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

Listed author(s):
  • Paul Goodwin

    ()

    (University of Bath)

  • Dilek Önkal

    ()

    (University of Bradford)

  • Herman O. Stekler

    ()

    (The George Washington University)

Registered author(s):

    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.

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    File URL: https://www2.gwu.edu/~forcpgm/2017-003.pdf
    File Function: First version, 2017
    Download Restriction: no

    Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2017-003.

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    Length: 34 pages
    Date of creation: Oct 2017
    Handle: RePEc:gwc:wpaper:2017-003
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    1. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. F. Hutton Barron & Bruce E. Barrett, 1996. "Decision Quality Using Ranked Attribute Weights," Management Science, INFORMS, vol. 42(11), pages 1515-1523, November.
    8. Lawrence, Michael & O'Connor, Marcus, 1992. "Exploring judgemental forecasting," International Journal of Forecasting, Elsevier, vol. 8(1), pages 15-26, June.
    9. 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.
    10. 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.
    11. repec:bla:joares:v:20:y:1982:i:2:p:711-723 is not listed on IDEAS
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