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

Author

Listed:
  • 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, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2017-003
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    File URL: https://www2.gwu.edu/~forcpgm/2017-003.pdf
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    References listed on IDEAS

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    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.
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    Keywords

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

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