Thinking and Deciding

524 RISK
tant in explaining socially important forms of risky behavior, such as crime, abuse
of drugs and alcohol, gambling, reckless driving, and violent conflict. Perhaps the
same factors can explain pathologies of excessive risk aversion such as anxiety and
obsessive behavior. Perhaps these factors play some role. But the studies that describe
these effects are about thinking, in which people consider the alternatives and
draw conclusions. Much of the harmful behavior of interest is not the result of thinking.
If anything, it is rationalized after the fact, but more often it is simply regretted.
Behavior is surely affected by factors other than thought (Loewenstein, 1996).
Conclusion
This chapter has presented several examples of parallel findings from laboratory
studies and examination of actual public decisions about risk. Societies have several
ways of dealing with risk: individual consumer behavior, lawsuits, laws, and
regulations. All these ways seem to express common intuitions about risk. These intuitions
oppose risks that are involuntary, catastrophic, unknown, caused by action,
or artificial. Intuitions favor reducing risks to zero. In the case of involuntary risks,
normative theory and intuition are on the same side. In other cases, normative theory
seems to be neutral, and people seem to be biased toward one side. These biases are
found in laboratory studies with questionnaires as well as in the real world.
We can avoid the deleterious effects of intuitive thinking if we learn to think
quantitatively. This does not mean that we must have numbers. It does mean that we
realize that a well-made decision requires comparisons of quantities. If we have a
feeling for the quantities, we can make a good guess at what the decision would be
even if we do not know the numbers themselves. This is what we do all the time in
other domains. Tennis players, for example, realize that the intended placement of a
shot is based on a tradeoff of the probability of its going out and the probability of
its winning the point if it does not go out. They do not know these probabilities as
a function of where they aim, although some tennis statistician could, in principle,
compile them. They do, however, think in this quantitative way even without the
numbers.
Quantitative thinking of this sort is not widespread. People do not even notice
its absence. Many people, for example, say that decisions are “difficult” because
there are costs as well as benefits. Many of these people do not seem to consider the
possibility of looking at some overall measure, such as death rates or life expectancy,
or doing something like a decision analysis.
The same goes for government regulation, such as regulation of clean air. If, for
example, we ask people how much money they are willing to pay to prevent one person
from getting asthma or emphysema, the answer is a fair amount. The same for
the time they are willing to spend. They may be telling the truth. Yet when the U.S.
Congress passed a revision of the Clean Air Act and the Environmental Protection
Administration (EPA) proposed new regulations requiring extra inspections of motor
vehicles, people rose up in anger at intrusive government regulation. Exasperated of-