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.
- Bayes theorem, forecasting, heuristics, probability estimation