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decision making is illustrated by the following questions: Should a consumer

products firm undertake an expensive test-market program before launching

a new and highly promising product? What scientific research approaches

should the government support in the long-term war on cancer? How should

a firm use macroeconomic forecasts of the economy to make inventory and

capacity decisions? What do polls and statistical analyses indicate about the

likely outcome of the upcoming presidential election? What tests are appro-

priate during pregnancies of older women to screen for severe fetal genetic

defects? What database is pertinent for predicting stock market returns for a

particular company or companies in an industry segment? How can informa-

tion on public risks—such as those posed by nuclear power, steel fatigue in

aging bridges or aircraft, coastal hurricanes, environmental pollution, the

spread of infectious diseases—be used to prevent disasters?

These are all broad and important questions. The aim of this chapter is

to provide a way of thinking about information, in particular about how it can

be used to make better decisions. We consider a trio of questions: When

should a manager acquire additional information before making his or her

main decision? How should the manager modify probability assessments of

uncertain events in light of this information? How should he or she make deci-

sions with this information in hand? Together, the answers to these questions

provide the foundation for determining the value of information in decisions

under uncertainty.

THE VALUE OF INFORMATION

The Oil Wildcatter Revisited

Let’s return to the oil-drilling decision of the previous chapter, but with one

additional option: Suppose the wildcatter forms a partnership with a well-

known geologist to explore for oil. At a cost, the partnership can take a seismic

test to obtain better information about the site before drilling. To begin, we

consider a perfectseismic test. Suppose that the geologist conducts the test and

that she categorizes its outcome as either “good” or “bad.” By “good” she means

that oil is present (the site is wet) for certain; by “bad” she means the site is

definitely dry. (Another way to say this is that wet sites always test good and dry

sites always test bad.) It should be clear that, for decision-making purposes, this

perfect test is very valuable. If the outcome is good, the partnership drills,

strikes oil with certainty, and gains a $600,000 profit. If it is bad, it knows there

is no oil and so avoids a loss by choosing not to drill.

The decision tree in Figure 13.1 displays this strategy. Notice that the tree

begins not with a decision square but with a chance circle. The outcome of the

test is resolved first: good or bad. Then two decision squares appear because a

choice must be made in two separate cases: after a good seismic test or after a

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