Topics in the
Psychology of Risk
Thomas S. Wallsten
Department of Psychology
University of Maryland
SAMSI 2007-08 Program on Risk Analysis,
Extreme Events, and Decision Theory
Outline
• Distinguish “risk” in psychology from other
domains
• Within psychology, distinguish risk
– Perception
– Communication
─ Attitude
─ Learning
• Early approach: EV and a 1-dimension
scale of riskiness
• A lesson from animal behavior
– Coefficient of variation as an index of risk
• Revealed risk-benefit trade-offs
• Psychometric approaches
Outline (Cont’d)
• Cognitive and affective components of risk
judgment
– Lessons from cognitive neuroscience
• The role of learning in judging risk
• Risk communication
• Concluding comments
• (Will not cover individual-differences or contextspecific aspects of risk)
The Construct of Risk Outside Psychology
• Within rational choice (EU) theory
– The utility function captures risk attitude
• Concave, linear, convex utility functions represent
risk aversion, neutrality, attraction, respectively
• E.g., Risk aversion ≡ prefer a sure $s to a lottery
($x, p, 0) with EV = $s
• Other constructs within economics,
engineering, finance, risk analysis
– Risk versus uncertainty (Knight, 1921)
• Risk ≡ known (aleatory) probability
• Uncertainty ≡ imprecise (epistemic) probability
The Construct of Risk Outside Psychology
• Within economics, engineering, finance,
risk analysis (cont’d)
– Probability of loss, undesirable outcome
– Variability or volatility of outcomes
– Index that combines probability and
magnitude of loss
• Most recently, DHS color-coded Security Advisory
System
– The higher the Threat Condition, the greater the risk of a
terrorist attack. Risk includes both the probability of an
attack occurring and its potential gravity.
The Construct of Risk Within Psychology
• Distinguish risk
– Perception
– Communication
─ Preference (attitude)
─ Learning with experience
• Risk perception and preference
–
–
–
–
Vary across individuals, situations, cultures
Are multidimensional
Have emotional and cognitive components
Change with experience and learning
• Risk communication
– Occurs in many formats
– Serves purposes beyond communicating risk
• We will sample within each of these areas
An Early Approach
• Due to Clyde Coombs
• Portfolio theory
– “… risky options involving monetary outcomes
with explicit probabilities are evaluated in
terms of their expected value and their
riskiness and, in particular, that an individual
has a single-peaked preference function over
risk if expected value is held constant.”
(Coombs, Donnell, & Kirk, 1978; p. 497)
– Risk is undefined, but increases with
probability and with amount to lose
An Early Approach
• Portfolio theory
– Riskiness is undefined
• Assumed to vary, at least, with probability and magnitude of
loss
– Distinguishes
• Perception – monotonic function of risk
• Preference – single-peaked over risk
– Tested
• Ss chose or rejected multiple-outcome lotteries among equal
EV sets at various EV levels
– Supported to a first approximation
• But systematic patterns of violations
– Not easily generalized to non-numerical contexts
A Lesson from Animal Behavior
• Energy budget rule for bird, insect foraging
– Risk-prone if short-term prospect of starving
– Risk-averse otherwise
• Shafir (2000) suggests coefficient of
variation as the index of risk
– CV = SD/EV
– Fits with Weber’s Law in psychophysics
• Magnitude of a just-noticeable-difference is
proportional to the size of the stimulus
A Lesson from Animal Behavior
Figure 1 from Shafir (2000) summarizing a
meta-analysis of studies for which budget rule
predicts risk-aversion (circles) or riskproneness (squares)
Figure 4 from Shafir (2000) showing risksensitivity of bees and wasps to variability in
nectar volume as a function of CV, SD, Var
Coefficient of Variation Applied to
Human Risk Preferences
• Weber, Shafir, & Blais (2004) metaanalysis of 20 studies with humans
– CV significantly predicted risky choice
• For both gain and loss situations
• CV predicted less well in financial than in other
domains; variance predicted better
• But other factors also were significant; e.g.,
• Real versus hypothetical outcomes
• Nationality
– Difference from non-human data may be in
how uncertainty was represented and learned
Learning via Experience versus Description
• All studies in meta-analysis represented alternatives
numerically or symbolically
• Compared choices under both learning methods
– Hertwig, Barron, Weber, & Erev (2004), Weber et al.
(2004)
– Data supported the idea that CV predicted choices better
given learning via experience than description
– Authors suggest that associative (rather than rule-based)
learning may be operating more strongly in that case
– Suggest that deviations of human choice behavior from
prescriptive models in finance and economics may be due
to people responding to a different risk index than
assumed by EU-type models. (Weber et al., 2004, p. 443)
Revealed Risk-Benefit Tradeoff
• Use historical and population data to estimate risk-benefit trade-offs
people make
– Starr (1969) did this for voluntary and involuntary activities
– Note in both cases R ≈ B3
• Risk measure –
fatalities/personhour of exposure
•
Benefit measure
– dollars devoted to
activity (voluntary)
or contribution of
activity to person’s
income (involuntary)
Revealed Risk-Benefits Tradeoff
• Population participation increases as risk
decreases over time (Starr, 1969)
Psychometric Approach
• Pioneered by Slovic, Fischhoff, Lichtenstein
• Developed an alternative to Starr’s revealed
preferences method
•
•
•
•
Fischhoff, Slovic, Lichtenstein, Read, & Combs (1978)
Slovic, Fischhoff, & Lichtenstein (1980)
Slovic, Fischhoff, & Lichtenstein (1985)
Slovic (1987)
Psychometric Approach
• Used questionnaire approach
– Asked about 30 or 90 hazards
• E.g., Home gas furnaces, nuclear weapons,
pesticides, aspirin, hunting, jogging, smoking, …
• Included the 8 Starr used
– Rated
•
•
•
•
Total benefit to society
Risk of dying
Acceptability of current risk level
9 or 13 characteristics of risk
– E.g., voluntariness, immediacy, known to exposed
person, known to science, severity of consequences, …
Psychometric Approach
• Different risk-benefit
relationship than Starr
(1969) found
– Different underlying
assumptions
– Different metrics
– Different types of data
Fischhoff et al., 1978
Psychometric Approach
• From Slovic et al. (1980)
• Factor analyze the rating
scales
– Factor 1: Dread,
controllability
– Factor 2: Known,
observable
– Factor 3 (not plotted):
Degree of exposure
• Desire for regulation
increases from bottom left
to upper right (Slovic,
1987)
Cognitive and Affective
Components of Risk Judgments
• Risk judgments, choices, have affective as
well as cognitive determinants
– This is consistent with the preceding results
– Loewenstein, Weber, Hsee, & Welch (2001)
• Contrast purely cognitive to cognitive plus affective
Cognitive and Affective
Components of Risk Judgments
• Neurological data suggests two systems
– Damasio (e.g., 1994) summarizes observations and
data:
• Prefrontal lobe lesions impair decision making by blocking
access to somatic reactions
• Little cognitive deficit
• Inability to associate choices with adverse consequences
• “Somatic marker hypothesis”
– Imaging studies show differential activity in frontal
lobes (“executive”) and amygdala (emotion, affect)
• Others (e.g., Sloman, 1996) suggest
– Cognitive component is rule-based
– Affective component is associative based
Learning in Sequential Risky Choice
• Respondents work at a computer-based
sequential risky choice task
– Wallsten, Pleskac, & Lejuez (2005); Pleskac (2007)
– Pump up a balloon to earn money
– Keep money if they stop before balloon explodes;
otherwise lose it
– They learn about the stochastic environment with
experience
– We successfully model the dynamic learning and
sequential choice behavior at level of individual
respondents
Learning in Sequential in Risky Choice
• Cognitive model has three components
– Probability learning
• DM has a mental model of environment; updates
probability estimates in optimal Bayesian fashion
– Prospect-theory valuation module
– Stochastic choice mechanism based on
valuation
• Successfully model details of the behavior
• Beginning research to manipulate and
model affective components
Communicating Risk
• A vast literature on this topic
– Numerical vs. verbal vs. graphical vs. color
– Relative vs. absolute risk
– Specialized for different domains – e.g.
• Medical, environmental, security
• Briefly discuss verbal communication
– People tend to prefer communicating risk to
others verbally and receiving it numerically
• Numerous reasons for this preference pattern
• Wallsten, Budescu, Zwick, & Kemp (1993)
Communicating Risk Verbally
range of
individual
upper bound
estimates
range from upper
to lower median
estimate
From Morgan (2004) adapted
from Wallsten et al. (1986)
Almost certain
Qualitative description of uncertainty used
range of
individual
lower bound
estimates
Probable
Likely
Good chance
Possible
Tossup
Unlikely
Improbable
Doubtful
Almost impossible
1.0
0.8
0.6
0.4
0.2
Probability that subjects associated
with the qualitative description
0.0
Communicating Risk Verbally
• A new scaling method (Dhami & Wallsten
(2005)
– Represent phrase meanings in terms of 2ndorder probability distributions (probability
signatures)
• Resulting representations
– Are directly comparable across individuals
– Have a natural error theory
– Provide, via a model, for predicting binary
choices
Probability Signatures
• Method
– Judges select and rank their lexicons
– Associate phrases with probabilities, p
• Estimating the signatures
– Fit normal distributions to ln[p/(1-p)]
– Equal-variance normal dist’s provide excellent
fits
– Equivalent across judges at equal ranks
Return
Dhami & Wallsten (2005)
Probability Signatures
• Probability signatures lend themselves to
stochastic modeling
– That assures a natural error theory when
assessing validity
– Successfully predict pair-comparison choices
• They also provide a metric for interpersonal
comparisons of meaning
– A consequence of two related conditions
• Common unit for all judges
• Scaling is unique (no permissible transformation)
proportion of correct choices
observed proportion
1
0.8
7 for whom
model failed
0.6
0.4
0.2
0
0
Wallsten & Jang (2007)
0.2
0.4
0.6
theoretical proportion
0.8
1
Concluding Comments
• Many aspects to the psychology of risk
• Various methods needed to study risk
judgments
– Choice, valuation, questionnaire
• Determinants of risk
– CV is a useful index when probabilities and
outcomes are learned purely via experience
– In other cases, there seems to be no simple
risk index
• Risk judgments are multidimensional
• Include affective as well as cognitive components
Concluding Comments
• The presence of affective components
presents a challenge for modeling risk
• Effective risk communication is
increasingly important and should be
informed by our understanding of the
psychology of risk
References
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Coombs, Donnell, & Kirk (1978)
Dhami & Wallsten (2005)
Fischhoff, Slovic, Lichtenstein, Read, & Combs (1978)
Hertwig, Barron, Weber, & Erev (2004)
Knight (1921)
Loewenstein, Weber, Hsee, & Welch (2001)
Pleskac (2007)
Shafir, S. (2000)
Slovic (1987)
Slovic, Fischhoff, & Lichtenstein (1980)
Slovic, Fischhoff, & Lichtenstein (1985)
Wallsten, Budescu, Zwick, Rapoport, & Forsyth (1986)
Wallsten, Budescu, Zwick, & Kemp (1993)
Weber, Shafir, & Blais (2004)