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8
Revising judgments in the
light of new information
Introduction
Suppose that you are a marketing manager working for an electronics
company which is considering the launch of a new type of pocket
calculator. On the basis of your knowledge of the market, you estimate
that there is roughly an 80% probability that the sales of the calculator
in its first year would exceed the break-even level. You then receive
some new information from a market research survey. The results of
this suggest that the sales of the calculator would be unlikely to reach
the break-even level. How should you revise your probability estimate
in the light of this new information?
You realize that the market research results are unlikely to be perfectly
reliable: the sampling method used, the design of the questionnaire and
even the way the questions were posed by the interviewers may all
have contributed to a misleading result. Perhaps you feel so confident in
your own knowledge of the market that you decide to ignore the market
research and to leave your probability unchanged. Alternatively, you
may acknowledge that because the market research has had a good track
record in predicting the success of products, you should make a large
adjustment to your initial estimate.
In this chapter we will look at the process of revising initial probability
estimates in the light of new information. The focus of our discussion
will be Bayes’ theorem, which is named after an English clergyman,
Thomas Bayes, whose ideas were published posthumously in 1763.
Bayes’ theorem will be used as a normative tool, telling us how we should
revise our probability assessments when new information becomes
available. Whether people actually do revise their judgments in the
manner laid down by the theorem is an issue which we will discuss in
Chapter 14.
Of course, new information, be it from market research, scientific
tests or consultants, can be expensive to obtain. Towards the end of