Grether, David M., 1992. "Testing bayes rule and the representativeness heuristic: Some experimental evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 17(1), pages 31-57, January.
    4. Holt, Charles A. & Smith, Angela M., 2009. "An update on Bayesian updating," Journal of Economic Behavior & Organization, Elsevier, vol. 69(2), pages 125-134, February.
    5. M.S.M. Lim & G. Jocham & L.T. Hunt & T.E.J. Behrens & R.D. Rogers, 2015. "Impulsivity and predictive control are associated with suboptimal action-selection and action-value learning in regular gamblers," International Gambling Studies, Taylor & Francis Journals, vol. 15(3), pages 489-505, December.
    6. Dickinson, David L. & McElroy, Todd, 2019. "Bayesian versus heuristic-based choice under sleep restriction and suboptimal times of day," Games and Economic Behavior, Elsevier, vol. 115(C), pages 48-59.
    7. Cowley, Elizabeth & Briley, Donnel A. & Farrell, Colin, 2015. "How do gamblers maintain an illusion of control?," Journal of Business Research, Elsevier, vol. 68(10), pages 2181-2188.
    8. David L. Dickinson & Eugenio Caleb Garbuio, 2020. "The Influence of Dietary Patterns on Outcomes in a Bayesian Choice Task," Working Papers 21-01, Department of Economics, Appalachian State University.
    9. repec:cup:judgdm:v:3:y:2008:i::p:181-190 is not listed on IDEAS
    10. David M. Grether, 1980. "Bayes Rule as a Descriptive Model: The Representativeness Heuristic," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 95(3), pages 537-557.
    11. Barash, Jori & Brocas, Isabelle & Carrillo, Juan D. & Kodaverdian, Niree, 2019. "Heuristic to Bayesian: The evolution of reasoning from childhood to adulthood," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 305-322.
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    More about this item

    Keywords

    gambling; bayes rule; probability judgements; cognitive reflection;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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