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236 Revising judgments in the light of new information
the importance of sensitivity analysis. This involved the calculation of
the expected value of perfect information and also the variation of the
inputs into the model. He found that use of the Lotus 1-2-3 spreadsheet
package made this sensitivity analysis easy, allowing a large number of
‘what if’ situations to be analyzed in minutes.
One of the areas with the greatest potential for the application of
Bayesian analysis is market research. Indeed, Assmus 4 has argued that
‘no other method has demonstrated an equally strong potential for
analyzing the returns from marketing research’. Nevertheless, Lacava
and Tull 5 refer to evidence that the approach is being used by only a
small proportion of companies engaged in market research because of
perceived difficulties in providing the necessary judgmental inputs and
unfamiliarity with the calculations required to apply the technique. The
authors have therefore developed a modified procedure for assessing
the expected value of market research in decisions where a company
has to decide whether or not to introduce a new product. The inputs
required by the decision maker are: (1) the maximum loss which will
need to be incurred before the product is removed from the market,
(2) the probability of incurring this loss if the product is introduced,
(3) the probability that the product will be successful, if introduced
and (4) the probability that the market research will accurately predict
the true state of the market. The authors have produced sets of tables
which enable the expected value of the market research information to
be determined, thus obviating the need to carry out calculations.
In the tables the EVII is expressed as the maximum percentage of the
potential loss which should be spent on research. For example, suppose
that the maximum loss is $1.2 million, the probability that this sum will
be lost is 0.3, the probability that the product will be a success is 0.6
and the probability that the market research will indicate correctly that
the product should be introduced is 90%, then the tables reveal that
the expected value of the market research information is 11.57% of the
maximum loss, that is, 11.57% of $1.2 million, which is $138 840.
Summary
In this chapter we have discussed the role that new information can play
in revising the judgments of a decision maker. We argued that Bayes’
theorem shows the decision maker how his or her judgments should be
modified in the light of new information, and we showed that this revision
will depend both upon the ‘vagueness’ of the prior judgment and