2.6.5. Debate over the Quality of Human Judgment
184.108.40.206. Deficiencies in Human Judgment
At some level, human judgment is an unavoidable element of all human decisions.
The question, then, naturally arises: How good is human judgment? Psychological
studies of human judgment provide evidence for shortcomings and systematic biases
in human decisionmaking. Furthermore, not only do peopleincluding expertssuffer
various forms of myopia; they also often are oblivious of the fact. Indeed,
statistical linear models summarizing the relationship between a set of predictor
variables and a predicted outcome often (repeatedly) perform better than intuitive
expert judgments (or subjective expert opinions). Burgeoning empirical evidence
suggests that humans, including experts, can be inept at making judgments, particularly
under conditions of high uncertainty.
Since the early 1970s, psychologists repeatedly have demonstrated human judgmental
error and linked these errors to the operational nature of mental processes.
The idea, spelled out in Kahneman et al. (1982), is that, because of
limited mental processing capacity, humans rely on strategies of simplification,
or mental heuristics, to reduce the complexity of judgment tasks. Although this
strategy facilitates decisionmaking, these procedures are vulnerable to systematic
error and bias.
220.127.116.11. Violation of Probability Laws
In a classic series of publications, Tversky and Kahneman (1974, 1983) and
Kahneman and Tversky (1979, 1996) claim that human judgment under uncertainty
violates normative rules of probability theory. For example, Tversky and Kahneman
(1983) invoke the "judgment by a representativeness" heuristic to
explain evidence for the conjunction fallacy, whereby a conjunction of
events is judged to be more likely than one of its constituents. This is a violation
of a perfectly simple principle of probability logic: If A includes B, the probability
of B cannot exceed A. Nevertheless, respondents consistently give a higher likelihood
to the possibility of a subset or joint event than to the whole set, thereby
violating the conjunction rule. Typically, respondents judge likelihood by representativeness
(or stereotypes) and thus fail to integrate statistically relevant factors.
However, Gigerenzer (1994, 1996) argues that people are naturally adapted to
reasoning with probabilities in the form of frequencies and that the conjunction
fallacy "disappears" if reasoning is in the form of frequencies. Several
studies report that violations of the conjunction rule are rare if respondents
are asked to consider the relative frequency of events rather than the probability
of a single event.
Kahneman and Tversky (1996) disagree and argue that the frequency format provides
respondents with a powerful cue to the relation of inclusion between sets that
are explicitly compared or evaluated in immediate succession. When the structure
of the conjunction is made more apparent, respondents who appreciate the constraint
supplied by the rule will be less likely to violate it.
Kahneman and Lovallo (1993) argue that people have a strong tendency to regard
problems as unique although they would be viewed more advantageously as instances
of a broader class. People pay particular attention to the distinguishing features
of a particular case and reject analogies to other instances of the same general
type as crudely superficial and unappealing. Consequently, they fall prey to
fallacies of planning by anchoring their estimates on present values or extrapolations
of current trends. Despite differing causal theories, both approaches find evidence
for poor judgment under uncertainty or, alternatively, evidence that people
are better off not attempting to assess probabilities for single events.
Nonetheless, public understanding of likelihood seems to be improved by adoption
of frequentist formats. Several studies have shown that experts have great difficulty
reasoning with subjective probabilities for unique or single events. However,
respondents apparently are much more successful when the same problems are presented
with frequencies rather than probabilities. Although experts have difficulties
with the probability versionmost give wrong answersmost undergraduates
readily provide the correct answer to similar problems constructed with frequencies.
Psychological research suggests that measures of risk that are communicated
in terms of frequencies rather than probabilities will be more readily understood
and rationally responded to, although IAMs need to translate these frequencies
into probability distributions (e.g., Morgan and Dowlatabadi, 1996) to portray
the wide range of outcomes that currently reflect estimates in the literature
and by most IPCC authors.