Biases and Flaws in Decision Making

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Chapter 7 Thought, Language, and Intelligence
Analyzing your choices and the possible
outcomes of each takes some time and
effort, but the results are usually worth
it. Like Dilbert’s boss, many people prefer
to make decisions more impulsively, and
although their decisions sometimes turn
out well, they often don’t. (Gladwell,
2005; Myers, 2004).
© Scott Adams/Dist. By United Feature Syndicate, Inc.
complicates decision making. Deciding which car to buy, which college to attend, or
even how to spend the evening are all examples of choices that require you to weigh
several options. Such choices are often based on the positive or negative value, or
utility, that you, personally, place on each feature of each option. Listing the pros and
cons of each option is a helpful way of keeping them all in mind as you think about
your decisions. You also have to estimate the probabilities and risks associated with the
possible outcomes of each choice. For example, you must consider how likely it is that
job opportunities in your chosen major will have faded by the time you graduate. In
studying risky decision making, psychologists begin with the assumption that the best
decision maximizes expected value. The expected value of a decision is the average
benefit you could expect to receive if the decision were repeated on several occasions.
Biases and Flaws in Decision Making
Most people think of themselves as logical and rational, but in making decisions about
everything from giving up smoking to investing in the stock market, they don’t always
act in ways that maximize expected value (Arkes & Ayton, 1999; Farmer, Patelli, &
Zovko, 2005; Shiller, 2001). Why not?
For one thing, our pain over losing a certain
amount is usually greater than the pleasure we feel after gaining the same amount. This
phenomenon is called loss aversion (Dawes, 1998; Tversky & Kahneman, 1991). Because
of loss aversion, you might go to more trouble to collect a $100 debt than to win a
$100 prize. In addition, the value of a gain doesn’t depend on the amount of the gain
but on what you start with. Suppose you could have a $10 gift certificate from a restaurant, but you have to drive 10 miles to pick it up. This gain has the same monetary
value as having an extra $10 added to your paycheck. However, most people tend to
behave as if the difference between $0 and $10 is greater than the difference between,
say, $300 and $310. So a person who won’t drive across town after work to earn a $10
bonus on next week’s paycheck might gladly make the same trip to pick up a $10 gift
certificate. Understanding these biases and how they affect people’s purchasing patterns
and other economic decisions has proven so important that Daniel Kahneman received
the 2002 Nobel Prize in economics for his research in this area.
People are also biased in how they think about probability. For example, we tend to
overestimate the probability of rare events and to underestimate the probability of frequent ones (Kahneman & Tversky, 1984). This bias helps explain why people gamble
in casinos and enter lotteries. The odds are against them, and the decision to gamble
has a negative expected value, but because people overestimate the probability of winning, they associate a positive expected value with gambling. In one study, not even a
course that highlighted gambling’s mathematical disadvantages could change university
students’ gambling behavior (Williams & Connolly, 2006). The tendency to overestimate rare events is amplified by the availability heuristic: Vivid memories of rare gambling successes and the publicity given to lottery winners encourage people to recall
gains rather than losses.
Gains, Losses, and Probabilities
utility In decision making, any subjective measure of value.
expected value The total benefit to be
expected of a decision if it were repeated on several occasions.
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Decision Making
A HIGHLY UNLIKELY OUTCOME
Lottery agencies try to attract business by
creating memorable images of big winners. They know that, like other decisions, people’s ticket buying will be
guided by the availability heuristic and
the tendency to overestimate the probability of rare events. Did you ever notice
that lottery ads and web sites never show
or talk about the millions of players who
win nothing?
Sometimes, our bias in estimating probability costs more than money. For example,
many people underestimate the risk of infection by HIV/AIDS and continue to engage
in unprotected sex (Specter, 2005). And after the September 11, 2001, terrorist attacks
on the United States, the risks of flying seemed so high that many more people than
usual decided to travel by car instead. Yet driving is more dangerous overall than flying, so the decision to drive actually increased these people’s risk of death. With more
cars on the road, there were 350 more traffic fatalities in the last three months of 2001
than there were during the same period in previous years (Gigerenzer, 2004). Similar
bias in risk perception leads many people to buy a big, heavy sport utility vehicle that
makes them feel safe, even though the chances of serious injury in an SUV are actually greater than in a minivan or family sedan (Gladwell, 2004).
Another bias in estimating probability is called the gambler’s fallacy: People believe
that the probability of future events in a random process will change depending on
past events. This belief is false. For example, if you flip a coin and it comes up heads
ten times in a row, what is the likelihood of tails on the next flip? Although some people think otherwise, the chance that it will come up tails on the eleventh flip is still
50 percent, just as it was for the first ten flips. Yet many gamblers continue feeding a
slot machine that has not paid off much for hours, because they believe it is “due.”
Poor decision making can also stem from the human tendency to be unrealistically
confident in the accuracy of our predictions. Baruch Fischoff and Donald MacGregor
(1982) devised a clever way to study this bias. People were asked whether they believed
a certain event would occur and then were asked to say how confident they were about
their prediction. For example, they were asked whether a particular sports team would
win an upcoming game. After the events were over, the accuracy of the people’s forecasts was compared with their level of confidence. Sure enough, their confidence in
their predictions was consistently greater than their accuracy. This overconfidence
operates even when people make predictions concerning the accuracy of their own
memories (Bjork, 1998).
Almost everyone makes decisions that they later regret, but
these outcomes may not be due entirely to biased thinking about gains, losses, and
probabilities. Some decisions are not intended to maximize expected value but rather
to satisfy other goals, such as minimizing expected loss, producing a quick and easy
How Biased Are We?