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270 Biases in probability assessment
businesses that survive (he would argue that they would be able to make
good estimates of this relative frequency) and use this as an estimate of
an individual’s business surviving.
Comparison of studies of the calibration of probability assessments
concerning unique individual events with those where assessments have
been made for repetitive predictions of weather events, reveals a general
finding of relatively poor calibration in the former contrasted with
good calibration in the latter. Bolger and Wright 44 have argued that
this differential forecasting performance is due, in part, to the existence
of rapid and meaningful feedback to the weather forecasters in
terms of both the relative frequency of probability predictions and the
predicted event’s occurrence. Such prediction-feedback frequency information
may well be ideal for the achievement of frequentistic-based
accuracy. However, such ideal conditions for probability assessment
are not common in many management situations which tend to be
characterized by the need to judge the likelihood of unique events.
In summary, we advocate that, in assessing a subjective probability,
you attempt to locate a reference class of previous forecasts that
you have made which are similar to the event that you now need to
forecast. If the event is, say, demand for a set number of batches of
perishable food (page 96), attendance at a conference in a provincial
town (page 102), or successful development of a new type of processor
(page 145), then you should first consider whether or not you
have made repetitive forecasts of such events in the past. If you have,
and have received timely feedback on the accuracy of your forecasts,
then the assessment task is likely to be like that of weather forecasting
where good calibration is a general finding. If not, then you
should consider whether there is a historic, relative frequency reference
class that you can use. For example, if you are considering the likelihood
that a newly hired worker will stay for at least a year then you
should consider the number of workers who have been hired in your
organization at that grade (i.e. identify the reference class) and then
calculate how many in, say, the last five years have remained for at least
one year.
If a reference class of previous forecasts or historic frequencies is not
obvious then be aware that the only way to assess the likelihood of the
event is to use judgmental heuristics and that such heuristics can lead
to bias – as we have documented in this chapter. Figure 9.1 summarizes
this conclusion.