Daniel Kahneman Nobel Lecture

statistical training does not eradicate intuitive heuristics such as representa-

tiveness, but only enables people to avoid some biases under favorable cir-

cumstances. The results shown in Figure 8, which were collected from statis-

tically knowledgeable graduate students, support this prediction. In the

absence of strong cues to remind them of their statistical knowledge, these re-

spondents made categorical predictions like everybody else – by representa-

tiveness. However, statistical sophistication made a difference in a stripped-

down version of the Linda problem, which required respondents to compare

the probabilities of Linda being “a bank teller” or “a bank teller who is active

in the feminist movement” (Tversky & Kahneman, 1983). The incidence of

errors remained high for the statistically naïve even in that transparent ver-

sion, but the error rate dropped dramatically among the sophisticated. 

The efficacy of System 2 is impaired by time pressure (Finucane, Alhakami,

Slovic, & Johnson, 2000) by concurrent involvement in a different cognitive

task (Gilbert, 1989, 1991, 2002), by performing the task in the evening for

‘morning people’ and in the morning for ‘evening people’ (Bodenhausen,

1990), and, surprisingly, by being in a good mood (Isen, Nygren, & Ashby,

1988; Bless et al., 1996). Conversely, the facility of System 2 is positively corre-

lated with intelligence (Stanovich & West, 2002), with ‘need for cognition’

(Shafir & LeBoeuf, 2002), and with exposure to statistical thinking (Nisbett et

al., 1983; Agnoli & Krantz, 1989; Agnoli, 1991). 

The observation that it is possible to design experiments in which ‘cogni-

tive illusions disappear’ has sometimes been used as an argument against the

usefulness of the notions of heuristics and biases (for example, Gigerenzer,

1991). In the present framework, however, there is no mystery about the con-

ditions under which illusions appear or disappear. An intuitive judgment that

violates a rule which the respondent accepts will be overridden, if the rule

comes early enough to the respondent’s mind. This argument is not circular,

because we have adequate scientific knowledge (as well as widely shared folk

knowledge) about the conditions that facilitate or impede the accessibility of

logical or statistical rules.

The examples of possible corrections in the Tom W. and Linda problems il-

lustrated two possible outcomes of the intervention of System 2: the intuitive

judgment may be adjusted, or else rejected and replaced by another conclu-

sion. A general prediction can be made about the former case, which is cer-

tainly the most frequent. Because the intuitive impression comes first, it is like-

ly to serve as an anchor for subsequent adjustments, and corrective adjustments

from anchors are normally insufficient. Variations on this theme are common

in the literature (Epley & Gilovich, 2002; Epstein, 1994; Gilbert, 2002; Griffin &

Tversky, 1992; Sloman, 2002; Wilson, Centerbar, & Brekke, 2002). 

The methodological implication of this analysis is that intuitive judgments

and preferences are best studied in between-subject designs. Within-subject

designs with multiple trials encourage the adoption of simplifying strategies

in which answers are computed mechanically, without delving into the

specifics of each problem. Factorial designs are particularly undesirable, be-

cause they provide an unmistakable cue that every factor that is manipulated

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must be relevant to the judgment (Kahneman & Frederick, 2002). It is inap-

propriate to study intuitive judgments in conditions that are guaranteed to

destroy their intuitive character. The difficulties of these experimental de-

signs were noted long ago by Kahneman and Tversky (1982a), who pointed

out that “Within-subject designs are associated with significant problems of in-

terpretation in several areas of psychological research (Poulton, 1975). In

studies of intuition, they are liable to induce the effect that they are intended

to test” (p. 500). Unfortunately, this methodological caution has been widely

ignored.

6. PROTOTYPE HEURISTICS

This section introduces a family of prototype heuristics, which share a com-

mon mechanism and a remarkably consistent pattern of cognitive illusions,

analogous to the effects observed in the Tom W. and in the Linda problems

(Kahneman & Frederick, 2002). Prototype heuristics can be roughly de-

scribed as the substitution of an average for a sum – a process that has been

extensively studied by Anderson in other contexts (e.g., Anderson, 1981, ch.

pp. 58–70; 1991a,b). The section also discusses the conditions under which

System 2 prevents or reduces the biases associated with these heuristics. 

Extensional and prototype attributes

The target assessments in several significant tasks of judgment and decision

making are extensional attributes of categories or sets. The value of an exten-

sional attribute in a set is an aggregate (not necessarily additive) of the values

over its extension. Each of the following tasks is illustrated by an example of

an extensional attribute and by the relevant measure of extension. The argu-

ment of this section is that the extensional attributes in these tasks are low in

accessibility, and are therefore candidates for heuristic substitution.

(i)

category prediction (e.g., the probability that the set of bank tellers contains

Linda / the number of bank tellers);

(ii)

pricing a quantity of public or private goods (e.g., the personal dollar 

value of saving a certain number of birds from drowning in oil ponds / the num-

ber of birds);

(iii) global evaluation of a past experience that extended over time (e.g., the

overall aversiveness of a painful medical procedure / the duration of the proce-

dure);

(iv)

assessment of the support that a sample of observations provides for a

hypothesis (e.g., the probability that a specified sample of colored balls has been

drawn from one urn rather than another / the number of balls).

Extensional attributes are governed by a general principle of conditional

adding, which dictates that each element of the set adds to the overall value

an amount that depends on the elements already included. In simple cases,

the value is additive: the total length of the set of lines in Figure 3 is just the

sum of their separate lengths. In other cases, each positive element of the set

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