### Abstract

Original language | English |
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Pages (from-to) | 1686-1691 |

Journal | Journal of Business Research |

Volume | 68 |

Issue number | 8 |

Early online date | 10 Apr 2015 |

DOIs | |

Publication status | Published - Aug 2015 |

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### Keywords

- Bayes theorem, forecasting, heuristics, probability estimation

### Cite this

**When simple alternatives to Bayes formula work well : reducing the cognitive load when updating probability forecasts.** / Goodwin, Paul.

Research output: Contribution to journal › Article

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TY - JOUR

T1 - When simple alternatives to Bayes formula work well

T2 - reducing the cognitive load when updating probability forecasts

AU - Goodwin, Paul

PY - 2015/8

Y1 - 2015/8

N2 - 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.

AB - 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.

KW - Bayes theorem, forecasting, heuristics, probability estimation

UR - http://dx.doi.org/10.1016/j.jbusres.2015.03.027

U2 - 10.1016/j.jbusres.2015.03.027

DO - 10.1016/j.jbusres.2015.03.027

M3 - Article

VL - 68

SP - 1686

EP - 1691

JO - Journal of Business Research

JF - Journal of Business Research

SN - 0148-2963

IS - 8

ER -