Here we evaluate the generalizability of calibration studies which have used general knowledge questions, and argue that on conceptual, methodological and empirical grounds the results have limited applicability to judgemental forecasting. We also review evidence which suggests that judgemental forecast probabilities are influenced by variables such as the desirability, imminence, time period and perceived controllability of the event to be forecast. As these variables do not apply to judgement in the domain of general knowledge, a need for research recognizing and exploring the psychological processes underlying uncertainty about the future is apparent.
KEY WORDS Calibration Judgemental forecasting Subjective probability
This paper reviews recent psychological research that has relevance for judgemental forecasting. Judgemental forecasts are often required in situations where actuarial, or relative frequency, data are unavailable or known to be unreliable. These judgements can be used as inputs to decision analysis and probabilistic information processing systems, which are based on subjective expected utility theory and Bayes' theorem, respectively. (For a review of these decision aids see ). Economists have identified a need for judgemental forecasts when there is seen to be a possibility of 'turning points' in time series data. The recognition of the possibility of such turning points may be made on the basis of observing discontinuous changes in variables that are assumed to have a causal influence on the criterion variable. For example, consider the impact of the miners' strike in the United Kingdom upon U.K. gross domestic product and U.K. investment. reviewed earlier research and reported some new empirical evidence on the psychology of judgement. Fischhoff and MacGregor focused on the calibration of subjective probabilities. Calibration is one measure of the validity of subjective probability assessment. For a person to be perfectly calibrated, assessed probability should equal percentage correct over a number of assessments of equal probability. For example, if you assign a probability of 0.7 to each of ten questions, you should get seven of those questions correct. Similarly, all events that you assess as being certain to occur (1 .O probability assessments) should occur.