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What if you are not Bayesian? The consequences for decisions involving risk

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  • Paul Goodwin

    (University of Bath)

  • Dilek Önkal

    (University of Bradford)

  • Herman O. Stekler

    (The George Washington University)

Abstract

Many studies have examined the extent to which individuals’ probability judgments depart from Bayes’ theorem when revising probability estimates in the light of new information. Generally, these studies have not considered the implications of such departures for decisions involving risk. We identify when such departures will occur in two common types of decisions. We then report on two experiments where people were asked to revise their own prior probabilities of a forthcoming economic recession in the light of new information. When the reliability of the new information was independent of the state of nature, people tended to overreact to it if their prior probability was low and underreact if it was high. When it was not independent, they tended to display conservatism. We identify the circumstances where discrepancies in decisions arising from a failure to use Bayes’ theorem were most likely to occur in the decision context we examined. We found that these discrepancies were relatively rare and, typically, were not serious.

Suggested Citation

  • Paul Goodwin & Dilek Önkal & Herman O. Stekler, 2017. "What if you are not Bayesian? The consequences for decisions involving risk," Working Papers 2017-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2017-003
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    References listed on IDEAS

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    1. Lawrence, Michael & O'Connor, Marcus, 1992. "Exploring judgemental forecasting," International Journal of Forecasting, Elsevier, vol. 8(1), pages 15-26, June.
    2. Trudy Cameron, 2005. "Updating Subjective Risks in the Presence of Conflicting Information: An Application to Climate Change," Journal of Risk and Uncertainty, Springer, vol. 30(1), pages 63-97, January.
    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. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    5. 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.
    6. Goodwin, Paul, 2015. "When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts," Journal of Business Research, Elsevier, vol. 68(8), pages 1686-1691.
    7. M. H. Schnader & H. O. Stekler, 1998. "Sources of turning point forecast errors," Applied Economics Letters, Taylor & Francis Journals, vol. 5(8), pages 519-521.
    8. Eger, C & Dickhaut, J, 1982. "An Examination Of The Conservative Information-Processing Bias In An Accounting Framework," Journal of Accounting Research, Wiley Blackwell, vol. 20(2), pages 711-723.
    9. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
    10. Gary Charness & Edi Karni & Dan Levin, 2007. "Individual and group decision making under risk: An experimental study of Bayesian updating and violations of first-order stochastic dominance," Journal of Risk and Uncertainty, Springer, vol. 35(2), pages 129-148, October.
    11. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    12. Bonaccio, Silvia & Dalal, Reeshad S., 2006. "Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(2), pages 127-151, November.
    13. F. Hutton Barron & Bruce E. Barrett, 1996. "Decision Quality Using Ranked Attribute Weights," Management Science, INFORMS, vol. 42(11), pages 1515-1523, November.
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    Keywords

    decision processes; Bayes’ theorem; judgmental biases; risk;
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