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Lecture 13

# INST 354 Lecture Notes - Lecture 13: Decision Analysis, Rational Expectations, Mental ModelExam

Department
Information Studies
Course Code
INST 354
Professor
Anton
Lecture
13

This preview shows half of the first page. to view the full 3 pages of the document. INST354 Lecture 13: Quality Decisions
The decision tree diagrams remind us that the crucial first step in understanding any decision is
to describe the situation in which the decision occurs. That step may sound trivial, but the
attempt to construct a summary diagram forces us to answer difficult questions about what to
include and, more difficult, what to exclude. Then the diagram prompts us to solve the
challenging problem of quantifying the uncertainties and values that define the decision. Solving
the problem of inferring how another person has conceptualized a decision situation is usually
the toughest part of psychological research or applied decision analysis. (Much of the craft of
research design involves creating experimental situations in which the researcher restricts the
subject’s thought processes and understands the effects of those restrictions on the subject’s
mental model of the experimental situation.)
If we believe that we have captured our subject’s situation model in a decision tree diagram, it
is relatively easy to calculate the decision that leads to the highest expected outcome by
applying a rule that follows from decision theory (the four rational assumptions introduced in
Chapter 1). This rule is called the rational expectations principle, and it is usually summarized
as an equation:
Utility = S (probabilityi × valuei).
The equation prescribes that for each alternative course of action under consideration (each
major branch of the decision tree), we need to weight each of the potential consequences by its
probability of occurrence, and then add up all the component products to yield a summary
evaluation called an expected utility for each alternative course of action (each initial left-
hand branch). In our example medical decision (Figure 2.1), the calculations specify the
expected utility for “have the operation” as +52 ([+80 × .65] + [0 × .35]) and for “do not operate”
as +44 ([+100 × .30] + [+20 × .70]), implying that the rational decision would be to have the
operation. In the case of the gamble (Figure 2.2), if we assume that the dollar values represent
the decision maker’s true personal values for those consequences (an assumption that needs to
be carefully examined), the expected utility for gamble (a) is \$9.00 ([\$45 × .20] + [0 × .80]) and
for gamble (b) is \$7.50 ([\$30 × .25] + [0 × .75]), implying the decision maker should choose to
play gamble (a), if the expected value is the only consideration.
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