When simple alternatives to Bayes formula work well: reducing the cognitive load when updating probability forecasts

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Abstract

Bayes theorem is the normative method for revising probability forecasts when new information is received. However, for unaided forecasters its application can be difficult, effortful, opaque and even counter-intuitive. Two simple heuristics are proposed for approximating Bayes formula while yielding accurate decisions. Their performance was assessed: i) where a decision is made on which of two events is most probable and ii) where a choice is made between an option yielding an intermediate utility for certain or a gamble which will result in either a worse or better utility (‘certainty or risk’ decisions). For ‘most probable event’ decisions the first heuristic always results in the correct decision when the reliability of the new information does not depend on which event will occur. In other cases the second heuristic typically led to the correct decision for about 95% of ‘most probable event’ decisions and 86% of ‘certainty or risk’ decisions.
Original languageEnglish
Pages (from-to)1686-1691
JournalJournal of Business Research
Volume68
Issue number8
Early online date10 Apr 2015
DOIs
Publication statusPublished - Aug 2015

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Probability forecasts
Cognitive load
Heuristics
Gambles
Bayes' theorem

Keywords

  • Bayes theorem, forecasting, heuristics, probability estimation

Cite this

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