MIT Encyclopedia of the Cognitive Sciences - Cryptome

424 Judgment Heuristics
small samples would affirm research hypotheses, leading
them to propose study designs with surprisingly low statistical
power. Of course, well-trained scientists can calculate the
correct value for power analyses. However, to do so, they
must realize that their heuristic judgment is faulty. Systematic
reviews have found a high rate of published studies with low
statistical power, suggesting that practicing scientists often
lack this intuition (Cohen 1962).
Kahneman and Tversky (1972) subsequently subsumed
this tendency under the more general representativeness
heuristic. Users of this rule assess the likelihood of an event
by how well it captures the salient properties of the process
producing it. Although sometimes useful, this heuristic will
produce biases whenever features that determine likelihood
are insufficiently salient (or when irrelevant features capture
people’s attention). As a result, predicting the behavior of
people relying on representativeness requires both a substantive
understanding of how they judge salience and a normative
understanding of what features really matter. Bias
arises when the two are misaligned, or when people apply
appropriate rules ineffectively.
Sample size is one normatively relevant feature that
tends to be neglected. A second is the population frequency
of a behavior, when making predictions for a specific individual.
People feel that the observed properties of the individual
(sometimes called “individuating” or “case-specific”
information) need to be represented in future events, even
when those observations are not that robust (e.g., small sample,
unreliable source).
Bias can also arise when normatively relevant features
are recognized but misunderstood. Thus, people know that
random processes should show variability, but expect too
much of it. One familiar expression is the “gambler’s fallacy,”
leading people to expect, say, a “head” coin flip after
four tails, but not after four alternations of head-tail. An
engaging example is the unwarranted perception that basketball
players have a “hot hand,” caused by not realizing
how often such (unrandom-looking) streaks arise by chance
(Gilovich, Vallone, and Tversky 1985). In a sense, representativeness
is a metaheuristic, a very general rule from which
more specific ones are derived for particular situations. As a
result, researchers need to predict how a heuristic will be
used in order to generate testable predictions for people’s
judgments. Where those predictions fail, it may be that the
heuristic was not used at all or that it was not used in that
particular way.
Two other (meta)heuristics are availability and anchoring
and adjustment. Reliance on availability means judging
an event as likely to the extent that one can remember examples
or imagine it happening. It can lead one astray when
instances of an event are disproportionately (un)available in
MEMORY. Reliance on anchoring and adjustment means
estimating a quantity by thinking of why it might be larger
or smaller than some initial value. Typically, people adjust
too little, leaving them unduly “anchored” in that initial
value, however arbitrarily it has been selected. Obviously,
there are many ways in which examples can be produced,
anchors selected, and adjustments made. The better these
processes are understood, the sharper the predictions that
can be made for heuristic-based judgments.
These seminal papers have produced a large research literature
(Kahneman, Slovic, and Tversky 1982). Their influence
can be traced to several converging factors (Dawes
1997; Jungermann 1983), including: (1) The initial demonstrations
have proven quite robust, facilitating replications
in new domains and the exploration of boundary conditions
(e.g., Plous 1993). (2) The effects can be described in
piquant ways, which present readers and investigators in a
flattering light (able to catch others making mistakes). (3)
The perspective fits the cognitive revolution’s subtext of
tracing human failures to unintended side effects of generally
adaptive processes. (4) The heuristics operationalize
Simon’s (1957) notions of BOUNDED RATIONALITY in ways
subject to experimental manipulation.
The heuristics-and-biases metaphor also provides an
organizing theme for the broader literature on failures of
human DECISION MAKING. For example, many studies have
found people to be insensitive to the extent of their own
knowledge (Keren 1991; Yates 1990). When this trend
emerges as overconfidence, one contributor is the tendency
to look for reasons supporting favored beliefs (Koriat, Lichtenstein,
and Fischhoff 1980). Although that search is a sensible
part of hypothesis testing, it can produce bias when
done without a complementary sensitivity to disconfirming
evidence (Fischhoff and Beyth-Marom 1983). Other studies
have examined hindsight bias, the tendency to exaggerate
the predictability of past events (or reported facts; Fischhoff
1975). One apparent source of that bias is automatically
making sense of new information as it arrives. Such rapid
updating should facilitate learning—at the price of obscuring
how much has been learned. Underestimating what one
had to learn may mean underestimating what one still has to
learn, thereby promoting overconfidence.
Scientists working within this tradition have, naturally,
worried about the generality of these behavioral patterns.
One central concern has been whether laboratory results
extend to high-stakes decisions, especially ones with experts
working on familiar tasks. Unfortunately, it is not that easy
to provide significant positive stakes (or threaten significant
losses) or to create appropriate tasks for experts. Those
studies that have been conducted suggest that stakes alone
do not eliminate bias nor lead people, even experts, to abandon
faulty judgments (Camerer 1995).
In addition to experimental evidence, there are anecdotal
reports and systematic observations of real-world expert
performance showing biases that can be attributed to using
heuristics (Gilovich 1991; Mowen 1993). For example,
overconfidence has been observed in the confidence assessments
of particle physicists, demographers, and economists
(Henrion and Fischhoff 1986). A noteworthy exception is
weather forecasters, whose assessments of the probability
of precipitation are remarkably accurate (e.g., it rains 70
percent of the times that they forecast a 70 percent chance
of rain; Murphy and Winkler 1992). These experts make
many judgments under conditions conducive to LEARNING:
prompt, unambiguous feedback that rewards them for accuracy
(rather than, say, for bravado or hedging). Thus, these
judgments may be a learnable cognitive skill. That process
may involve using conventional heuristics more effectively
or acquiring better ones.