Journal of Behavioral Decision Making, J. Behav. Dec. Making (2012)
Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/bdm.1757
Strategy Selection in Risky Choice: The Impact of Numeracy, Affect, and
Cross-Cultural Differences
THORSTEN PACHUR1* and MIRTA GALESIC2
1
Department of Psychology, University of Basel, Switzerland
2
Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Berlin, Germany
ABSTRACT
Real-world decisions often involve options with outcomes that are uncertain and trigger strong affect (e.g., side effects of a drug). Previous
work suggests that when choosing among affect-rich risky prospects, people are rather insensitive to probability information, potentially
compromising decision quality. We modeled the strategies of less and more numerate participants in the United States and in Germany when
choosing between affect-rich prospects and between monetarily equivalent affect-poor prospects. Using large probabilistic national samples
(n = 1047 from the United States and Germany), Study 1 showed that compared with more numerate participants, less numerate participants
chose the normatively better option (i.e., the one with the higher expected value) less often, guessed more often, and relied more on a simple
risk-minimizing strategy. U.S. participants—although less numerate—selected the normatively better option more frequently and were more
consistent across affect-rich and affect-poor problems than the German participants. Using a targeted quota sample (n = 118 from Germany),
Study 2 indicated that although both more and less numerate participants paid less attention to probability information in affect-rich than in
affect-poor problems, the two numeracy groups relied on different outcome-based heuristics: More numerate participants often followed
the minimax heuristic, and less numerate participants the affect heuristic. The observed strategy differences suggest that attempts to improve
decision-making need to take into account individual differences in numeracy as well as cultural-specific experiences in making trade-offs.
Copyright © 2012 John Wiley & Sons, Ltd.
key words
risky choice; numeracy; affect; cultural comparison; heuristics
In early spring 2009, doctors in the state of Veracruz, Mexico,
described the first human case of a new strain of H1N1 influenza virus. By August 2010, when World Health Organization
director Margaret Chan declared the end of the “swine flu”
pandemic, the virus had infected 600 000 people and killed
18 000 in 208 countries. As H1N1 exploded into a pandemic,
many governments invested large amounts of money in
vaccines (Jack, 2010) and initiated campaigns that encouraged
the public to get their shot. Soon, however, media reports
about side effects and even deaths associated with H1N1
vaccinations dominated the headlines (Langer, 2009),
confronting people with a complex trade-off between the risks
and benefits of getting vaccinated.
How do people make such decisions? Rottenstreich and
Hsee (2001) highlighted that compared with the monetary
lotteries usually used in risky choice studies, options involving
the risk of experiencing an aversive outcome (e.g., side effects)
often trigger stronger anticipatory affect and that such affectrich prospects might trigger different decision behavior than
affect-poor prospects. Specifically, the authors showed that
in decisions concerning affect-rich options, people seem to
treat probability information differently than in decisions
concerning relatively affect-poor options (see also Sunstein,
2002). Similarly, research on risk perception has shown
that that people’s reactions to dreadful risks are often rather
insensitive to probability information (Slovic, 1987). Reduced
sensitivity to probabilities can lead to poor choices: For
instance, people may be unwilling to accept a beneficial
*Correspondence to: Thorsten Pachur, Department of Psychology, University of
Basel, Missionsstrasse 60/62, 4055 Basel, Switzerland. E-mail: thorsten.
[email protected]
Copyright © 2012 John Wiley & Sons, Ltd.
treatment (e.g., effective medication) that can lead to a highly
adverse side effect, even when the probability of the side effect
is very small (Waters, Weinstein, Colditz, & Emmons, 2009).
Improving people’s decisions among affective prospects
requires understanding the strategies underlying affect-rich
and affect-poor choice (e.g., Payne & Venkatraman, 2011).
The strategies used can depend on individual and also
cultural factors.
Our primary goal in this article was to investigate the
influence of an individual factor, numeracy—defined as an
individual’s ability to understand and use numerical information—on strategy selection in affect-poor and affect-rich
risky choices. In light of evidence for cross-cultural differences
in decision-making (Chu & Spires, 2008; Leng & Botelho,
2010), a secondary goal was to compare choice and strategy
selection of participants from the United States and Germany,
two countries differing considerably in their educational
and health care systems (Rindermann, 2007; World Health
Organization, 2008). In two studies, we examined the choices
and choice strategies of more and less numerate people in
a (hypothetical) medical decision-making task (choices
between medications) and compared them with choices
between monetary lotteries. Study 1 investigated the use of
an expected value strategy and various heuristics and was
based on large probabilistic national samples from the United
States and Germany. In Study 2, we additionally considered
the use of a strategy that recruits affect to make a choice.
Moreover, we examined whether a person’s choice consistency—that is, the degree to which she makes the same choice
among affect-rich as among monetarily equivalent affect-poor
options—is related to her ability to reliably map subjective
utilities of affect-rich outcomes onto a monetary scale.
Journal of Behavioral Decision Making
RISKY CHOICE AND AFFECT
There is growing evidence that the anticipatory affective
reactions triggered by the outcomes of risky options are an
important determinant of how people respond to them
(Loewenstein, Weber, Hsee, & Welch, 2001; Slovic, 1987).
In a seminal study, Rottenstreich and Hsee (2001) asked
participants to indicate how much they would be willing to
pay to avoid either a 1% or a 99% chance of experiencing
an unpleasant outcome. The outcome was either relatively
affect-rich (e.g., an electric shock) or affect-poor (e.g., a $20
fine). For the affect-poor options, participants indicated a
considerably higher amount for the 99% chance than for
the 1% chance ($18 vs. $1), suggesting a high sensitivity to
probability information. For the affect-rich alternatives, by
contrast, the indicated amounts were rather similar for the
99% and 1% chances ($10 vs. $7). From these results, Sunstein
(2002) concluded that people’s sensitivity to probability
information is reduced when the outcomes are affect-rich
compared with when they are affect-poor—and referred to this
phenomenon as probability neglect (for a critical discussion,
see McGraw, Shafir, & Todorov, 2010). Because probability
neglect is more likely to occur in affect-rich than in affect-poor
tasks, people’s choices might often diverge between the two,
leading to preference reversals.
What are the strategies giving rise to probability neglect?
Although several studies have examined decision-making
between affect-rich choices, there is practically no research
that has compared the strategies used when people decide
between affect-rich or affect-poor options. One strategy that
implements an extreme form of probability neglect and can
be an adaptive strategy in the loss domain is the minimax heuristic (Savage, 1951). According to this heuristic, probability
information is ignored completely and a choice is based
exclusively on information about the options’ worst possible
outcome (for a more detailed description, see below). In a
model comparison, Suter, Pachur, and Hertwig (2012) found
the heuristic to be superior to prospect theory in describing
people’s affect-rich choices.
NUMERACY AND DECISION-MAKING
The strategies people use in risky choice may depend not only
on aspects of the task (e.g., such as the amount of affect triggered) but also on the decision maker’s ability to deal with
numbers. This ability is captured in the “numeracy” construct,
which encompasses knowing how to perform elementary calculations with percentages as well as an understanding of stochastic processes (e.g., the concept of a random coin toss; Lipkus,
Samsa, & Rimer, 2001; Schwartz, Woloshin, Black, & Welch,
1997). Several studies have shown strong variability in numeracy in the population (Lipkus et al., 2001; Schwartz et al., 1997).
How could numeracy impact risky decision-making (e.g.,
Reyna, Nelson, Han, & Dieckmann, 2009)? First, less numerate
people may be less likely to integrate multiple pieces of numeric information describing a risk. For instance, when evaluating probability information from a ratio (e.g., 7/10, 33/100),
people low in numeracy sometimes base their judgments solely
Copyright © 2012 John Wiley & Sons, Ltd.
on the numerator, whereas people high in numeracy tend to take
both the numerator and the denominator into account (GarciaRetamero & Galesic, 2009; Peters et al., 2006). Similarly, one
might expect that in risky choice less numerate people are less
likely to integrate probabilities and outcome information than
more numerate people and thus choose the option with the
higher expected value less often. In addition, given that less numerate people have, by definition, difficulty interpreting probability information correctly, they might focus more on outcome-based strategies, which ignore probabilities.
A second possible way in which people high and low in
numeracy might differ is in terms of how their decisions are
influenced by irrelevant contextual information (Dieckmann,
Slovic, & Peters, 2009). For instance, in a study involving
risky options framed as either gains or losses, Peters and Levin
(2008) found that people low in numeracy were more
influenced by the different framings than people high in
numeracy. In the context of our studies, one might expect that
less numerate participants are more influenced by the affective
content of a decision problem than more numerate participants
and that consequently less numerate participants would show
fewer preference reversals between affect-rich and affect-poor
problems. Third, there is some evidence that people with
low numeracy scores are less likely to use numeric information
to make a decision and instead recruit nonnumerical information, such as the strength of the affective reaction to an option
(e.g., Peters, 2008; Peters et al., 2006, 2009).
RESEARCH QUESTIONS AND HYPOTHESES
We report two studies in which we presented to participants
affect-rich and equivalent affect-poor risky choice problems
(in a within-subjects design) that could lead to losses. In both
studies, we examined the differences in strategies underlying
the choices of more and less numerate participants. In addition,
in Study 1 we compare the choices of U.S. and German participants to examine the degree to which strategy use may vary
between cultures. In contrast to previous investigations on
the role of affect in risky choice, which focused on highly educated and therefore probably highly numerate samples (i.e.,
students; Rottenstreich & Hsee, 2001; Suter et al., 2012), our
samples covered a broad range of numeracy levels. On the basis of previous studies, we expected that preferences would
systematically reverse between affect-rich and affect-poor problems. Specifically, the option with the higher expected value
should be less frequently chosen in affect-rich problems, where
people should instead choose the option with the more attractive worst outcome (thus displaying probability neglect).
Given that less numerate people have been found to be influenced by context information to a greater degree than more numerate people, we expected that the former would show a
greater proportion of preference reversals.
Whereas several studies have merely speculated about
the impact of numeracy on strategy selection (e.g., Peters &
Levin, 2008; Peters et al., 2006, 2009), here we model people’s
choices by using several precisely formalized strategies (for
an alternative approach, see Glöckner & Pachur, 2012, and
Pachur, Hanoch, & Gummerum, 2010). Specifically, we
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
T. Pachur and M. Galesic
considered a compensatory strategy (expected value strategy)
that is often used as a gold standard to evaluate the quality
of a decision (e.g., Keeney & Raiffa, 1976) as well as three
heuristics that have been proposed as possible ways to simplify
a risky choice: (for an investigation of further heuristics, see
Brandstätter, Gigerenzer, & Hertwig, 2006) a probabilitybased heuristic (least-likely) and two outcome-based heuristics
(minimax and the affect heuristic).
According to the expected value (EV) strategy, people
aggregate the outcomes of each option, weighted by their respective probabilities, and choose the option with the most attractive expected value. For instance, when faced with a choice
between option A, leading to a loss of $8 with a probability of
70% (nothing otherwise), and option B, leading to a loss of $16
with a probability of 25% (nothing otherwise), EV would
choose option A. EV is a compensatory strategy because a very
unattractive outcome can be compensated for by a low probability. For our implementation of EV, we used the outcomes’
monetary value as a proxy for how people evaluate them.
According to the least-likely heuristic (Thorngate, 1980), people identify each option’s worst outcome and choose the option
with the lowest probability of yielding the worst outcome. In
the above example, least-likely would choose option B because it has the lower probability of yielding the worst outcome, $16 (25% vs. 70%). Although least-likely inspects both
outcome and probability information, it is a noncompensatory
strategy because outcome and probability cannot compensate
for each other. For instance, a very unattractive outcome
cannot be compensated by a low probability. According to
the minimax heuristic (Savage, 1951), people only consider
the worst outcome of each option and choose the option with
the most attractive worst outcome. In the preceding example,
minimax would choose option A, because its worst outcome
(loss of $8) is less bad than option B’s (loss of $16). Because
minimax ignores all other outcomes and all probability information, it is a noncompensatory strategy. As for EV, we
used the outcomes’ monetary value as a proxy for how they
are evaluated. The final strategy we tested was also an outcome-based heuristic. Like minimax, it considers only information about the worst possible outcome of an option and
chooses the option with the most attractive worst outcome.
Unlike EV and minimax, however, this heuristic assumes
that a choice is based on the affective evaluation of the
outcomes (rather than their monetary value). This strategy
therefore represents a possible implementation of the affect
heuristic (Finucane, Alhakami, Slovic, & Johnson, 2000; in
the General Discussion, we discuss an extended implementation of the affect heuristic). In the aforementioned example,
if a loss of $8 triggers less negative affect than a loss of $16,
the affect heuristic would choose option A. We tested EV,
least-likely, and minimax in Studies 1 and 2, and the affect
heuristic in Study 2.
Concerning our primary goal—to investigate the role of
numeracy in strategy selection—we hypothesized that less
numerate participants would be classified as following the
compensatory EV strategy less frequently than more numerate
participants. We expected that less numerate participants
would instead be more frequently classified as following a
noncompensatory strategy. However, we also expected that
Copyright © 2012 John Wiley & Sons, Ltd.
Numeracy, Affect, and Risky Choice
more and less numerate participants would use different noncompensatory strategies. Given that people with lower
numeracy scores have difficulty in understanding probability
information, one might expect that they will be less frequently
classified as following least-likely than people with higher
numeracy scores. Moreover, given the evidence that less
numerate people often recruit nonnumerical information to
make a decision, we expected that they would be more
frequently classified as following the affect heuristic than
more numerate people.
Regarding our secondary goal—to examine possible
differences between the United States and Germany—one
might expect that given that quantitative literacy, and thus
numeracy, has been found to be higher in Germany (Kutner,
Greenberg, Jin, & Paulsen, 2006; Programme for International
Student Assessment, 2003), the use of compensatory strategies
(and thus the choice of the normative option) and also consistency across affect-poor and affect-rich choices might be
more pronounced in the German sample.
STUDY 1
The aim of Study 1 was to examine the risky choices of
more and less numerate participants in the United States and
Germany in affect-poor and in affect-rich problems and to
model their choices with the EV strategy, the least-likely
heuristic, and the minimax heuristic.
Method
Participants
The study was conducted with probabilistic national samples
in Germany and the United States, as a part of a larger project
investigating health-related decision-making. The larger
project involved two waves. In the first wave (for a detailed
description of the sampling procedure, see Galesic &
Garcia-Retamero, 2010), participants in Germany (n = 1001)
and in the United States (n = 1009) completed a numeracy
scale consisting of nine items (for details, see Galesic &
Garcia-Retamero, 2010) developed by Schwartz et al.
(1997) and by Lipkus et al. (2001), as well as other questions
concerning risk comprehension not reported here. Overall,
the German participants had slightly higher numeracy scores
than the U.S. participants, with 6.2 and 5.9 items answered
correctly, respectively, t(2008) = 2.35, p = .02, d = .11. The
median score was 6 in both countries. In the second wave,
which included the study described here, approximately
the top and bottom third of the participants in the first wave,
ordered by numeracy scores, were invited to participate.
The response rate was 71%, resulting in a sample of n = 534
participants from Germany and n = 513 participants from
the United States (for a total of n = 1047; Table 1). Having
sampled equally from the top third and the bottom third of
the numeracy distribution, we were able to compare
more and less numerate people within each country, as well
as between countries.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
Journal of Behavioral Decision Making
Table 1. Sample structure in Study 1, by country, numeracy, sex, and age
Germany
Less numerate
(M = 3.39, SD = 1.32)
Total
Sex
Male
Female
Age (years)
2539
4054
5569
United States
More numerate
(M = 8.58, SD = .52)
Less numerate
(M = 3.12, SD = 1.47)
More numerate
(M = 7.82, SD = .97)
257
277
237
276
108
149
177
100
89
148
146
130
44
97
116
98
107
72
56
86
95
61
119
96
Note: The gender distribution within each age group was similar to the overall gender distribution for each numeracy group in both countries.
Materials
We used a total of seven side effects (fatigue, fever, itching,
insomnia, depression, hallucinations, memory loss) to
construct affect-rich lottery problems. Monetary losses,
representing the amount of money that each participant
was willing to pay to avoid the side effects, were used to
construct affect-poor lottery problems (we demonstrate in
Study 2 that the side effects indeed trigger stronger affect
than their monetary equivalents). Participants were presented
with a total of three tasks. In a monetary evaluation task,
participants were asked to imagine that they suffer from a
specific illness and that two alternative medications are
available to treat the illness. Both medications treat the illness
equally effectively but one medication has a particular side
effect, whereas the other medication has no side effect.
Participants indicated for each of the seven side effects their
willingness-to-pay (WTP)—that is, how much more they
would be willing to pay (in $ or € for the American and
German samples, respectively) for a packet of the medication
that does not have the side effect compared with the medication
that does have the side effect.
The next tasks were two lottery tasks, each of which
consisted of four lottery problems. In each problem of the first
lottery task, participants were asked to choose between two
medications, both being equally effective in targeting the
disease but implicating the possibility of different side effects:
Medication A: With a probability of 15% the medication leads to
fever as a side effect, with a probability of 85% no side effects occur.
Medication B: With a probability of 10% the medication leads to
insomnia as a side effect, with a probability of 90% no side effects
occur.
We constructed these problems such that the expected
values of the two options—calculated on the basis of monetary evaluations of the side effects in a pilot study—would
be comparable (to avoid clearly dominated options) and
that a more aversive side effect would occur with a smaller
probabilities than a less aversive side effect. (The probability
of the side effects, which were the same for all participants,
varied between .5% and 70%. A complete list of the lottery
problems is available upon request from the authors.). In each
problem of the second lottery task, participants were presented with a choice between the same lotteries as in the first
Copyright © 2012 John Wiley & Sons, Ltd.
lottery task, except that the side effects were replaced with
the monetary amount that the person indicated for the respective side effect in the monetary evaluation task. For instance,
consider a person who had indicated that she would be willing
to pay $20 to avoid a fever and $25 to avoid insomnia. Subsequently, she would be presented with the problem:
Lottery A:
Lottery B:
With a probability of 15% you lose $20,
with a probability of 85% you lose nothing.
With a probability of 10% you lose $25,
with a probability of 90% you lose nothing.
p = .15
p = .85
p = .10
p = .90
Because in previous studies (Pachur, Hertwig, & Wolkewitz,
2012; Suter et al., 2012) we had consistently found that side
effects trigger stronger affect than their monetary equivalents,
we refer to these two lottery tasks as affect-rich and affect-poor,
respectively. Importantly, because for the affect-poor problems,
we used the WTP of each side effect that each individual
participant had indicated in the monetary evaluation task, the
problems in the affect-poor lottery task and those in the affectrich lottery task were structurally equivalent.
Design and procedure
All tasks were administered on a computer. Participants
first completed the monetary evaluation task, followed by the
affect-rich and affect-poor lottery tasks (their order was
counterbalanced across participants).
Results
Choices
We conducted an ANOVA with the proportion of choices
(in the lottery tasks) of the option with higher expected value
as dependent variable and numeracy group (i.e., participants
sampled either from the top or bottom half of the numeracy distribution) and country as independent variable.1 The expected
1
For the analysis of people’s choices of the option with the higher expected
value, 89 of 1047 participants were excluded because, on the basis of their
responses in the monetary evaluation task, in all problems both options had
identical expected values, and thus, the option with the highest expected value
was not defined. For the strategy classification, however, these participants
were included, as based on their evaluations they were predicted to guess.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
T. Pachur and M. Galesic
values of the options in the affect-rich problems were calculated using each participant’s WTPs of the side effects. As hypothesized, the more numerate participants chose the option
with the higher expected value more frequently than the less
numerate participants, Ms = 64.0% (SD = 20.7) vs. 59.2%
(SD = 23.8), F(1, 956) = 8.48, p = .004, 2p = .009 (note that
the choice of the option with a higher expected value does
not necessarily mean that the EV strategy was used; we turn
to a modeling of the choice strategies in the next section). This
pattern held for both U.S. and German participants—as
indicated by a nonsignificant interaction between numeracy
and country, F(1, 956) = 1.97, p = .16, 2p = .002; overall,
however, the U.S. participants chose the option with the
higher expected value more often than the German participants, Ms = 68.4% (SD = 20.6) vs. 55.5% (SD = 22.1),
F(1, 956) = 87.30, p = .001, 2p = .084.
In addition, there were strong differences in choice
between the affect-rich and affect-poor lottery tasks. We
conducted a repeated-measures ANOVA with the proportion of choices of the option with the higher expected
value as dependent variable, type of lottery task (affect-rich
vs. affect-poor) as within-subjects factor, and numeracy
group and country as between-subjects factors. Whereas
in the affect-poor lottery task participants chose the option
with the higher expected value in, on average, 71.5%
(SD = 28.8) of the cases, in the affect-rich lottery task,
they chose that option in only 52.0% (SD = 30.8) of the
cases, F(1, 956) = 7.32, p = .007, 2p = .008. The difference
between the affect-poor and affect-rich choices was not
affected by numeracy, F(1, 956) = .56, p = .46, 2p = .001
(more numerate: Ms = 74.3% [SD = 27.1] vs. 53.8%
[SD = 30.2]; less numerate: Ms = 68.3% [SD = 30.2] vs.
50.0% [SD = 31.5]). The choice differences were reflected
in a high proportion of within-person preference reversals between affect-rich and affect-poor problems: Averaged across participants, preferences reversed in 52.5%
(SD = 33.1) of the cases. Unlike hypothesized, however,
the proportions of preference reversals did not differ between more and less numerate participants (based on an
ANOVA with the proportion of preference reversals as dependent variable and numeracy and country as independent
variable), Ms = 52.7% (SD = 32.7) vs. 52.2% (SD = 33.7),
F(1, 1041) = .38, p = .54, 2p < .0001. This result held for
both U.S. and German participants, F(1, 1041) = .76,
p = .38, 2p = .001. U.S. participants tended to show, overall,
a lower proportion of preference reversals than German
participants, Ms = 42.7% (SD = 30.6) vs. 62.9% (SD = 32.8),
F(1, 1041) = 94.46, p = .001, 2p = .083.
Strategy selection
The aforementioned analysis showed that participants often
reversed their preferences between the structurally equivalent
affect-poor and affect-rich problems in the lottery tasks. Were
the preference reversals systematic, resulting from the use of
different choice strategies? Moreover, how did more and less
numerate participants differ in their strategy selection? To address these questions, we modeled each participant’s choices
with the EV strategy, the least-likely heuristic, and the
Copyright © 2012 John Wiley & Sons, Ltd.
Numeracy, Affect, and Risky Choice
minimax heuristic, separately for the affect-poor and affectrich lottery tasks, and classified each participant to the strategy with the best fit.2 Note that because we used the WTPs
from the monetary evaluation task as a proxy for how people
evaluated the side effect when making choices, for the affectrich lottery task, the strategies make the same predictions as
for the affect-poor task. The classification was based on a
maximum likelihood approach. Accordingly, we determined
for each participant i the goodness of fit of strategy k as
G2i;k ¼ 2
XN
j¼1
ln fj ðyÞ
(1)
where fj(y) represents the probability with which a strategy
predicts an individual choice y at lottery problem j. That
is, if an observed choice coincided with the strategy’s
prediction, fj(y) = 1 ei,k; otherwise fj(y) = ei,k, where ei,k
represents participant i’s application error (across all N pairs
of lottery problems) for strategy k (Sokal & Rohlf, 1994).
For each strategy, ei,k was estimated as the proportion of
choices that deviated from strategy k’s predictions (which
represents the maximum likelihood estimate of this parameter;
cf. Bröder & Schiffer, 2003). Participants were classified as
following the strategy with the lowest G2 (indicating the best
fit). If the G2 of the best-fitting strategy equaled (or was higher
than) the value of G2 under random choice (i.e., with e = .5),
then the participant was classified to the category “guessing
or other strategy.” As a measure of classification confidence,
we calculated a Bayes factor for each classification. The Bayes
factor is defined based on the Bayes information criterion
(BIC) differences between the best-fitting
and the secondbest-fitting model, BF ¼ exp 12 ΔBIC (for details, see
Wasserman, 2000). A Bayes factor in the range of 3 to 10 gives
moderate evidence for the classification, and a Bayes factor
larger than 10 indicates strong evidence. Across participants
(excluding participants classified as guessing), the median
Bayes factor was BF = 9.11 and 6.44 for the affect-poor and affect-rich lottery tasks, respectively, indicating moderate evidence for the strategy to which each participant was assigned.
Figure 1(a) shows the proportion of participants classified
as following EV, least-likely, minimax, or to the “guessing or
other strategy” category, separately for the affect-poor and
the affect-rich lottery tasks. As can be seen, participants
clearly relied on different strategies in the affect-poor and
affect-rich problems, as indicated by a significant association
between affect and strategy, w2(3, N = 2094) = 281.37,
p = .001, Cramer’s V = .37 (additional analyses showed
that all conclusions hold when controlling for a possible
confound of numeracy and country with gender and age).
Specifically, in the affect-poor problems, large proportions of
participants were classified as following EV and least-likely;
in affect-rich problems, most participants were classified as
following minimax or to the “guessing or other strategy”
category. Overall, the distribution across the strategies showed
a similar pattern for both numeracy groups, indicated by a
nonsignificant three-way association between strategy, affect,
2
The overlap between the predictions of the different strategies was, on average, 39.1% between EV and minimax, 67.9% between EV and least-likely,
and 8.4% between minimax and least-likely.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
Journal of Behavioral Decision Making
Figure 1. Classification of participants on the basis of their responses in the lottery tasks in Study 1, separately for the affect-rich and affectpoor lottery tasks (a), more and less numerate participants (b), and the U.S. and the German samples (c)
and numeracy (based on a log-linear analysis), w2(3,
N = 2094) = 4.15, p = .25. Nevertheless, Figure 1(b) shows that
strategy selection was associated with numeracy, w2(3,
N = 2094) = 26.74, p = .001, Cramer’s V = .11. Across both
types of lottery tasks, the less numerate participants were
somewhat less often classified as following EV (18.2% vs.
22.6%) or the minimax heuristic (19.2% vs. 25.7%) and
more often classified as following the least-likely heuristic
(33% vs. 26.7%) or to the “guessing or other strategy” category
(29.6% vs. 25.0%) than the more numerate participants.
These patterns held for both U.S. and German participants,
indicated by a nonsignificant three-way association between
strategy, numeracy, and country, w2(3, N = 2094) = 2.26,
p = .52 (based on a log-linear analysis). Nevertheless, as
Figure 1(c) shows, strategy selection was associated with
country, w2(3, N = 2094) = 107.3, p = .001, Cramer’s V = .23:
U.S. participants followed the compensatory EV strategy more
frequently than German participants (25.5% vs. 15.5%). In
addition, they more often followed the least-likely heuristic
(35.5% vs. 24.5%) and were less frequently classified to
the “guessing or other strategy” category (18.5% vs. 36.0%)
compared with German participants.
Summary and discussion
In Study 1 we found that, as hypothesized, less numerate
participants selected the option with the higher expected value
less frequently than more numerate participants. However, less
numerate participants did not seem to simplify their decisionmaking by focusing on outcome information (i.e., minimax);
rather, they relied more on the least-likely heuristic, which
strives to minimize the probability of experiencing the worst
outcome. Overall, we do not find that less numerate participants generally avoid strategies that consider probability
information.
Concerning the comparison between the United States
and Germany, U.S. participants selected the normatively
better option more frequently than German participants,
showed more consistent choices between affect-poor and
affect-rich problems, and seemed to use the compensatory
EV strategy, which trades off outcome and probability,
Copyright © 2012 John Wiley & Sons, Ltd.
more often. These results may seem surprising given that
U.S. participants had somewhat lower numeracy scores
than German participants. However, current numeracy
scales focus primarily on the understanding and manipulation of probabilities, not on the ability to combine probabilities and outcomes to make a choice.
Why might people in the United States be more inclined
than people in Germany to make trade-offs when making
decisions? One contributing factor might be that one crucial
mental operation for making trade-offs between outcomes
and probabilities, namely calculating the fraction of a monetary
value, is an almost every day procedure for many Americans:
in the United States, tips to service personnel are expected
and typically around 15% of the amount; in Germany, by
contrast, tips are optional and usually calculated by simply
rounding up the amount. Although the reasons for these
cross-cultural differences deserve to be scrutinized further in
future studies, our results are in line with studies on judgment
and decision-making styles in different countries, which
showed that Americans have a strong preference for using
compensatory strategies (Chu & Spires, 2008; Leng &
Botelho, 2010).
Unexpectedly, in Study 1, choices and strategy selection of
more and less numerate participants seemed to differ in similar
ways between affect-poor and affect-rich problems. In affectpoor problems, both groups mainly followed the prediction
of the least-likely heuristic and the EV strategy. In affect-rich
problems, both groups mainly followed the minimax heuristic,
or guessed or used a strategy not included in our model comparison. This finding may seem surprising given that people
with low numeracy have previously been found to be more
influenced in their decision-making by contextual factors
(e.g., emotions). In Study 2, we examine the impact of
numeracy on strategy selection further.
STUDY 2
Our first aim in Study 2 was to use a more nuanced approach
to examine differences in strategy selection between less and
more numerate participants. On the basis of the thesis that
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
T. Pachur and M. Galesic
affective information might play an important role in risky
decision-making (Loewenstein et al., 2001), we tested how
well an implementation of the affect heuristic (Finucane
et al., 2000) is able to capture participants’ choices. To derive
the predictions of the affect heuristic, we asked participants
to indicate for each affect-poor (i.e., monetary loss) and each
affect-rich (i.e., side effect) outcome the amount of negative
affect that the outcome would trigger. Because participants
might not be able to discriminate between two outcomes in
terms of their WTP, but still find one outcome less
affectively adverse than another, the affect heuristic could
sometimes make different predictions than minimax. We
hypothesized that less numerate participants would be more
likely to select the affect heuristic than minimax, because
evaluating and comparing options in terms of affect
rather than money may be easier (Peters et al., 2009). The
affect ratings for the outcomes also enabled us to test our
assumption that the side effects trigger stronger affect than
their monetary equivalents. Further, we attempted to better
distinguish between EV and the least-likely heuristic, whose
predictions overlapped in, on average, 67.9% of the cases in
Study 1. To do so, we added two lottery problems (for both
the affect-rich and the affect-poor lottery tasks) for which
we expected that EV and least-likely would often make
opposing predictions.
A second, important aim of Study 2 was to test whether the
finding in Study 1 that the proportion of participants classified
as following a strategy that is based on monetary outcome
information (i.e., EV and minimax; see Figure 1(a)) was somewhat lower for the less numerate group might have been due
to this group being less reliable in providing WTPs for the
side effects in the monetary evaluation task. To test this possibility, we asked participants to indicate their WTPs for the
side effects twice.
Method
Participants
One hundred and eighteen German participants were selected
from a pool of respondents willing to participate in online
studies, maintained by the company Respondi (www.
respondi.com). Half of the sample had lower education
(no Abitur—the end-of-school exam that permits students
to apply to college) and half had higher education (Abitur
or more). On the basis of previous results (Galesic &
Garcia-Retamero, 2010), we expected that the differences
in education would lead to a less numerate and a more
numerate group. All participants completed the same numeracy test used in Study 1 and were classified as being more
or less numerate by a median split (the median score was
again 6). Table 2 shows demographic characteristics of the
resulting two numeracy groups.
Numeracy, Affect, and Risky Choice
Table 2. Sample structure in Study 2, by numeracy, sex, and age
Total
Sex
Male
Female
Age (years)
1839
4054
5569
Less numerate
(M = 4.06, SD = 1.65)
More numerate
(M = 8.25, SD = .83)
53
65
20
33
35
30
26
17
10
29
13
23
Note: The gender distribution within each age group was similar to the
overall gender distribution for each numeracy group in both countries.
predictions of EV and the least-likely heuristic.3 Second, we
added an affective evaluation task (presented after the lottery
tasks), which consisted of two parts (the order in which the
two parts were presented was counterbalanced across participants). In the first part, participants were asked to imagine that
they had taken a medication and would, as a consequence,
experience a side effect. For each of the seven side effects, they
were instructed to indicate on a scale from 1 (not upset) to 10
(very upset) the amount of negative affect elicited by experiencing the side effect.4 Suter et al. (2012) have shown that
people’s responses to this item are highly correlated with their
general affective reactions to the side effects (as measured by a
sum score across nine positive and negative emotions). In the
second part, participants were asked to imagine that they had
lost a bet and would have to pay money. For each of the
monetary amounts a participant had indicated as WTP for
the side effects in the monetary evaluation task, they rated
how upset they would be by having to pay the respective
amount. Finally, to compare the reliability of more and less
numerate participants’ monetary evaluations, we presented
(unexpectedly for the participants) the monetary evaluation
task for a second time at the end of the study.
Results
As can be seen in Table 3, for each of the seven side effects,
participants indicated that experiencing it would trigger a
significantly higher negative affect than losing the monetary
equivalent of the side effect. This supports our assumption
that the lottery problems with side effects and monetary
outcomes represent (relatively) affect-rich and affect-poor
lottery tasks, respectively.
Reliability of monetary evaluations
We found no evidence that the less numerate participants
provided less reliable monetary evaluations of the side
3
Materials, design, and procedure
Materials, design, and procedure were the same as in Study 1,
except for three modifications. First, we added two problems
to both the affect-rich and the affect-poor lottery tasks, which
we expected (based on pilot data) would often lead to different
Copyright © 2012 John Wiley & Sons, Ltd.
Specifically, these additional problems were constructed such that the option with the higher expected value (based on the median monetary evaluation of the side effects in a pilot study) led to the side effect with a lower
probability than the option with the lower expected value. As a consequence
the former was more attractive according to the EV strategy, but less attractive according to the least-likely heuristic.
4
We used the German expression “sich ärgern,” which refers to a negative
emotion between being upset, angry, and annoyed.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
Journal of Behavioral Decision Making
Table 3. Affective evaluation (from 1 = not upset to 10 = very upset) of the side effects and the monetary amounts that participants indicated
they were willing to pay to avoid the side effects and results of the paired-sample t-test
Side effects
Outcome
Fatigue
Insomnia
Fever
Itching
Depression
Memory loss
Hallucinations
Monetary amounts
Statistical test
M
SD
M
SD
t
p
5.66
6.75
6.35
7.29
8.83
8.72
8.70
2.27
1.90
1.98
2.06
1.46
1.75
1.74
3.48
4.32
4.71
5.17
6.48
6.84
7.44
2.39
2.09
2.38
2.85
2.38
2.48
2.54
8.10
5.71
4.80
5.20
6.24
3.53
1.97
.001
.001
.001
.001
.001
.002
.061
effects than the more numerate participants. The correlation
(across side effects) between the first and the second evaluation (calculated for each participant) did not differ between
the two groups, Ms = .63 (SD = .41) vs. .72 (SD = .41),
F(1, 80) = .87, p = .36, 2p = .010; nor did the average (across
side effects) absolute deviation (in €) between the two
evaluations, Ms = 4.45 (SD = 12.37) vs. 2.67 (SD = 5.64),
F(1, 113) = 1.46, p = .23, 2p = .013.
Choices
We conducted an ANOVA with numeracy group as
independent variable and the proportion of choices of the
option with the higher expected value as dependent variable.
As in Study 1, the more numerate participants chose the
option with the higher expected value more frequently than
the less numerate participants, Ms = 66.3% (SD = 18.5) vs.
56.7% (SD = 21.6), F(1, 104) = 6.79, p = .01, 2p = .061 (the
calculation of the expected value was based on participants’
responses in the first monetary evaluation task). Moreover,
participants chose the option with the higher expected
value more often in the affect-poor lottery task than in the
affect-rich lottery task (Ms = 70.0%, SD = 24.8 vs. 54.5%,
SD = 29.1), F(1, 104) = 3.48, p = .07, 2p = .032 (repeatedmeasures ANOVA with type of lottery task as withinsubjects factor and numeracy group as between-subjects
factor). The difference between the affect-poor and the
affect-rich choices was not affected by numeracy,
F(1, 104) = .04, p = .84, 2p < .0001 (high numeracy:
Ms = 73.5% [SD = 22.4] vs. 59.1% [SD = 28.1]; low numeracy: Ms = 65.2% [SD = 27.3] vs. 48.3% [SD = 29.6]). In, on
average, 48.4% (SD = 31.1) of the cases, participants
reversed their preferences between affect-poor and affectrich problems. The proportion of preference reversals
did not differ between the more and less numerate participants, Ms = 50.8% (SD = 29.4) vs. 45.6% (SD = 33.0),
F(1, 114) = 1.43, p = .23, 2p = .012, replicating Study 1.
Although, as reported above, the reliability of people’s
monetary evaluations was rather high, it was not perfect
(i.e., when asked twice, participants did not always provide
exactly the same value for a side effect). Was this lack of
reliability responsible for the frequent inconsistencies
between people’s choices in the affect-rich and affect-poor
tasks? Additional analyses provided no evidence for this possibility. Specifically, the proportion of preference reversals
was neither associated with individual differences in the correlation between the first and the second evaluation (r = .03,
Copyright © 2012 John Wiley & Sons, Ltd.
p = .80), nor with the deviation between the first and second
monetary evaluations (r = .05, p = .57). The reliability measures were also uncorrelated with the proportion of choices of
the option with the higher expected value (p > .15).
Strategy selection
Figure 2 shows the proportion of participants classified as
following EV, least-likely, minimax, the affect heuristic, or
to the “guessing or other strategy” category (using the same
classification procedure as in Study 1). The predictions for
EV, least-likely, and minimax were derived as in Study 1.
The affect heuristic predicted that the option with the
outcome (i.e., side effect or monetary loss) that triggered
the lower amount of negative affect (as measured in the
affective evaluation task) was chosen. Across participants
(excluding participants classified as guessing), the median
Bayes factor for the strategy classification was BF = 7.10
and 4.29 for the affect-poor and affect-rich lottery tasks,
respectively. As in Study 1, there was an association between
affect and strategy, w2(4, N = 236) = 33.58, p = .001, Cramer’s
V = .38, such that in affect-poor choices, the least-likely
heuristic was clearly most prevalent, whereas in affect-rich
choices, participants were more equally distributed across
minimax, least-likely, and guessing. (As for Study 1 additional analyses showed that all results reported in this section
hold also when controlling for gender and age.) There was
also an association between numeracy and strategy, w2(4,
N = 236) = 9.34, p = .05, Cramer’s V = .20, such that overall,
EV and minimax were more prevalent among the more
numerate participants and least-likely less prevalent than
among the less numerate participants.
However, there were also differences from Study 1—
possibly due to the additional lottery problems and the
addition of the affect heuristic (compared with Study 1, the
proportion of cases where the predictions of EV and leastlikely overlapped dropped to, on average, 56.9%5). First, a
5
The average overlap between the predictions of the other strategies was
50.1% between EV and minimax and 9.2% between minimax and leastlikely. For the affect-rich and affect-poor lottery tasks, the affect heuristic’s
predictions overlapped with those of EV by 29.1% and 45.1%, respectively;
with those of least-likely by 7.5% and 8.2%, respectively; and with those of
minimax by 51.4% and 88.4%, respectively. Of those cases where minimax
and the affect heuristic made different predictions, the strategies predicted
opposite choices 17.7% (affect-rich task) and 19.5% (affect-poor task) of
the time; for the other cases, the predictions diverged because one strategy
predicted a guess.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
T. Pachur and M. Galesic
Numeracy, Affect, and Risky Choice
Figure 2. Classification of participants on the basis of their responses in the affect-rich and affect-poor lottery tasks in Study 2
considerably lower proportion of participants were classified
to the “guessing or other strategy” category (less numerate
participants were, overall, still more likely to be classified to
this category than more numerate participants: 12.3% vs.
6.2%). Second, the affect heuristic captured the choices of a
substantial proportion of both more and less numerate participants, in particular in the affect-rich lottery task. An additional
analysis showed that using only the strategies tested in Study 1,
7.4%, 3.7%, 33.3%, and 55.6% of the participants classified as
following the affect heuristic in the affect-rich lottery task
would have been classified as following EV, least-likely,
minimax, and to the “guessing or other strategy” category,
respectively. Of the participants classified as following the
affect heuristic in the affect-poor lottery task, all would have
been classified to the “guessing or other strategy” category.
As in Study 1, the overall pattern of differences in strategy
selection between the affect-rich and the affect-poor problems
was similar for the less and the more numerate participants, as
indicated by a nonsignificant three-way association between
strategy, affect, and numeracy (based on a log-linear analysis),
w2(4, N = 236) = 4.74, p = .32. Nevertheless, as Figure 2 shows,
there were now some clear differences between the two
numeracy groups: Specifically, whereas the more numerate
participants relied less on EV and more on minimax in
affect-rich than in affect-poor choices (z = 2.96, p = .007 and
z = 7.21, p = .001, respectively), for the less numerate, the
use of EV and minimax was invariably low in the two types
of lottery tasks (z = .27, p = .79 and z = .53, p = .60, respectively). Finally, although collapsed across affect-rich and
affect-poor problems, similar proportions of more and less
numerate participants were classified as following the affect
heuristic (16.7% vs. 18.6%); in the affect-rich problems, the
less numerate group showed a preference for the affect heuristic over the minimax heuristic (z = 3.15, p = .002), whereas for
the more numerate group, this was not the case (z = 1.23,
p = .21), and there was even a slight trend in the opposite
direction.
Summary and discussion
On the basis of a more nuanced approach to model people’s
strategies, Study 2 suggests that when making affect-rich
choices, more and less numerate participants relied on
different outcome-based heuristics. The more numerate
participants often used the minimax heuristic, whereas the
less numerate participants often used the affect heuristic.
Moreover, a substantial proportion of the less numerate
Copyright © 2012 John Wiley & Sons, Ltd.
participants followed the least-likely heuristic even in
affect-rich problems, whereas this was not the case for more
numerate participants. There was no evidence that differences in strategy selection between the two numeracy groups
were due to differences in reliability in evaluating options
monetarily. Finally, the finding that measures of reliability
of the monetary evaluations were uncorrelated with the proportion of preference reversals and the proportion of
expected value choices suggests that differences in reliability
are an unlikely reason for differences in choice between more
and less numerate people and the cross-cultural differences
observed in Study 1.
Our results provide no evidence that the difference between affect-rich and affect-poor choices is due to a lack of
reliability in WTP evaluations of the side effects. First, the
proportion of preference reversals was unrelated to the reliability in the monetary evaluation task. Second, if the apparent
differences between the affect-rich and affect-poor lottery
tasks occur because people’s WTPs for the side effects are
not reliable, there should be more participants in the affectrich choices classified as guessing. As Figure 2 shows, however, this was not the case. Moreover, based on the same
paradigm as used in our studies, Suter et al. (2012) found that
choices in affect-rich and affect-poor tasks diverged even
when the affect-rich outcomes were presented along with the
individual participant’s monetary evaluations of the side
effects (see also Rottenstreich & Hsee, 2001).
Study 2 also showed that, as assumed and shown in earlier
studies, the side effects trigger stronger negative affect than
their monetary equivalents. The more frequent use of minimax
and our implementation of the affect heuristic (both of which
ignore probabilities) in choices among the medications than
in choices among the monetary lotteries may thus, at least in
part, be due to differences in affect—in line with Sunstein’s
(2002) proposal that in affect-rich decisions, people are less
sensitive to probability information. We cannot exclude, however, that additional factors, such as the nonnumerical nature of
the side effects, might have contributed to the differences in
strategy use (cf. McGraw et al., 2010; but see Rottenstreich
& Hsee, 2001; Suter et al., 2012).
GENERAL DISCUSSION
In two studies, we examined how numeracy and cross-cultural
differences influence the difference between affect-poor and
affect-rich risky choice and the strategies people use to make
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
Journal of Behavioral Decision Making
the choices. There was a large proportion of preference reversals between affect-poor and affect-rich problems, and participants avoided trade-offs more frequently in affect-rich choices.
We found that less numerate participants were similarly inconsistent between affect-poor and affect-rich choice problems as
more numerate participants, and both groups preferred outcome-based heuristics when making affect-rich choices. Nevertheless, the type of outcome-based heuristics differed between more and less numerate people: Whereas more
numerate participants showed a preference for the minimax
heuristic in affect-rich choices, the less numerate participants
preferred the affect heuristic. Overall, less numerate participants were less likely to choose the option with the higher
expected value and seemed to rely primarily on the least-likely
heuristic, which focuses on minimizing the probability of experiencing the worst outcome, and the affect heuristic, which
focuses on avoiding the worst affective outcome (Figure 2).
The more numerate participants, in contrast, seemed to use a
broader set of decision strategies, including a compensatory
one (EV).
Concerning cross-cultural differences examined in Study 1,
we found that U.S. participants chose more consistently across
affect-poor and affect-rich problems than German participants.
U.S. participants also chose the normatively better option more
frequently than German participants and used compensatory
strategies more often.
Our results extend the work of Rottenstreich and Hsee
(2001) by examining and contrasting the strategies that
underlie people’s affect-rich and affect-poor choices. We
demonstrated that simple noncompensatory heuristics that
ignore probability information provide a good description
of people’s decisions between affect-rich options. Evidence
for a limited attention to probabilities in affect-rich choices
was also found in a process-tracing experiment by Pachur
et al. (2012), in which participants paid less attention to
probability information in affect-poor than in affect-rich
problems.
Numeracy and strategy selection
Our results provide important insights into how decision
makers with low numeracy simplify the decision process when
making risky choices. Their more frequent use of heuristics
supports the notion that less numerate people are less likely
to integrate multiple pieces of information (cf. Peters et al.,
2006). However, the observed frequent use of the least-likely
heuristic by the less numerate participants suggests that rather
than focusing on only one reason (e.g., probability or
outcome), they often simplify the decision by processing information sequentially (recall that least-likely first identifies
each option’s worst outcome and then chooses the option with
the lower probability of the worst outcome).
Our finding that more numerate participants frequently
chose the option with the higher expected value but were
nevertheless better described by the least-likely heuristic (in
particular in affect-poor problems) is consistent with verbal
protocol analyses by Cokely and Kelley (2009). These
authors showed that although high-numeracy participants
often selected the option with the higher expected value,
Copyright © 2012 John Wiley & Sons, Ltd.
these choices were produced by simple processing principles
(e.g., “5% probably won’t happen) that sometimes mimic the
choices of the EV strategy. The evidence for the least-likely
heuristic is in line with earlier studies showing that the attractiveness of lotteries is largely influenced by probabilities of
winning and losing rather than by monetary outcomes (Slovic & Lichtenstein, 1968; Venkatraman, Payne, Bettman,
Luce, & Huettel, 2009).
On the basis of previous research (Peters & Levin,
2008), we had hypothesized that less numerate participants
would more frequently rely on the affect heuristic. Our
results provide partial support for this view. On the one
hand, less numerate participants were not more frequently
classified as following the affect heuristic than more
numerate participants. On the other hand, in affect-rich
problems, less numerate participants relied more on the
affect heuristic than on the minimax heuristic, whereas
more numerate participants showed the opposite pattern
(Figure 2). Nevertheless, it may seem surprising that the
differences between affect-rich and affect-poor choices
were not larger for the less numerate participants than for
the more numerate participants. One possible reason could
lie in the source of affect. Specifically, whereas studies that
found a stronger susceptibility to affective information
among low numeracy people often involved incidental
affect, our study focused on affect arising directly from
the stimuli (i.e., being directly triggered by the prospect
of experiencing the outcome).
It should be emphasized, however, that our instantiation
of the affect heuristic considered only people’s affective
reactions to outcomes. It is possible that people also consider
affect triggered by probability information. To the extent that
people high in numeracy draw stronger affective meaning
from probability information than people low in numeracy
(cf. Peters et al., 2006), an extended model of the affect heuristic that considers this source of affect as well might
account for the decisions of more numerate people better
than the instantiation used in our studies.
CONCLUSION
Despite having difficulty in interpreting probability information, less numerate people do not seem to ignore probabilities generally in risky choice, but rather process them
differently than more numerate people. Moreover, although
less and more numerate people share a tendency of using
outcome-based heuristics for making affect-rich decisions,
more numerate people seem to make decisions reflecting
the monetary value of the outcomes, whereas less numerate
people tend to focus more on the affective value of the
outcomes. As suggested by the cross-cultural differences
in Study 1, the negative impact of low numeracy on
decision-making can sometimes be offset by cultural
practices in making trade-offs. These insights should
inform the development of effective new ways to increase
decision quality. Moreover, they highlight that such interventions may need to be tailored to both the decision
maker’s capacities and her cultural experiences.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm
T. Pachur and M. Galesic
ACKNOWLEDGEMENTS
This research was supported by Swiss National Science
Foundation grant 100014_125047/2 and a grant by the
Suzanne and Hans Biäsch Foundation awarded to Thorsten
Pachur, and a grant by the Foundation for Informed
Medical Decision Making, within the project “Helping
people with low numeracy to understand medical information.”,
awarded to Mirta Galesic. We thank Wolfgang Gaissmaier,
Rocio Garcia-Retamero, and Renata Suter for their comments
on an earlier draft of this article and Anita Todd and Laura Wiles
for editing the manuscript.
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Basel. His research interests include memory and other cognitive
processes in judgment and decision-making, frequency cognition,
and the psychology of risky choice.
Mirta Galesic received her PhD from the University of Zagreb in
2004. She works as a Research Scientist at the Max Planck Institute
for Human Development in Berlin. Her research interests include
social rationality, sampling models of cognition, and the communication of risks.
Authors’ addresses:
Authors’ biographies:
Thorsten Pachur received his PhD from the Free University in
Berlin in 2006. He worked at the Max Planck Institute for Human
Development and is now a Research Scientist at the University of
Copyright © 2012 John Wiley & Sons, Ltd.
Thorsten Pachur, Department of Psychology, University of Basel,
Switzerland.
Mirta Galesic, Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Berlin, Germany.
J. Behav. Dec. Making (2012)
DOI: 10.1002/bdm