380 • CHAPTER 13 Reasoning and Decision Making
However, the opt-in versus opt-out results indicate that the procedure used to identify
people’s willingness to be organ donors does have an effect.
An example of how the wording of a problem can infl uence a decision was
demonstrated by Paul Slovic and coworkers (2000). They showed forensic psychologists
and psychiatrists a case history of a mental patient, Mr. Jones, and asked them to judge
the likelihood that the patient would commit an act of violence within 6 months of
being discharged. The key variable in this experiment was the nature of a statement
that presented information about previous cases. When they were told that “20 out of
every 100 patients similar to Mr. Jones are estimated to commit an act of violence,”
41 percent refused to discharge him. However, when told that “patients similar to
Mr. Jones are estimated to have a 20 percent chance of committing an act of violence,”
only 21 percent refused to discharge him. Why did this difference occur? One possibility
is that the fi rst statement conjures up images of 20 people being beaten up, whereas the
second is a more abstract probability statement that could be interpreted to mean that
there is only a small chance that patients like Mr. Jones will be violent.
Here’s another example of choosing between two alternatives, for you to try.Imagine that the United States is preparing for the outbreak of an unusual disease that is
expected to kill 600 people. Two alternative programs to combat the disease have been pro-
posed. Assume that the exact scientifi c estimates of the consequences of the programs are as
follows:- If Program A is adopted, 200 people will be saved.
- If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3
probability that no people will be saved.
Which of the two programs would you favor?
Now consider the following additional proposals for combating the same disease:- If Program C is adopted, 400 people will die.
- If Program D is adopted, there is a 1/3 probability that nobody will die, and a 2/3 probability
that 600 people will die.
Which of these two programs would you pick?When offered the fi rst pair of proposals, 72 percent of the students in an experiment
by Tversky and Kahneman (1981) chose Program A and the rest picked Program B
(● Figure 13.12). The choice of Program A represents a risk aversion strategy. The
idea of saving 200 lives with certainty is more attractive than the risk that no one
will be saved. However, when Tversky and Kahneman presented the descriptions of
Programs C and D to another group of students, 22 percent picked Program C and
78 percent picked Program D. This represents a risk-taking strategy. The certain death
of 400 people is less acceptable than a 2 in 3 chance that 600 people will die.
Tversky and Kahneman concluded that, in general, when a choice is framed in terms
of gains (as in the fi rst problem, which is stated in terms of saving lives), people use a
risk aversion strategy, and when a choice is framed in terms of losses (as in the second
problem, which is stated in terms of losing lives), people use a risk-taking strategy.
But if we look at the four programs closely, we can see that they are identical pairs
(Figure 13.12). Programs A and C both result in 200 people living and 400 people
dying. Yet 72 percent of the participants picked Program A and only 22 percent picked
Program C. A similar situation occurs if we compare Programs B and D. Both lead to
the same number of deaths, yet one was picked by 28 percent of the participants and
the other by 78 percent. These results illustrate the framing effect—decisions are infl u-
enced by how the choices are stated, or framed.Lexical Decision
Risky Decisions
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