**Risk as Analysis and Risk as Feelings: Some Thoughts**

**about Affect, Reason, Risk, and Rationality**

Paul Slovic, Melissa L. Finucane, Ellen Peters, and Donald MacGregor

Decision Research

1201 Oak Street

Eugene, OR 97401

[http://www.decisionresearch.org](http://www.decisionresearch.org/)

Paper presented at the National Cancer Institute workshop on
Conceptualizing and Measuring Risk Perceptions, Washington, D.C.,
February 13-14, 2003

Risk as Analysis and Risk as Feelings: Some Thoughts

about Affect, Reason, Risk, and Rationality

Introduction

Risk in the modern world is confronted and dealt with in three
fundamental ways. *Risk as feelings* refers to our fast, instinctive,
and intuitive reactions to danger. *Risk as analysis* brings logic,
reason, and scientific deliberation to bear on hazard management. When
our ancient instincts and our modern scientific analyses clash, we
become painfully aware of a third reality . . . *risk as politics*. In
the present paper we shall examine what recent research in psychology
and cognitive neuroscience tells us about this first dimension, "risk as
feelings," an important vestige of our evolutionary journey.

That intuitive feelings are still the predominant method by which human
beings evaluate risk is cleverly illustrated in a cartoon by Garry
Trudeau (Figure 1). Trudeau's two characters decide whether to greet one
another on a city street by employing a systematic analysis of the risks
and risk-mitigating factors. We instantly recognize that no one in such
a situation would ever be this analytical, even if their life was at
stake. Most risk analysis is handled quickly and automatically by what
we shall describe later as the "experiential" mode of thinking.

Background and Theory: The Importance of Affect

Although the visceral emotion of fear certainly plays a role in risk as
feelings, we shall focus here on a "faint whisper of emotion" called
*affect*. As used here, "affect" means the specific quality of
"goodness" or "badness" (i) experienced as a feeling state (with or
without consciousness) and (ii) demarcating a positive or negative
quality of a stimulus. Affective responses occur rapidly and
automatically -- note how quickly you sense the feelings associated with
the stimulus word "treasure" or the word "hate." We argue that reliance
on such feelings can be characterized as "the affect heuristic." In this
paper, we trace the development of the affect heuristic across a variety
of research paths followed by ourselves and many others. We also discuss
some of the important practical implications resulting from ways that
this heuristic impacts the way we perceive and evaluate risk, and, more
generally, the way it effects all human decision making.

*Two Modes of Thinking*

Affect also plays a central role in what have come to be known as
dual-process theories of thinking, knowing, and information processing.
(Chaiken & Trope, 1999; Kahneman & Frederick, 2002; Sloman, 1996) As
Epstein (1994) observed,

> There is no dearth of evidence in every day life that people apprehend
> reality in two fundamentally different ways, one variously labeled
> intuitive, automatic, natural, non-verbal, narrative, and
> experiential, and the other analytical, deliberative, verbal, and
> rational. (p. 710)

Table I, adapted from Epstein, further compares these modes of thought.
One of the main characteristics of the experiential system is its
affective basis. Although analysis is certainly important in some
decision-making circumstances, reliance on affect and emotion is a
quicker, easier, and more efficient way to navigate in a complex,
uncertain, and sometimes dangerous world. Many theorists have given
affect a direct and primary role in motivating behavior (Barrett &
Salovey, 2002; Clark & Fiske, 1982; Forgas, 2000; Le Doux, 1996; Mowrer,
1960; Tomkins, 1962, 1963; Zajonc, 1980). Epstein's (1994) view on this
is as follows:

> The experiential system is assumed to be intimately associated with
> the experience of affect, . . . which refer\[s\] to subtle feelings of
> which people are often unaware. When a person responds to an
> emotionally significant event . . . the experiential system
> automatically searches its memory banks for related events, including
> their emotional accompaniments . . . If the activated feelings are
> pleasant, they motivate actions and thoughts anticipated to reproduce
> the feelings. If the feelings are unpleasant, they motivate actions
> and thoughts anticipated to avoid the feelings. (p. 716)

Whereas Epstein labeled the right side of Table I the "rational system,"
we have renamed it the "analytic system," in recognition that there are
strong elements of rationality in both systems.

It was the experiential system, after all, that enabled human beings to
survive during their long period of evolution. Long before there was
probability theory, risk assessment, and decision analysis, there were
intuition, instinct, and gut feeling to tell us whether an animal was
safe to approach or the water was safe to drink. As life became more
complex and humans gained more control over their environment, analytic
tools were invented to "boost" the rationality of our experiential
thinking. Subsequently, analytic thinking was placed on a pedestal and
portrayed as the epitome of rationality. Affect and emotions were seen
as interfering with reason.

The importance of affect is being recognized increasingly by decision
researchers. A strong early proponent of the importance of affect in
decision making was Zajonc, (1980) who argued that affective reactions
to stimuli are often the very first reactions, occurring automatically
and subsequently guiding information processing and judgment. If Zajonc
is correct, then affective reactions may serve as orienting mechanisms,
helping us navigate quickly and efficiently through a complex,
uncertain, and sometimes dangerous world. Important work on affect and
decision making has also been done by Isen (1993), Janis and Mann
(1977), Johnson and Tversky (1983), Kahneman, Schkade, and Sunstein
(1998), Kahneman and Snell (1990), Loewenstein (1996), Loewenstein,
Weber, Hsee, and Welch (2001), Mellers (2000), Mellers, Schwartz, Ho,
and Ritov (1997), Rottenstreich and Hsee (2001), Rozin, Haidt, and
McCauley (1993), Schwarz and Clore (1988), Slovic, Finucane, Peters, and
MacGregor (2002), and Wilson et al. (1993).

One of the most comprehensive and dramatic theoretical accounts of the
role of affect and emotion in decision making was presented by the
neurologist, Antonio Damasio (1994). In seeking to determine "what in
the brain allows humans to behave rationally," Damasio argued that
thought is made largely from images, broadly construed to include
perceptual and symbolic representations. A lifetime of learning leads
these images to become "marked" by positive and negative feelings linked
directly or indirectly to somatic or bodily states. When a negative
somatic marker is linked to an image of a future outcome, it sounds an
alarm. When a positive marker is associated with the outcome image, it
becomes a beacon of incentive. Damasio hypothesized that somatic markers
increase the accuracy and efficiency of the decision process and their
absence, observed in people with certain types of brain damage, degrades
decision performance.

We now recognize that the experiential mode of thinking and the analytic
mode of thinking are continually active, interacting in what we have
characterized as "the dance of affect and reason" (Finucane, Peters, &
Slovic, in press). While we may be able to "do the right thing" without
analysis (e.g., dodge a falling object), it is unlikely that we can
employ analytic thinking rationally without guidance from affect
somewhere along the line. Affect is essential to rational action. As
Damasio (1994) observes:

> The strategies of human reason probably did not develop, in either
> evolution or any single individual, without the guiding force of the
> mechanisms of biological regulation, of which emotion and feeling are
> notable expressions. Moreover, even after reasoning strategies become
> established . . . their effective deployment probably depends, to a
> considerable extent, on a continued ability to experience feelings.
> (p. xii)

*The Affect Heuristic*

The feelings that become salient in a judgment or decision making
process depend on characteristics of the individual and the task as well
as the interaction between them. Individuals differ in the way they
react affectively, and in their tendency to rely upon experiential
thinking (Gasper & Clore, 1998; Peters & Slovic, 2000). As will be shown
in this paper, tasks differ regarding the evaluability (relative
affective salience) of information. These differences result in the
affective qualities of a stimulus image being "mapped" or interpreted in
diverse ways. The salient qualities of real or imagined stimuli then
evoke images (perceptual and symbolic interpretations) that may be made
up of both affective and instrumental dimensions.

The mapping of affective information determines the contribution
stimulus images make to an individual's "affect pool." All of the images
in people's minds are tagged or marked to varying degrees with affect.
The affect pool contains all the positive and negative markers
associated (consciously or unconsciously) with the images. The intensity
of the markers varies with the images.

People consult or "sense" the affect pool in the process of making
judgments. Just as imaginability, memorability, and similarity serve as
cues for probability judgments, (e.g., the availability and
representativeness heuristics, Kahneman, Slovic, & Tversky, 1982),
affect may serve as a cue for many important judgments (including
probability judgments). Using an overall, readily available affective
impression can be easier and more efficient than weighing the pros and
cons of various reasons or retrieving relevant examples from memory,
especially when the required judgment or decision is complex or mental
resources are limited. This characterization of a mental short-cut has
led us to label the use of affect a "heuristic" (Finucane, Alhakami,
Slovic, & Johnson, 2000).

# Empirical Support for the Affect Heuristic

Support for the affect heuristic comes from a diverse set of empirical
studies, only a few of which will be reviewed here.

# *Early Research: Dread and Outrage in Risk Perception*

Evidence of risk as feelings was present (though not fully appreciated)
in early psychometric studies of risk perception (Fischhoff, Slovic,
Lichtenstein, Read, & Combs, 1978; Slovic, 1987). Those studies showed
that feelings of dread were the major determiner of public perception
and acceptance of risk for a wide range of hazards. Sandman, noting that
dread was also associated with factors such as voluntariness,
controllability, lethality, and fairness, incorporated these qualities
into his "outrage model" (Sandman, 1989). Reliance on outrage was, in
Sandman's view, the major reason that public evaluations of risk
differed from expert evaluations (based on analysis of hazard; e.g.,
mortality statistics).

# *Risk and Benefit Judgments*

The earliest studies of risk perception also found that, whereas risk
and benefit tend to be positively correlated in the world, they are
negatively correlated in people's minds (and judgments, Fischhoff et
al., 1978). The significance of this finding for the affect heuristic
was not realized until a study by Alhakami and Slovic (1994) found that
the inverse relationship between perceived risk and perceived benefit of
an activity (e.g., using pesticides) was linked to the strength of
positive or negative affect associated with that activity as measured by
rating the activity on bipolar scales such as *good/bad, nice/awful,
dread/not dread,* and so forth. This result implies that people base
their judgments of an activity or a technology not only on what they
*think* about it but also on how they *feel* about it. If their feelings
towards an activity are favorable, they are moved toward judging the
risks as low and the benefits as high; if their feelings toward it are
unfavorable, they tend to judge the opposite---high risk and low
benefit. Under this model, affect comes prior to, and directs, judgments
of risk and benefit, much as Zajonc proposed. This process, which we
have called "the affect heuristic" (see Figure 2), suggests that, if a
general affective view guides perceptions of risk and benefit, providing
information about benefit should change perception of risk and
vice-versa (see Figure 3). For example, information stating that benefit
is high for a technology such as nuclear power would lead to more
positive overall affect which would, in turn, decrease perceived risk
(Figure 3A).

Finucane et al. (2000) conducted this experi­ment, providing four
different kinds of information designed to manipulate affect by
increasing or de­creasing perceived benefit or by increasing or
de­creasing perceived risk for each of three technologies. The
predictions were confirmed. Be­cause by design there was no apparent
logical relationship between the information provided and the
nonmanipulated variable, these data support the theory that risk and
benefit judgments are in­fluenced, at least in part, by the overall
affective evaluation (which was influenced by the informa­tion provided).
Further support for the affect heu­ristic came from a second experiment
by Finucane et al. finding that the inverse relationship between
perceived risks and benefits increased greatly un­der time pressure, when
opportunity for analytic deliberation was reduced. These two experiments
are important because they demonstrate that af­fect influences judgment
directly and is not simply a response to a prior analytic evaluation.

Further support for the model in Figure 2 has come from two very
different domains---toxicology and finance. Slovic, MacGregor, Malmfors,
and Purchase (n.d.) surveyed members of the British Toxicological
Society and found that these experts, too, produced the same inverse
relation between their risk and benefit judgments. As expected, the
strength of the inverse relation was found to be mediated by the
toxicologists' affective reactions toward the hazard items being judged.
In a second study, these same toxicologists were asked to make a "quick
intuitive rating" for each of 30 chemical items (e.g., benzene, aspirin,
second-hand cigarette smoke, dioxin in food) on an affect scale
(bad-good). Next, they were asked to judge the degree of risk associated
with a very small exposure to the chemical, defined as an exposure that
is less than 1/100^th^ the exposure level that would begin to cause
concern for a regulatory agency. Rationally, because exposure was so
low, one might expect these risk judgments to be uniformly low and
unvarying, resulting in little or no correlation with the ratings of
affect. Instead, there was a strong correlation across chemicals between
affect and judged risk of a very small exposure. When the affect rating
was strongly negative, judged risk of a very small exposure was high;
when affect was positive, judged risk was small. Almost every respondent
(95 out of 97) showed this negative correlation (the median correlation
was -.50). Importantly, those toxicologists who produced strong inverse
relations between risk and benefit judgments in the first study also
were more likely to exhibit a high correspondence between their
judgments of affect and risk in the second study. In other words, across
two different tasks, reliable individual differences emerged in
toxicologists' reliance on affective processes in judgments of chemical
risks.

In the realm of finance, Ganzach (2001) found support for a model in
which analysts base their judgments of risk and return for unfamiliar
stocks upon a global attitude. If stocks were perceived as good, they
were judged to have high return and low risk, whereas if they were
perceived as bad, they were judged to be low in return and high in risk.
However, for familiar stocks, perceived risk and return were positively
correlated, rather than being driven by a global attitude.

## Judgments of Probability, Relative Frequency, and Risk

The affect heuristic has much in common with the model of "risk as
feelings" proposed by Loewenstein et al. (2001) and with dual process
theories put forth by Epstein (1994), Sloman (1996), and others. Recall
that Epstein argues that individuals apprehend reality by two
interactive, parallel processing systems. The *analytic* system is slow
and deliberative, and functions by way of established rules of logic and
evidence (e.g., probability theory). The *experiential* system encodes
reality in images, metaphors, and narratives to which affective feelings
have become attached.

To demonstrate the influence of the experiential system, Denes-Raj and
Epstein (1994) showed that, when offered a chance to win \$1.00 by
drawing a red jelly bean from an urn, individuals often elected to draw
from a bowl containing a greater absolute number, but a smaller
proportion, of red beans (e.g., 7 in 100) than from a bowl with fewer
red beans but a better probability of winning (e.g., 1 in 10). These
individuals reported that, although they knew the probabilities were
against them, they *felt* they had a better chance when there were more
red beans.

We can characterize Epstein's subjects as following a mental strategy of
"imaging the numerator" (i.e., the number of red beans) and neglecting
the denominator (the number of beans in the bowl). Consistent with the
affect heuristic, images of winning beans convey positive affect that
motivates choice.

Although the jelly bean experiment may seem frivolous, imaging the
numerator brings affect to bear on judgments in ways that can be both
non-intuitive and consequential. Slovic, Monahan, and MacGregor (2000)
demonstrated this in a series of studies in which experienced forensic
psychologists and psychiatrists were asked to judge the likelihood that
a mental patient would commit an act of violence within 6 months after
being discharged from the hospital. An important finding was that
clinicians who were given another expert's assessment of a patient's
risk of violence framed in terms of relative frequency (e.g., of every
100 patients similar to Mr. Jones, 10 are estimated to commit an act of
violence to others...") subsequently labeled Mr. Jones as more dangerous
than did clinicians who were shown a statistically "equivalent" risk
expressed as a probability (e.g., "Patients similar to Mr. Jones are
estimated to have a 10% chance of committing an act of violence to
others").

Not surprisingly, when clinicians were told that "20 out of every 100
patients similar to Mr. Jones are estimated to commit an act of
violence," 41% would refuse to discharge the patient. But when another
group of clinicians was given the risk as "patients similar to Mr. Jones
are estimated to have a 20% chance of committing an act of violence,"
only 21% would refuse to discharge the patient. Similar results have
been found by Yamagishi (1997), whose judges rated a disease that kills
1,286 people out of every 10,000 as more as more dangerous than one that
kills 24.14% of the population.

Follow-up studies showed that representations of risk in the form of
individual probabilities of 10% or 20% led to relatively benign images
of one person, unlikely to harm anyone, whereas the "equivalent"
frequentistic representations created frightening images of violent
patients (example: "Some guy going crazy and killing someone"). These
affect-laden images likely induced greater perceptions of risk in
response to the relative-frequency frames.

Although frequency formats produce affect-laden imagery, story and
narrative formats appear to do even better in that regard. Hendrickx,
Vlek, and Oppewal (1989) found that warnings were more effective when,
rather than being presented in terms of relative frequencies of harm,
they were presented in the form of vivid, affect-laden scenarios and
anecdotes. Sanfey and Hastie (1998) found that compared with respondents
given information in bar graphs or data tables, respondents given
narrative information more accurately estimated the performance of a set
of marathon runners. Furthermore, Pennington and Hastie (1993) found
that jurors construct narrative-like summations of trial evidence to
help them process their judgments of guilt or innocence.

Perhaps the biases in probability and frequency judgment that have been
attributed to the availability heuristic (Tversky & Kahneman, 1973) may
be due, at least in part, to affect. Availability may work not only
through ease of recall or imaginability, but because remembered and
imagined images come tagged with affect. For example, Lichtenstein,
Slovic, Fischhoff, Layman, and Combs (1978) invoked availability to
explain why judged frequencies of highly publicized causes of death
(e.g., accidents, homicides, fires, tornadoes, and cancer) were
relatively overestimated and underpublicized causes (e.g., diabetes,
stroke, asthma, tuberculosis) were underestimated. The highly publicized
causes appear to be more affectively charged, that is, more sensational,
and this may account both for their prominence in the media and their
relatively overestimated frequencies.

*Proportion Dominance*

There appears to be one generic information format that is highly
evaluable (e.g., highly affective), leading it to carry great weight in
many judgment tasks. This is a representation characterizing an
attribute as a proportion or percentage of something, or as a
probability.

Proportion or probability dominance was evident in an early study by
Slovic and Lichtenstein (1968) that had people rate the attractiveness
of various two-outcome gambles. Ratings of a gamble's attractiveness
were determined much more strongly by the probabilities of winning and
losing than by the monetary outcomes. This basic finding has been
replicated many times (Goldstein & Einhorn, 1987; Ordóñez & Benson,
1997).

Slovic et al. (2002) tested the limits of this probability dominance by
asking one group of subjects to rate the attractiveness of a simple
gamble (7/36, win \$9; on a 0-20 scale and asking a second group to rate
a similar gamble with a small loss (7/36, win \$9; 29/36, lose 5¢) on
the same scale. The data were anomalous from the perspective of economic
theory, but expected from the perspective of the affect heuristic. The
mean response to the first gamble was 9.4. When a loss of 5¢ was added,
the mean attractiveness jumped to 14.9 and there was almost no overlap
between the distribution of responses around this mean and the responses
for the group judging the gamble that had no loss.

Slovic also performed a conjoint analysis where each subject rated one
of 16 gambles formed by crossing four levels of probability (7/36,
14/36, 21/36, 28/36) with four levels of payoff (\$3, \$6, \$9, \$12 in
one study and \$30, \$60, \$90, \$120 in another). He found that,
although subjects wanted to weight probability and payoff relatively
equally in judging attractiveness (and thought they had done so) the
actual weighting was 5 to 16 times greater for probability than for
payoff.

We hypothesize that these curious findings can be explained by reference
to the notion of affective mapping. According to this view, a
probability maps relatively precisely onto the attractiveness scale,
because it has an upper and lower bound and people know where a given
value falls within that range. In contrast, the mapping of a dollar
outcome (e.g., \$9) onto the scale is diffuse, reflecting a failure to
know whether \$9 is good or bad, attractive or unattractive. Thus, the
impression formed by the gamble offering \$9 to win with no losing
payoff is dominated by the rather unattractive impression produced by
the 7/36 probability of winning. However, adding a very small loss to
the payoff dimension puts the \$9 payoff in perspective and thus gives
it meaning. The combination of a possible \$9 gain and a 5¢ loss is a
*very attractive* win/lose ratio, leading to a relatively precise
mapping onto the upper part of the scale. Whereas the imprecise mapping
of the \$9 carries little weight in the averaging process, the more
precise and now favorable impression of (\$9: --5¢) carries more weight,
thus leading to an increase in the overall favorability of the gamble.

Proportion dominance surfaces in a powerful way in a very different
context, the life-saving interventions studied by Baron (1997),
Fetherstonhaugh, Slovic, Johnson, and Friedrich (1997), Friedrich et al.
(1999), and Jenni and Loewenstein (1997). These studies found that,
unless the number of lives saved is explicitly comparable from one
intervention to another, evaluation is dominated by the proportion of
lives saved (relative to the population at risk), rather than the actual
number of lives saved.

The results of our lifesaving study (Fetherstonhaugh et al., 1997) are
important because they imply that a specified number of human lives may
not carry precise affective meaning, similar to the conclusion we drew
about stated payoffs (e.g., \$9) in the gambling studies. The gamble
studies suggested an analogous experiment with lifesaving. In the
context of a decision pertaining to airport safety, my colleagues and I
asked people to evaluate the attractiveness of purchasing new equipment
for use in the event of a crash landing of an airliner. In one
condition, subjects were told that this equipment affords a chance of
saving 150 lives that would be in jeopardy in such an event. A second
group of subjects were told that this equipment affords a chance of
saving 98% of the 150 lives that would be in jeopardy. We predicted
that, because saving 150 lives is diffusely good, hence only weakly
evaluable, whereas saving 98% of something is clearly very good, support
for purchasing this equipment would be much greater in the 98%
condition. We predicted that other high percentages would also lead to
greater support, even though the number of lives saved was fewer. The
results, reported in Slovic et al. (2002) confirmed these predictions
(See Figure 4).

*Insensitivity to Probability*

Outcomes are not always affectively as vague as the quantities of money
and lives that were dominated by proportion in the above experiments.
When consequences carry sharp and strong affective meaning, as is the
case with a lottery jackpot or a cancer, the opposite phenomenon occurs
-- variation in probability often carries too little weight. As
Loewenstein et al. (2001) observe, one's images and feelings toward
winning the lottery are likely to be similar whether the probability of
winning is one in 10 million or one in 10,000. They further note that
responses to uncertain situations appear to have an all or none
characteristic that is sensitive to the *possibility* rather than the
*probability* of strong positive or negative consequences, causing very
small probabilities to carry great weight. This they argue, helps
explain many paradoxical findings such as the simultaneous prevalence of
gambling and the purchasing of insurance. It also explains why societal
concerns about hazards such as nuclear power and exposure to extremely
small amounts of toxic chemicals fail to recede in response to
information about the very small probabilities of the feared
consequences from such hazards. Support for these arguments comes from
Rottenstreich and Hsee (2001) who show that, if the potential outcome of
a gamble is emotionally powerful, its attractiveness or unattractiveness
is relatively insensitive to changes in probability as great as from .99
to .01.

Failures of the Experiential System

Throughout this paper, we have portrayed the affect heuristic as the
centerpiece of the experiential mode of thinking, the dominant mode of
risk assessment and survival during the evolution of the human species.
But, like other heuristics that provide efficient and generally adaptive
responses but occasionally get us into trouble, reliance on affect can
also mislead us. Indeed, if it was always optimal to follow our
affective and experiential instincts, there would have been no need for
the rational/analytic system of thinking to have evolved and become so
prominent in human affairs.

There are two important ways that experiential thinking misguides us.
One results from the deliberate manipulation of our affective reactions
by those who wish to control our behaviors (advertising and marketing
exemplify this manipulation). The other results from the natural
limitations of the experiential system and the existence of stimuli in
our environment that are simply not amenable to valid affective
representation. The latter problem is discussed below.

Judgments and decisions can be faulty not only because their affective
components are manipulable, but also because they are subject to
inherent biases of the experiential system. For example, the affective
system seems designed to sensitize us to small changes in our
environment (e.g., the difference between 0 and 1 deaths) at the cost of
making us less able to appreciate and respond appropriately to larger
changes further away from zero (e.g., the difference between 500 deaths
and 600 deaths). Fetherstonhaugh et al. (1997) referred to this
insensitivity as "psychophysical numbing." Albert Szent-Gyorgi put it
another way: "I am deeply moved if I see one man suffering and would
risk my life for him. Then I talk impersonally about the possible
pulverization of our big cities, with a hundred million dead. I am
unable to multiply one man's suffering by a hundred million."

Similar problems arise when the outcomes that we must evaluate are
visceral in nature. Visceral factors include drive states such as
hunger, thirst, sexual desire, emotions, pain, and drug craving. They
have direct, hedonic impacts that have a powerful effect on behavior.
Although they produce strong feelings in the present moment, these
feelings are difficult if not impossible to recall or anticipate in a
veridical manner, a factor that plays a key role in the phenomenon of
addiction (Loewenstein, 1999):

> Unlike currently experienced visceral factors, which have a
> disproportionate impact on behavior, delayed visceral factors tend to
> be ignored or severely underweighted in decision making. Today's pain,
> hunger, anger, etc. are palpable, but the same sensations anticipated
> in the future receive little weight. (p. 240)

*The Decision to Smoke Cigarettes*

Cigarette smoking is a dangerous activity that takes place, one
cigarette at a time, often over many years and hundreds of thousands of
episodes. The questionable rationality of smoking decisions provides a
dramatic example of the difficulty that experiential thinking faces in
dealing with outcomes that change very slowly over time, are remote in
time, and are visceral in nature.

For many years, beginning smokers were portrayed as "young economists,"
rationally weighing the risks of smoking against the benefits when
deciding whether to initiate that activity (Viscusi, 1992), analogous to
the "street calculus" being spoofed in Figure 1. However, recent
research paints a different picture. This new account (Slovic, 2001)
shows young smokers acting experientially in the sense of giving little
or no conscious thought to risks or to the amount of smoking they will
be doing. Instead, they are driven by the affective impulses of the
moment, enjoying smoking as something new and exciting, a way to have
fun with their friends. Even after becoming "regulars," the great
majority of smokers expect to stop soon, regardless of how long they
have been smoking, how many cigarettes they currently smoke per day, or
how many previous unsuccessful attempts they have experienced. Only a
fraction actually quit, despite many attempts. The problem is nicotine
addiction, a visceral condition that young smokers recognize by name as
a consequence of smoking but do not understand experientially until they
are caught in its grip.

The failure of the experiential system to protect many young people from
the lure of smoking is nowhere more evident than in the responses to a
survey question that asked smokers: "If you had it to do all over again,
would you start smoking?" More than 85% of adult smokers and about 80%
of young smokers (ages 14--22) answered "no" (Slovic, 2001). Moreover,
the more individuals perceive themselves to be addicted, the more often
they have tried to quit, the longer they have been smoking, and the more
cigarettes they are currently smoking per day, the more likely they are
to answer "no" to this question.

The data indicate that most beginning smokers lack the experience to
appreciate how their future selves will perceive the risks from smoking
or how they will value the tradeoff between health and the need to
smoke. This is a strong repudiation of the model of informed rational
choice. It fits well with the findings indicating that smokers give
little conscious thought to risk when they begin to smoke. They appear
to be lured into the behavior by the prospects of fun and excitement.
Most begin to think of risk only after starting to smoke and gaining
what to them is new information about health risks.

These findings underscore the distinction that behavioral decision
theorists now make between decision utility and experience utility
(Kahneman, 1994; Kahneman & Snell, 1992; Loewenstein & Schkade, 1999).
Utility predicted or expected at the time of decision often differs
greatly from the quality and intensity of the hedonic experience that
actually occurs.

*Implications*

This experiential analysis has a number of implications for
interventions to reduce adolescent smoking.

[Ban advertising]{.underline}. The affect/experiential thinking account
show the need to ban tobacco advertising and promotion. Tobacco
marketers have understood the importance of imagery and affect for
decades. They have hired sophisticated researchers to do focus groups
and surveys designed to help them understand and exploit "smoker
psychology," and the results of these studies have guided marketing and
promotional activities that now exceed \$10 billion per year in the
United States. Companies learned that it is image and affect that
manipulate the behaviors of their target audiences. Thus, tobacco
advertising has virtually no informational value, and what little
informational content it does have (e.g., "light," "low tar") has been
found to be misleading. Positive imagery in advertising creates the
wrong impression of the "smoking experience." Through the workings of
the affect heuristic, it likely depresses the perception of smoking
risks. The repetitive exposure to smoking and cigarette brands through
advertising likely creates positive affect by means of what is known as
"the mere exposure effect" (Bornstein, 1989; Zajonc, 1980). As studies
using subliminal images show, the influence of affective imagery is
powerful, manipulative, and not under conscious control (Winkielman,
Zajonc, & Schwarz, 1997). Thus, people, young and old alike, are unaware
of these effects and are poorly equipped to defend against them.

Related implications are that antitobacco messages should be designed
with the same skill and appreciation of affect that protobacco messages
have exhibited. In addition, promotional activities such as giving
people cigarettes or clothing with brand logos and the like should be
prohibited. We know that such "endowments" manipulate affect and
preference (Knetsch, 1989).

[Create experiential knowledge]{.underline}. We also know that health
statistics designed to engage the analytic mind, have less impact on
youths than do experiential knowledge and imagery. The problem is that
valid experience is hard for young people to acquire. Adolescents need
opportunities to meet and learn from people who are caught in the grip
of nicotine addiction and from people who are suffering from
tobacco-induced illnesses. They need to become familiar with the misery
and feelings of self-loathing being experienced by regular smokers.

Time and risk are hard to understand experientially (Ainslie & Haslam,
1992; Loewenstein & Elster, 1992). Research should be undertaken to help
people deal with cumulative risk -- risk that increases very slowly but
surely over thousands of repeated acts. Similarly, we need to better
educate about the difference between dying at age 55 and dying at 70 or
80 for young people whose minds, from a temporal distance, see 55, 70,
and 80 as basically the same.

Conclusion

It is sobering to contemplate how elusive meaning is, due to its
dependence upon affect. Thus the forms of meaning that we take for
granted and upon which we justify immense effort and expense toward
gathering and disseminating "meaningful" information, may be illusory.
We cannot assume that an intelligent person can understand the meaning
of and properly act upon even the simplest of numbers such as amounts of
money or numbers of lives at risk, not to mention more esoteric measures
or statistics pertaining to risk, unless these numbers are infused with
affect.

Contemplating the workings of the affect heuristic helps us appreciate
Damasio's contention that rationality is not only a product of the
analytical mind, but of the experiential mind as well. The perception
and integration of affective feelings, within the experiential system,
appears to be the kind of high-level maximization process postulated by
economic theories since the days of Jeremy Bentham. These feelings form
the neural and psychological substrate of utility. In this sense, the
affect heuristic enables us to be rational actors in many important
situations. But not in all situations. It works beautifully when our
experience enables us to anticipate accurately how we will like the
consequences of our decisions. It fails miserably when the consequences
turn out to be much different in character than we anticipated.

The scientific study of affective rationality is in its infancy. It is
exciting to contemplate what might be accomplished by future research
designed to help humans understand the affect heuristic and employ it
beneficially in risk analysis, risk communication, and other worthy
endeavors.

References

Ainslie, G., & Haslam, N. (1992). Self-control. In G. F. Loewenstein &
J. Elster (Eds.), *Choice over time* (pp. 177-209). New York: Russell
Sage Foundation.

Alhakami, A. S., & Slovic, P. (1994). A psychological study of the
inverse relationship between perceived risk and perceived benefit. *Risk
Analysis, 14*(6), 1085-1096.

Barrett, L. F., & Salovey, P. (Eds.) (2002). *The wisdom in feeling.*
New York: Guilford.

Baron, J. (1997). Confusion of relative and absolute risk in valuation.
*Journal of Risk and Uncertainty, 14*(3), 301-309.

Bornstein, R. F. (1989). Exposure and affect: Overview and meta-analysis
of research, 1968-1987. *Psychological Bulletin, 106*, 265-289.

Chaiken, S., & Trope, Y. (1999). *Dual-process theories in social
psychology.* New York: Guilford.

Clark, M. S., & Fiske, S. T. (Eds.) (1982) *Affect and cognition.*
Hillsdale, NJ: Erlbaum.

Damasio, A. R. (1994). *Descartes' error: Emotion, reason, and the human
brain.* New York: Avon.

Denes-Raj, V., & Epstein, S. (1994). Conflict between intuitive and
rational processing: When people behave against their better judgment.
*Journal of Personality and Social Psychology, 66,* 819-829.

Epstein, S. (1994). Integration of the cognitive and the psychodynamic
unconscious. *American Psychologist, 49,* 709-724.

Fetherstonhaugh, D., Slovic, P., Johnson, S. M., & Friedrich, J. (1997).
Insensitivity to the value of human life: A study of psychophysical
numbing. *Journal of Risk and Uncertainty, 14*(3), 282-300.

Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The
affect heuristic in judgments of risks and benefits. *Journal of
Behavioral Decision Making, 13,* 1-17.

Finucane, M. L., Peters, E., & Slovic, P. (in press). Judgment and
decision making: The dance of affect and reason. In S. L. Schneider & J.
Shanteau (Eds.), *Emerging perspectives on judgment and decision
research.* New York: Cambridge University Press.

Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S., & Combs, B.
(1978). How safe is safe enough? A psychometric study of attitudes
toward technological risks and benefits. *Policy Sciences, 9,* 127-152.

Forgas, J. P. (Ed.) (2000). *Feeling and thinking: The role of affect in
social cognition.* Cambridge, U. K.: Cambridge University Press.

Friedrich, J., Barnes, P., Chapin, K., Dawson, I., Garst, V., & Kerr, D.
(1999). Psychophysical numbing: When lives are valued less as the lives
at risk increase. *Journal of Consumer Psychology, 8,* 277-299.

Ganzach, Y. (2001). Judging risk and return of financial assets.
*Organizational Behavior and Human Decision Processes, 83,* 353-370.

Gasper, K., & Clore, G. L. (1998). The persistent use of negative affect
by anxious individuals to estimate risk. *Journal of Personality &
Social Psychology, 74*(5), 1350-1363.

Goldstein, W. M., & Einhorn, H. J. (1987). Expression theory and the
preference reversal phenomena. *Psychological Review, 94,* 236-254.

Hendrickx, L., Vlek, C., & Oppewal, H. (1989). Relative importance of
scenario information and frequency information in the judgment of risk.
*Acta Psychologica, 72,* 41-63.

Isen, A. M. (1993). Positive affect and decision making. In M. Lewis &
J. M. Haviland (Eds.), *Handbook of emotions* (pp. 261-277). New York:
The Guilford Press.

Janis, I. L., & Mann, L. (1977). *Decision making.* New York: The Free
Press.

Jenni, K. E., & Loewenstein, G. (1997). Explaining the "identifiable
victim effect." *Journal of Risk and Uncertainty, 14*(3), 235-258.

Johnson, E. J., & Tversky, A. (1983). Affect, generalization, and the
perception of risk. *Journal of Personality and Social Psychology, 45,*
20-31.

Kahneman, D. (1994). New challenges to the rationality assumption.
*Journal of Institutional and Theoretical Economics, 150,* 18-36.

Kahneman, D., & Frederick, S. (2002). Representativeness revisited:
Attribute substitution in intuitive judgment. In T. Gilovich & D.
Griffin & D. Kahneman (Eds.), *Heuristics and biases: The psychology of
intuitive judgment* (pp. 49-81). New York: Cambridge University Press.

Kahneman, D., Schkade, D., & Sunstein, C. R. (1998). Shared outrage and
erratic awards: The psychology of punitive damages. *Journal of Risk and
Uncertainty, 16,* 49-86.

Kahneman, D., Slovic, P., & Tversky, A. (Eds.) (1982). *Judgment under
uncertainty: Heuristics and biases.* New York: Cambridge University
Press.

Kahneman, D., & Snell, J. (1990). Predicting utility. In R. M. Hogarth
(Ed.), *Insights in decision making* (pp. 295-310): University of
Chicago.

Kahneman, D., & Snell, J. (1992). Predicting a changing taste. *Journal
of Behavioral Decision Making, 5,* 187-200.

Knetsch, J. (1989). The endowment effect and evidence of nonreversible
indifference curves. *The American Economic Review, 79*, 1277-1284.

Le Doux, J. (1996). *The emotional brain.* New York: Simon & Schuster.

Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M., & Combs, B.
(1978). Judged frequency of lethal events. *Journal of Experimental
Psychology: Human Learning and Memory, 4,* 551-578.

Loewenstein, G. F. (1996). Out of control: Visceral influences on
behavior. *Organizational Behavior and Human Decision Processes, 65,*
272-292.

Loewenstein, G. F. (1999). A visceral account of addiction. In J. Elster
& O-J. Skog (Eds.), *Getting hooked: Rationality and addiction* (pp.
235-264). London: Cambridge University Press.

Loewenstein, G. F., & Elster, J. (Eds.). (1992). *Choice over time*. New
York: Russell Sage Foundation.

Loewenstein, G. F., & Schkade, D. (1999). Wouldn't it be nice?
Predicting future feelings. In E. Diener & N. Schwartz & D. Kahneman
(Eds.), *Well-being: The foundations of hedonic psychology* (pp.
85-105). New York: Russell Sage Foundation.

Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, E. S. (2001).
Risk as feelings. *Psychological Bulletin, 127*(2), 267-286.

Mellers, B. A. (2000). Choice and the relative pleasure of consequences.
*Psychological Bulletin, 126*(6), 910-924.

Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision
affect theory: Emotional reactions to the outcomes of risky options.
*Psychological Science, 8,* 423-429.

Mowrer, O. H. (1960). *Learning theory and behavior.* New York: John
Wiley & Sons.

Ordóñez, L., & Benson, L., III. (1997). Decisions under time pressure:
How time constraint affects risky decision making. *Organizational
Behavior and Human Decision Processes, 71*(2), 121-140.

Pennington, N., & Hastie, R. (1993). A theory of explanation-based
decision making. In G. Klein & J. Orasano & R. Calderwood & C. E.
Zsambok (Eds.), *Decision making in action: Models and methods* (pp.
188-204). Norwood, NJ: Ablex.

Peters, E., & Slovic, P. (2000). The springs of action: Affective and
analytical information processing in choice. *Personality and Social
Psychology Bulletin, 26,* 1465-1475.

Rottenstreich, Y., & Hsee, C. K. (2001). Money, kisses and electric
shocks: On the affective psychology of probability weighting.
*Psychological Science, 12*(3), 185-190.

Rozin, P., Haidt, J., & McCauley, C. R. (1993). Disgust. In M. Lewis &
J. M. Haviland (Eds.), *Handbook of emotions* (pp. 575-594). New York:
Guilford.

Sandman, P. (1989). Hazard versus outrage in the public perception of
risk. In V. T. Covello & D. B. McCallum & M. T. Pavlova (Eds.),
*Effective risk communication: The role and responsibility of government
and nongovernment organizations* (pp. 45-49). New York: Plenum Press.

Sanfey, A., & Hastie, R. (1998). Does evidence presentation format
affect judgment? An experimental evaluation of displays of data for
judgments. *Psychological Science, 9*(2), 99-103.

Schwarz, N., & Clore, G. L. (1988). How do I feel about it? Informative
functions of affective states. In K. Fiedler & J. Forgas (Eds.),
*Affect, cognition, and social behavior* (pp. 44-62). Toronto: Hogrefe
International.

Sloman, S. A. (1996). The empirical case for two systems of reasoning.
*Psychological Bulletin, 119*(1), 3-22.

Slovic, P. (1987). Perception of risk. *Science, 236,* 280-285.

Slovic, P. (2001). Cigarette smokers: Rational actors or rational fools?
In P. Slovic (Ed.), *Smoking: Risk, perception, and policy* (pp.
97-124). Thousand Oaks, CA: Sage.

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2002). The
affect heuristic. In T. Gilovich & D. Griffin & D. Kahneman (Eds.),
*Heuristics and biases: The psychology of intuitive judgment* (pp.
397-420). New York: Cambridge University Press.

Slovic, P., & Lichtenstein, S. (1968). Relative importance of
probabilities and payoffs in risk taking. *Journal of Experimental
Psychology Monograph, 78*(3, Pt. 2), 1-18.

Slovic, P., MacGregor, D. G., Malmfors, T., & Purchase, I. F. H. (n.d.).
*Influence of affective processes on toxicologists' judgments of risk.*
Eugene, OR: Decision Research.

Slovic, P., Monahan, J., & MacGregor, D. M. (2000). Violence risk
assessment and risk communication: The effects of using actual cases,
providing instructions, and employing probability vs. frequency formats.
*Law and Human Behavior, 24*(3), 271-296.

Tomkins, S. S. (1962). *Affect, imagery, and consciousness: Vol. 1. The
positive affects.* New York: Springer.

Tomkins, S. S. (1963). *Affect, imagery, and consciousness: Vol. 2. The
negative affects.* New York: Springer.

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for
judging frequency and probability. *Cognitive Psychology, 5,* 207-232.

Viscusi, W. K. (1992). *Smoking: Making the risky decision.* New York:
Oxford University Press.

Wilson, T. D., Lisle, D. J., Schooler, J. W., Hodges, S. D., Klaaren, K.
J., & LaFleur, S. J. (1993). Introspecting about reasons can reduce
post-choice satisfaction. *Personality and Social Psychology Bulletin,
19*(3), 331-339.

Winkielman, P., Zajonc, R. B., & Schwarz, N. (1997). Subliminal
affective priming resists attributional interventions. *Cognition and
Emotion, 11*(4), 433-465.

Yamagishi, K. (1997). When a 12.86% mortality is more dangerous than
24.14%: Implications for risk communication. *Applied Cognitive
Psychology, 11,* 495-506.

Zajonc, R. B. (1980). Feeling and thinking: Preferences need no
inferences. *American Psychologist, 35,* 151-175.

+-------------------------------------+--------------------------------+
| ##### Table I                       |                                |
|                                     |                                |
| ##### *Two M                        |                                |
| odes of Thinking: Comparison of the |                                |
|  Experiential and Analytic Systems* |                                |
+-------------------------------------+--------------------------------+
| ###### **Experiential system**      | ##### **Analytic system**      |
+-------------------------------------+--------------------------------+
| 1\. Holistic                        | > 1\. Analytic                 |
+-------------------------------------+--------------------------------+
| 2\. Affective: Pleasure-pain        | > 2\. Logical: Reason oriented |
| oriented                            | > (what is sensible)           |
+-------------------------------------+--------------------------------+
| 3\. Associationistic connections    | > 3\. Logical connections      |
+-------------------------------------+--------------------------------+
| 4\. Behavior mediated by "vibes"    | > 4\. Behavior mediated by     |
| from past experiences               | > conscious appraisal of       |
|                                     | > events                       |
+-------------------------------------+--------------------------------+
| 5\. Encodes reality in concrete     | > 5\. Encodes reality in       |
| images, metaphors, and narratives   | > abstract symbols, words, and |
|                                     | > numbers                      |
+-------------------------------------+--------------------------------+
| 6\. More rapid processing: Oriented | > 6\. Slower processing:       |
| toward immediate action             | > Oriented toward delayed      |
|                                     | > action                       |
+-------------------------------------+--------------------------------+
| 7\. Self-evidently valid:           | > 7\. Requires justification   |
| "experiencing is believing"         | > via logic and evidence       |
+-------------------------------------+--------------------------------+

![](media/image1.jpeg){width="4.909027777777778in"
height="7.636111111111111in"}**Figure 1.** Street calculus.

![](media/image2.wmf)

![](media/image3.wmf)

![](media/image4.wmf)