VALUES, EMPATHY, AND FAIRNESS ACROSS SOCIAL BARRIERS
Experimental Game Theory
and Behavior Genetics
David Cesarini,a Christopher T. Dawes,b
Magnus Johannesson,c Paul Lichtenstein,d and Björn Wallacec
a
Department of Economics, Massachusetts Institute of Technology, Cambridge,
Massachusetts 02142, USA
b
Political Science Department, University of California, San Diego, La Jolla,
California 92093, USA
c
Department of Economics, Stockholm School of Economics, Stockholm, Sweden
d
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet,
Stockholm, Sweden
We summarize the findings from a research program studying the heritability of behavior in a number of widely used economic games, including trust, dictator, and ultimatum
games. Results from the standard behavior genetic variance decomposition suggest that
strategies and fundamental economic preference parameters are moderately heritable,
with estimates ranging from 18 to 42%. In addition, we also report new evidence on
so-called “hyperfair” preferences in the ultimatum game. We discuss the implications
of our findings with special reference to current efforts that seek to understand the
molecular genetic architecture of complex social behaviors.
Key words: behavior genetics; experimental economics; biomarkers
Introduction
In recent decades, economists have increasingly turned to laboratory experiments to study
the various forces that influence economic decision making. The most important benefit of
laboratory experiments is that other economic
data is primarily observational.1 Laboratory
experimentation gives the researcher the ability
to control key features of the studied economic
environment. Experiments can be used to both
estimate fundamental preference parameters,
such as discounting and attitudes toward risk,2
and to test theories of strategic interaction.3
Of course, there are legitimate concerns regarding the external validity of laboratory experiments, so benefits must always be weighed
against costs.4
Address for correspondence: David Cesarini, Department of Economics, 50 Memorial Drive, Cambridge, MA 02142. Voice: 617-4120196. [email protected]
One important and robust finding in laboratory experiments is that for a certain class
of games the assumption that agents act rationally to maximize their own material returns
does not predict behavior well.5 Rejected offers
in the ultimatum game6 present a clear example of this. In the ultimatum game, two subjects
are given an endowment of money, or “cake”,
by the experimenter and bargain under conditions of anonymity. One subject is assigned
the role of the proposer and the other that of
the receiver. The proposer makes an offer to the
receiver on how to divide the cake between
them. If the receiver accepts the proposer’s offer, the players are paid accordingly, whereas if
the offer is rejected, both players receive a zero
payoff. In a one-shot game, rational and materially maximizing responders should accept
any positive offer because the alternative is a
zero payoff. In practice, low offers are routinely
rejected and modal offers are 40 or 50% of the
endowment.3
Values, Empathy, and Fairness across Social Barriers: Ann. N.Y. Acad. Sci. 1167: 66–75 (2009).
c 2009 New York Academy of Sciences.
doi: 10.1111/j.1749-6632.2009.04505.x 66
Cesarini et al.: Game Theory and Behavior Genetics
For a wide class of other games, models based
on alternative assumptions about preferences
appear to better predict behavior.5 These findings, which arguably are stable across cultures
and for a wide range of experimental conditions,7 have inspired a large body of theoretical work seeking to incorporate considerations, such as aversion to inequity, spite, and
reciprocity, into models of decision making.8–10
A parallel literature also attempts to understand how such “other-regarding” or “social”
preferences evolved.11–13 Although controversy
still surrounds the question of how one should
think about laboratory manifestations of otherregarding preferences,4,14 it is generally agreed
that repeated observations in laboratory experiments say something interesting about human
nature.
A second finding that emerges from laboratory experiments is that there is significant
individual variation.3 This is true not only for
laboratory measures of social preferences but
also for more conventional preference parameters.2 In this paper, we survey recent work that
uses behavior genetic methods, in particular
studies of twins, to partition individual variation in how individuals play economic games.
Specifically, we summarize research findings on
the ultimatum game,15 the trust game,16 the
dictator game, and laboratory measures of risk
taking17 that have been reported in a number of recent papers. In addition to previously
published findings, we report new evidence on
so-called “hyperfair” preferences in ultimatum
games.18 We view this behavior genetic work as
complementary to attempts to find biological
markers of individual differences in economic
experiments.19–21
Behavior genetic analyses of laboratory
games serve a number of purposes. Despite ample experimental evidence, the origins of individual behavioral variation in economic games
have remained elusive, and attempts to find
theoretically appealing and empirically stable
correlates to preferences elicited experimentally have yielded contradictory results.3 Behavior genetic work, which is fundamentally
67
about the study of human variation, can provide important clues as to the sources of these
individual differences.22 Specifically, the twin
methodology may be used to partition individual differences into genetic effects, shared environmental effects, and unique environmental
effects.
The results from behavior genetic decompositions can be used to inform economic and
evolutionary models. For example, the finding
that income is heritable23 suggests that parent–
child income correlations, the standard metric
of persistence in research on social mobility,
cannot be given purely environmental interpretations. The finding of genetic variation in trust,
willingness to reject an offer perceived to be unfair, or willingness to take financial risks would
also tend to favor models for the evolution
of these behaviors that predict polymorphic
equilibria.
A number of genetic association studies,
looking for genetic variants that explain individual differences in economic games, are currently underway. The results reported here may
inform the design and implementation of such
studies. Most importantly, the heritabilities reported in the reviewed papers are always significantly different from zero and hence provide
a motivation for the molecular genetic studies.
The existence of genetic variation is obviously a
necessary condition for these efforts to have any
prospects of bearing fruit. Moreover, the general patterns of correlations we observe suggest
that dominance and epistatic effects cannot be
ruled out. If corroborated, this finding would
be consistent with the evidence from behavior genetic studies of personality that much of
the genetic variance in personality is nonadditive.24 It is also clear from the results surveyed
here that the monozygotic (MZ) and dizygotic
(DZ) correlations are substantially lower than
what has been observed for personality.24 We
will return to the issue of how this is best interpreted but suggest that one implication is that
research designs with multiple measurements
should be favored over those with single-round
experiments.
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Annals of the New York Academy of Sciences
The paper is structured as follows. Section
1 provides an informal introduction to behavior genetic methodology. Section 2 introduces the ultimatum, trust, and dictator games
and a measure of financial risk taking. Section
3 reports and discusses the results. Section 4
concludes.
Behavior Genetic Methodology
The logic behind behavior genetic methodology can be elucidated by means of a simple
example. Suppose a researcher has access to
a large sample of MZ twins separated at birth
and randomly assigned to environments. In this
scenario, any resemblance of the MZ twins can
ultimately be traced to genes, and the MZ correlation in the outcome variable of interest is a
measure of the share of variation explained by
genes (heritability). Studies of MZ twins separated at birth do in fact exist (although assignment to families is not random), and they
provide compelling evidence on the ubiquity of
genetic variation.25 However, because of the
difficulty of obtaining such samples, other
methods are usually employed.
The most common behavior genetic research design uses twins reared together. Comparing the behavior of identical and nonidentical twins reared together is also a form of
quasi-experiment. MZ and DZ twins differ in
their genetic relatedness. If a trait is heritable,
then it must be the case that the correlation
in MZ twins is higher than the correlation in
DZ twins. However, for twins reared together,
the fact that two twins were exposed to similar
environmental influences growing up is an additional source of similarity. Under some strong
structural assumptions, the correlations in MZ
and DZ twins can nevertheless be used to estimate the variance in the outcome variable that
is explained by additive genetic variation (A),
common environmental variation (C), and idiosyncratic variation (E).
Consider a pair of MZ twins and suppose
first that the phenotype (P) can be written as
the sum of two independent influences: additive
genetic effects and environmental influences, U.
Normalize the variables to be mean zero and
standard deviation one. We then have that,
P = aA + uU
(1)
where a and u are standardized regression coefficients. Using a superscript to denote the variables for twin 2 in a pair,
P = a A + u U .
(2)
Because for MZ twins A = A , the covariance
(which, because of our normalization, is also a
correlation) between the phenotypes of the two
twins is given by,
ρMZ = a 2 + u 2 COV (U, U )M Z .
(3)
Now consider a DZ pair. Under the assumptions of genetic additivity ad random assortative mating with respect to the trait of interest
and genetic additivity, it will be the case that,
COV (A , A )D Z = 0.5.
(4)
We then have that,
ρDZ = a 2 + u 2 COV (U, U )D Z .
(5)
Finally, we impose the equal environment assumption, namely that,
COV (U, U )MZ = COV (U, U )D Z .
(6)
In the standard behavior genetics framework,
environmental influences are generally written
as the sum of a common environmental component (C) and a nonshared environmental component (E), such that,
P = aA + cC + eE,
(7)
where, again, all variables are standardized.
With this terminology, the environmental covariance component of the trait correlation,
u 2 COV (U, U ), can be written as c2 , because
by definition any covariance must derive only
from the common component. This allows us
to write the individual variation as the sum of
three components, a2 , c2 , and e2 ; where a2 is the
69
Cesarini et al.: Game Theory and Behavior Genetics
share of variance explained by genetic differences, c2 is the share of variance explained by
common environmental influences, and e2 the
share of variance explained by nonshared environmental influences. With three equations
and three unknowns, the parameters of interest
are, therefore, identified and can be estimated
by maximum likelihood,26 least squares,27 and,
recently, also Bayesian methods.28
Threshold Models
Standard analysis of twin data proceeds under the assumption of normally distributed phenotypes. With categorical data, such an assumption may be problematic. As will become
clear, the new results reported in this paper are
based on a binary variable for “fairness” preferences, which takes the value 1 if a subject has
a non-monotonic strategy. We therefore follow
standard practice and use a threshold model.
A threshold model assumes that the two categories observed are merely arbitrary cutoffs
of some underlying distribution of the studied
trait. For each twin pair, the distribution of the
phenotype is assumed to be jointly normal with
unit variance and correlation varying as a function of zygosity. Maximum likelihood estimation is carried out with respect to the variance
components and the threshold, which also is estimated as a part of the model. For more details,
see Rijsdijk and Sham.29
Confidence intervals are based on likelihood
ratio tests. In particular, they are obtained by
displacing one parameter from its optimal value
until the deterioration in likelihood is significant. The logic underlying this approach is that
the model with the displaced parameter can be
thought of as a nested submodel of the general
model and hence a standard likelihood ratio
test is (asymptotically) valid for inference. The
algorithm, which estimates confidence intervals
for the parameters, is explained in some detail
in Neale and Miller.30 The new analyses on
hyperfair preferences reported here were run
using the software MX (Neale, M. C. [1995].
Mx: Statistical Modeling, 3rd ed., Box 980126
MCV, Richmond, VA, USA).
Discussion
In this section, we summarize work previously published in Wallace and colleagues15
and Cesarini and colleagues.16,17
Dictator Game
In the dictator game, which was first proposed by Forsythe and colleagues,31 a subject
simply decides how to split a sum of money between herself and a second party, usually under
conditions of anonymity. Dictator games are
reviewed in Camerer.3 A variant of the dictator
game in which a subject decides how to allocate
a sum of money between herself and a charity
was proposed by Eckel and Grossman.32 Fong33
has shown that empathy is a more important
motivation for dictator game giving when recipients are perceived to be in great need (in
their case welfare recipients). In Cesarini and
colleagues,17 subjects decided how to allocate
SEK 100 (about $15) between themselves and
a charity called Stadsmissionen. Stadsmissionen’s work is predominantly focused on helping
the homeless in Sweden.
Ultimatum Game
An ultimatum game is a simple bargaining
experiment that can be thought of as an extended dictator game. In the dictator game,
whatever allocation is proposed by the first
of two subjects is implemented. In an ultimatum game, the second player, also known as
the responder, can reject the proposed allocation. If the proposal is rejected, both players
earn nothing. Wallace and colleagues15 administered ultimatum games in a subject pool of
twins. In the first stage of the experiment, all
subjects played the role of proposer and were
asked to divide SEK 100 (approximately $15)
between themselves and a randomly selected
anonymous counterpart (not an individual’s
co-twin). In the second and final stage, all subjects played the role of responder and were
once again matched with a randomly selected
anonymous counterpart (different from the one
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Annals of the New York Academy of Sciences
in stage 1) who was not participating in the
same session. Before learning the actual offer,
subjects indicated how they would react to each
possible offer.34 The researchers recorded the
lowest offer that was accepted in the range of
offers between 0% and 50% and took this to be
the outcome variable.
vestor. Cesarini and colleagues16 administered
trust games to two subject pools of twins, one
Swedish sample and one US sample, recruited
from the Twinsburg festival in Ohio. In the
Swedish sample, the endowment was SEK 50;
in the US sample, the endowment was set at
$10.
Hyperfair Preferences
Risk Preferences
The original design by Wallace and colleagues15 used the strategy method to define
acceptance thresholds. This method makes it
possible to recover the entire strategy of each
participant and allows us to examine in further detail a well-known finding, namely that
exceedingly generous offers are sometimes rejected. We characterize an individual who exhibits a non-monotonic strategy over the range
from 0 to 100 as hyperfair. Because all subjects
accept a 50-50 split, this amounts, in practice,
to examining whether or not the proclivity to
reject overly generous offers is heritable. We define an indicator variable taking the value 1 if a
subject rejected at least one offer of more than
half the endowment and characterize such individuals as having hyperfair preferences. We
are agnostic about whether or not this label is
appropriate.
To measure risk aversion, Cesarini and colleagues17 presented subjects with six choices,
each between a certain payoff and a 50/50
gamble for SEK 100, 30, 40, 50, 60, or 80. After subjects had made their six choices, one of
these was randomly chosen for payoff by rolling
a die. The gamble was resolved with a coin
toss in front of the participants. This method
was originally proposed by Holt and Laury.36
It determines seven intervals for the certainty
equivalent of the gamble. The highest certain
payoff that an individual is willing to gamble is
taken as the measure of risk aversion.
Trust Game
The trust game was developed by Berg and
colleagues.35 In a trust game, both individuals
are given an endowment by the experimenter.
One player, known as the investor, decides how
much money out of this initial endowment to
send to another player, known as the trustee.
Both players are instructed that the amount
sent will be tripled as part of the experiment.
In the one-shot game, the investor only makes
one investment and conditions of anonymity
are maintained throughout. The trustee is instructed to repay any amount of the tripled
investment back to the investor. The trustee
plays a dictator game in which the amount
to be allocated was determined by the in-
Samples
We end this section with a brief description
of the samples. Wallace and colleagues,15 studying ultimatum game rejections, used a sample of 658 Swedish twins recruited from the
population-based twin registry. The same sample was also used in the study of the trust game
by Cesarini and colleagues,16 who also reported
an independent replication based on a US sample of twins. Finally, Cesarini and colleagues17
used an augmented sample, based on a second
round of data collection, in their study of giving
and risk aversion. The new evidence on hyperfair preferences reported here is based on the
augmented sample. Details on the representativeness of the sample are provided in Cesarini
et al.17
Results and Discussion
In Table 1, we summarize MZ and DZ correlations for the experiments described in the
Cesarini et al.: Game Theory and Behavior Genetics
TABLE 1. Reported Twin Correlations in Economic
Games
Variable
MZ
Corr
DZ
Corr
Ultimatum Responder
Ultimatum “Hyperfair”
Trust Investment
Trustworthiness
Risk
Giving
0.39
0.29
0.19
0.27
0.22
0.32
−0.04
0.23
−0.04
0.12
0.03
0.11
# MZ # DZ
pairs pairs
253
320
536
536
307
319
71
139
146
146
135
141
Ultimatum Game Responder Behavior is reported in
Wallace et al.15 Ultimatum “Hyperfair” results are based
on the same sample that Cesarini et al.17 used. Trust Investment and Trustworthiness are weighted averages of
the correlations for the US and Swedish samples used by
Cesarini et al.16 Risk and Giving are taken from Cesarini
et al.17 All reported correlations are non-parametric. MZ,
monzygotic; DZ, dizygotic.
previous section. With the exception of the results on hyperfair preferences, these results have
been previously reported in Cesarini et al.16,17
and Wallace et al.17 Because the results in Cesarini et al.16 were based on a US sample of
twins and a Swedish sample of twins, the correlations for trust and trustworthiness reported
in Table 1 are a weighted average of the correlations in the two samples.
Examining Table 1, there is a sense in which
these results are not too surprising; the correlations match Turkheimer’s three laws closely and
are qualitatively similar to previously reported
estimates for personality and intelligence.22 MZ
correlations are always higher than DZ correlations, usually significantly so. The MZ and
DZ correlations are lower than what is found
in studies of personality, however, suggesting
that more variation is a result of the E term in
one-shot economic games. Finally, as is usually
found in twin studies, genetic influences seem
to be a more important source of variation than
shared environmental influences.
A number of points are worth emphasizing about these results. First, as is often the
case, the results point to genetic differences as
a source of phenotypic variation. This is an important point for experimental economists to
embrace because it has important methodolog-
71
ical implications, for example, in terms of how
parent–offspring correlations are interpreted.
Surveying the ultimatum and dictator games,
Camerer3 notes that few demographic effects
have proven to be large or replicable and proceeds to conclude that demographic variables
generally have weak effects. One interpretation of this finding is that genetic variance is
only imperfectly proxied by the environmental
variables that have thus far been considered.
Another interesting point to note is that in
some cases the DZ correlations are close to
zero and less than half of the MZ correlations.
This may be because of sampling variation. Alternatively, it could be an indication of model
misspecification. The basic behavior genetic
decomposition assumes genetic additivity. An
implication of the basic ACE model is that the
DZ correlation is at least half the MZ correlation. Although this assumption cannot be formally rejected for any of the outcome variables,
the consistently low DZ correlations may be an
indication of dominance or epistasis. This is
perhaps not too surprising given the very convincing evidence of nonadditive genetic variation in personality.24 A very large sample, ideally comprising several different sibling types
with variation in genetic relatedness and rearing status, would be needed to separately identify and precisely estimate additive and nonadditive effects. Alternatively, the issue may
ultimately be resolved using molecular genetic
techniques, which explicitly test the additivity
assumption. Several studies on the molecular
genetic basis of behavior in economic games
are currently underway and some have already been published. Knafo and colleagues,37
in the first study on the molecular genetic basis of behavior in an economic game, demonstrated a polymorphism in the AVPR1a gene
that is associated with generosity in the dictator game.23 A recent paper by Dreber and
colleagues38 reports that carriers of the 7R allele on the DRD4 gene take greater risks in
a laboratory task of financial risk taking developed by Gneezy and Potter.39 These studies are both based on modest samples, and
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Annals of the New York Academy of Sciences
TABLE 2. Maximum Likelihood Estimates (Ordered Probit) of the Structural Equation ACE Model and Its
Submodels
P-value
Chi-square of model
Model
value
fit
RMSE
ACE
AE
CE
E
<0.001 0.38 (0.30–0.46) 0.17 (0.00–0.56) 0.28 (0.00–0.52)
<0.001 0.38 (0.30–0.46) 0.47 (0.34–0.59)
<0.001 0.38 (0.30–0.46)
0.42 (0.31–0.53)
0.13 0.38 (0.31–0. 45)
-
1.12
2.26
1.50
34.29
0.76
0.69
0.83
0.00
Threshold
A (genetic
contribution)
C (common
environmental
contribution)
E (unique
environmental
contribution)
0.55 (0.43–0.69)
0.53 (0.42–0.66)
0.58 (0.47–0.69)
1.00 (1.00–1.00)
95% confidence intervals in parentheses. The genetic contribution (A) is estimated at 0.17 and is not significantly
different from zero. The common environmental contribution (C) has a point estimate of 0.28. The sample size is 918
individuals (320 MZ pairs and 139 DZ pairs). The unique environmental contribution (E) is estimated at 0.55.
replications will be necessary before any definitive conclusions can be drawn.
A second point to observe regarding these
findings is that the MZ correlations tend to be
small in magnitude. However, without retest
correlations, it is not clear whether this is
because of large idiosyncratic environmental
forces operating or alternatively that the measures themselves are very noisy. Cesarini and
colleagues have found that, for the class of
games surveyed here, retest correlations are
usually in the range 0.3 to 0.6. There are several possible interpretations of these low retest
correlations. At a general level, it should be emphasized that these games were not designed
to ensure that reliability meets the usual standards of psychometric research. Instead, experimental economists are often more interested in
studying how some experimental manipulation
shifts average behavior. Yet, if one thinks of behavioral experiments as attempting to measure
some constant underlying trait, the fact that
there is little temporal stability in individual
behavior is a serious problem, suggesting that
measurement error corrections are in order.
Regardless of one’s favored interpretation of
the poor temporal stability, the implication for
association studies are obvious and important.
In Table 2, we report the standard ACE
decomposition for the hyperfair variable. The
point estimates suggest a role for genetic variation with an estimate of 17%, and, somewhat surprisingly, the estimated common environmental component is actually larger (0.28).
Neither estimate is statistically significant, however. Given the moderate sample size and the
difficulty of interpreting these rejections,40 we
are reluctant to draw any strong conclusions
based on this finding alone. The estimated heritability is broadly in line with what has previously been reported,15–17 but precision is fairly
poor.
We have indicated some of the implications
of these results for the current efforts to document the molecular genetic basis for these traits.
Social scientists are, however, confronted with
a different set of issues. The first is how new
findings from molecular and behavior genetics
can ultimately lead to a better economic science.41 The results surveyed here demonstrate
that economic phenotypes are no different from
other outcome variables that have been studied by behavior geneticists in the last decades
(for example, personality, intelligence, and disease), suggesting that many of the lessons from
the classical phenotypes studied by behavior
geneticists carry over to economic outcomes.
The same is true for the complex traits studied
by political scientists. A recent surge of interest
in this area has produced evidence of genetic
variation in political attitudes,42 voting,43 and
partisan attachment.44 Indeed, some tentative
steps have even been taken toward exploring
the specific genes involved.45
One specific venue for future research is to
examine the role of the genetic inheritance of
economic preferences for the intergenerational
transmission of economic opportunity. With
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Cesarini et al.: Game Theory and Behavior Genetics
better measured preferences and more precise
estimates of additive variation, the role of genetically inherited preferences for the intergenerational transmission of economic status could be
explored.46 This is an area of research that has
been hindered by data availability. It is known
that, although a number of social class indicators are heritable, the parent–child association
is, in part, a result of the genetic inheritance
of traits relevant for the determination of socioeconomic status. Yet, only a modest share of
this heritability can be accounted for by genetic
differences in intelligence,46 suggesting that the
genetic transmission of other traits and skills
that have yet to be identified is important.
Conclusion
Economics, along with many other social
sciences, is currently undergoing a reorientation. The results summarized here show that
behavior in a number of economic experiments, whose influence extends well beyond
economics, is heritable. The challenge now is
to find the specific genetic variants that account for this heritability. Here, we have emphasized two implications for the efforts that
are currently underway to find these genetic
variants.
First, there is some tentative evidence of
dominance, with fairly low DZ correlations
for most of the studied games. This is an intriguing finding, which, if it survives replication, suggests that scholars working with molecular genetic data should, whenever possible,
test for dominance and epistatic effects. It also
suggests that extended family studies, which
incorporate more types of relatives than just
twins, should be conducted so that models that
rely on fewer identifying assumptions can be
estimated.
Second, power calculations should not be
premised on the assumption that behavior in
these games exhibits a temporal stability similar to, say, the scales from the big five personality inventory or IQ. Both the MZ and DZ
correlations are very low compared to what is
observed in standard studies of personality. A
corollary of this finding is that it is probably
advisable to administer multiple rounds of the
experiments to the same subject.
In the next few years, more work with behavior genetic techniques using paradigms from
experimental economics will no doubt emerge.
We would be surprised if Turkheimer’s three
laws are not confirmed for any of the central
preference parameters that are of interest to
economists. With better measured preferences,
we also anticipate that behavior genetic techniques will also be useful in shedding light on
the role played by the genetic inheritance of
“noncognitive” traits in the transmission of economic inequality across generations.46
If the findings on the ubiquity of genetic influence on economic preferences are brought
into mainstream economics, a process that is
currently on its way in political science,47 the
ramifications will likely be broad in scope, influencing how economists think about human
behavior in a number of domains. In this paper, we have discussed some areas where behavior genetics may be useful. For instance, with
very few exceptions, scholars in economics have
been reluctant to entertain the hypothesis that
much individual variation can ultimately be
traced to genetic differences. This has led to an
almost singular preoccupation on various crude
environmental proxies in studies of individual
variation. Studies, such as those surveyed here,
when considered jointly provide fairly strong
evidence of genetic variation in key economic
preferences.
Conflicts of Interest
The authors declare no conflicts of interest.
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