Biol Theory
DOI 10.1007/s13752-013-0108-0
THEMATIC ISSUE ARTICLE: STRATEGIC INTERACTION
Genes, Culture, and Preferences
Nikolaus Robalino • Arthur J. Robson
Received: 7 June 2012 / Accepted: 9 October 2012
Ó Konrad Lorenz Institute for Evolution and Cognition Research 2013
Abstract This paper explores the notion that economic
preferences were shaped by the joint action of genetic and
cultural evolution. We review the evidence that preferences
are partly innate, the output of genetic forces, and partly
plastic, the output of cultural forces. A model of how genes
and culture might jointly shape preferences is sketched.
Keywords Cultural evolution Genetic evolution Preferences
Introduction
We advocate in this paper the view that economic preferences were shaped by the joint action of genetic and cultural evolution. We argue that references are partly innate,
the output of genetic forces, and partly plastic, the output of
cultural forces; but that the very plasticity of the culturally
determined component of preferences was shaped
genetically.
Such a view of the origins of preferences—and of economic behavior, more generally—has profound implications. Consider savings behavior as a case in point. How
much of an individual’s time preference, as reflected in a
propensity to save, is determined by enculturation? How
much of it is innate? To the extent that time preference is
plastic and enculturated, a policy that encouraged savings
and eliminated market barriers might be successful in
N. Robalino A. J. Robson (&)
Department of Economics, Simon Fraser University,
8888 University Drive, Burnaby, BC V5A 1S6, Canada
e-mail: [email protected]
N. Robalino
e-mail: [email protected]
fostering greater long-run equality. At the same time, the
extent to which time preferences are innate and heritable
would set limits on the success of such a policy. Relatedly,
we might ask: To what extent are our risk attitudes learned?
To what extent are they genetically inherited from our
parents? To the extent that the greater risk aversion displayed by women, for example, is plastic and enculturated,
it would be possible to reduce this, assuming this were
considered desirable.1
Consider further the enculturated component of preferences. How exactly is this molded by culture? How much
influence can be attributed to parents, and to close relatives? How much to peers? To prominent members of
society? When are preferences most malleable? Is there a
stage in an agent’s life history when they are effectively
immutable? Answers to such questions clearly have serious
implications for the execution of policy goals.
More basically, the mutable nature of preferences
necessitates a fundamental reevaluation of welfare—of the
individual and therefore a fortiori of society. If preferences
can change, how should the notion of Pareto efficiency be
interpreted? With respect, that is, to which set of preferences? A convincing model of the joint action of genetics
and culture, with the operation of culture itself subject to
natural selection, holds out the hope of permitting the
reincarnation of a basis for welfare judgements.
We first review the empirical literature on genetics and
culture in relation to economic behavior. Much of the early
work in this area has focused on the Nature-Nurture
question as it pertains to economic outcomes, such as
income, wealth, educational attainment, and so on.
Recently, however, attempts have been made to understand
1
See Croson and Gneezy (2009) for evidence on the greater risk
aversion of women.
123
N. Robalino, A. J. Robson
how genetics might determine economically relevant
characteristics, such as attitudes to risk, time preference,
and trust attitudes.
One approach, within this literature, in particular, is to
decompose the variation in characteristics into contributions from genetic and environmental variation. This work
has generally been very successful in demonstrating that
both genetics and culture matter, but it also illuminates
critical gaps in our current understanding. In particular, a
description of how genetics and environment might interact
has remained elusive—by and large, research has proceeded under the highly questionable assumption that
genetics and environment are independent. The time is ripe
then, for a theoretical framework that would model this
interaction and provide a basis for better informed empirical work. After reviewing the relevant literature in economics we proceed to outline the elements such a coevolutionary analysis of preferences might require.
The Literature from Economics
The Classical Nature-Nurture Approach
Most of the relevant empirical literature within economics
examines the roles of genetics and childhood environment
in adult economic characteristics or outcomes. The point of
departure there is the benchmark twin and adoptee
approach developed by behavioral geneticists, building on
the classical work of Fisher (1918) .2 The aim of this work
is to estimate the relative contributions of genetics and
environment to the variance in some measurable outcome,
such as IQ, income, and so on.
The behavioral genetics framework is now outlined.
Fisher (1918) assumes that a person’s phenotype is jointly
determined by a genotype and the environment.3 For our
purposes, the environment determines the information this
person is given. This information is transmitted through
two mechanisms: (1) social learning (culture), and (2)
individual experience.4 In the following we give a brief
description of Fisher’s model.
Phenotypes (a trait, such as IQ, degree of risk aversion,
or an economic outcome such as years of education),
genotypes and environments are described as values
Y, G and E, respectively. A structural model,
Y ¼ G þ E;
is assumed. That is, genetic endowments and environments
additively determine an individual’s phenotype. The
variance of the observed phenotype can then be
decomposed into the variance of genotypes and the
variance of the environment, i.e.,
r2Y ¼ r2G þ r2E þ 2 r2GE :
ð1Þ
The covariance between the phenotypes of two
individuals, on the other hand, is given by
r2YY 0 ¼ r2GG0 þ r2EE0 þ r2EG0 þ r2E0 G :
With this last expression, data in adoptive and identical
twin siblings can be used in order to estimate the quantities
in (1). For instance, for adoptive siblings
r2YY 0 ¼ r2EE0 þ r2EG0 þ r2E0 G ;
under the assumption that adoptive siblings’ genotypes are
uncorrelated within the population of interest. Assuming
further that adoptive siblings share the same environments
and that these are uncorrelated to the children’s genotypes5
yields
r2E ¼ r2YY 0 :
That is, the share of phenotype variance that can be
attributed to environmental variance can be estimated with
the sample covariance in observed phenotypes among pairs
of adoptive siblings. For identical twins, since they are
genetically identical (invoking the same assumptions as
previously),
r2YY 0 ¼ r2G þ r2E ¼) r2G ¼ r2YY 0 r2YY 0 jadoptive :
That is, the ‘‘heritability’’ of the observed phenotype is
the covariance of phenotypes among identical twins less
the covariance among adoptive siblings.
The assumption that r2EG ¼ r2E0 G ¼ r2EG0 ¼ 0 admits a
clean decomposition of the variance in phenotypes. Such
an assumption, however, appears unreasonable. Furthermore, aside from making a distinction between shared and
non-shared environment (i.e., family, neighborhood characteristics, and so on, versus experiences that siblings
reared in the same household do not share), the above literature gives little insight into the precise nature of the
‘‘Nurture’’ mechanisms.
2
The bulk of the behavioral genetics literature attempts to decompose IQ variance into genetic and environmental influences. See, for
instance, the reviews by Goldberger (1979), Devlin et al. (1997), and
Plomin and Spinath (2004).
3
See Behrman and Taubman (1989) and also Sacerdote (2011).
4
Of course, the environment’s influence is not limited to information—i.e., the quality of food consumed, exposure to toxins, and so
on, all may potentially affect phenotypes.
123
5
This latter requirement can be problematic. It is reasonable if
environment is measured by parents’ income. It will not hold if
parents invest differentially in their children according to perceived
innate abilities, for example. The assumption that siblings’ environments are perfectly shared is also questionable.
Genes, Culture, and Preferences
The Classical Approach as Applied to Economics
Empirical Work Exploiting Random Adoption
We turn now to examining key papers taking a behavioral
genetics approach to economics. Behrman and Taubman
(1989) is a landmark paper that focused on educational
outcomes. Their model extends the R.A. Fisher model by
permitting the environments of relatives reared in different
households to be correlated. Environment is represented by
the father’s occupation and the subject’s number of siblings. They estimate Fisher’s model for a variety of kin
groups related through a sample of male twins to find that
genetics explains most of the variance in years of schooling
attained. (Table 3 in the paper reports a ratio of genotypic
variance to phenotypic variance of 0.88.)
Cronqvist and Siegal (2011) use data on fraternal and
identical twins to explain savings behavior. They decompose savings behavior of the twins into unobserved genetic,
shared environmental, and idiosyncratic environmental
components. Under the assumption that these are independent, their data suggests that approximately 1/3 of the
variation in savings behavior can be explained by genetics
and that most of the remaining variation is due to individual, as opposed to shared, environment. Interestingly,
they find that the influence of genetics is stronger in high
socioeconomic status environments. One way to interpret
this result is that the affluence of these families permits
greater variation in the cultural norms about saving that are
transmitted.
Barnea et al. (2010) find that approximately one third of
the variation in investor’s risk behavior—e.g., stock market
participation and asset allocation—is explained by genetic
factors. Here, as in the previous study, most of the
remaining variation is due to non-shared environment. The
empirical design is as in Cronqvist and Siegal (2011). This
paper suggested also that family environment has a transitory effect on investor behavior—disappearing, in particular, as individuals accumulate experience.
Cesarini et al. (2009) provide estimates of genetic and
environmental determinants of experimentally elicited risk
preferences. The subjects consist of fraternal and identical
twin pairs. The variance in phenotypes is decomposed into
genetic, shared, and non-shared environments. As above,
these influences are assumed to be independent from one
another. Cesarini et al. find strong evidence that preferences for risk taking are heritable, suggesting that genetic
differences explain approximately 20 percent of the variation is risk-taking behavior. Common environment plays a
modest role. Cesarini et al. (2010) uses the same design as
Cesarini et al. (2009). The observed variables are individual investment decisions rather than experimentally elicited
risk preferences as in the previous study. The data suggests
that approximately 25 percent of variation in portfolio risk
can be explained by genetic differences.
A distinct empirical approach is to identify intergenerational transmission coefficients by regressing the outcomes
of children on those of their parents. Adoption data is then
used to disentangle the effects of family environment from
the effects of genetic endowments. Adoption here generates a natural experiment that makes genetic variation
independent of the environment, given that adoptees are
randomly assigned to families. This literature finds a
greater role for shared environment. The most likely
explanation for this difference is that by availing itself of
the natural experiment of random adoption, this second
literature does not need to impose structure on the interplay
between genetics and environment.
Plug and Vijverberg (2003) use years of schooling in
order to study the transmission of cognitive ability from
parents to offspring. Parents’ IQ is used as an explanatory
variable for the schooling success of adopted and nonadopted children. They conclude that at least 50 % of
schooling-related ability is passed on genetically.
Björklund et al. (2006) study the transmission of earnings and education. They use a data set on adoptees that
contains information on the characteristics of both their
biological and adoptive parents. Biological parents determine prebirth factors, such as genes and the prenatal
environment, while the adoptive family determines the
postbirth environment. The phenotypes of the adoptees are
then regressed on the phenotypes of their biological and
adoptive parents (fathers and mothers). Both the prebirth
factors and the postbirth environment are found to be
important for income and education outcomes. For
instance, in the case of transmissions from fathers to
children, the biological and adoptive fathers’ phenotypes
appear roughly equally significant for schooling outcomes.
However, biological mothers’ phenotypes are at least twice
as important as adoptive mothers’ phenotypes.6
Sacerdote (2002, 2007) study educational and labor
market outcomes using data from adoptees that are randomly
assigned to families. Random assortment implies the genetic
endowments of the child are orthogonal to the income and
socioeconomic status of the adoptive families. His results
suggest that the socioeconomic status of the adoptive family
has a significant positive impact on whether the child attends
college, or not, and on the selectivity of the college attended.
A Direct Approach to the Genetics of Economic
Characteristics
A promising new strand in the literature directly examines
the effects of genes on economic characteristics. Rather
6
See Table II on page 1013 of the paper for these results.
123
N. Robalino, A. J. Robson
than looking from the top down, by examining the characteristics exhibited by relatives in varying environments,
this strand looks from the bottom up for precise sets of
genetic loci that underpin the characteristic in question. For
example, Carpenter et al. (2011) show that dopamine
receptor genes predict risk preferences.7 Benjamin et al.
(2012) utilize a large sample, but conclude that an even
larger sample may be needed to address the statistical
difficulties. The data and statistical issues involved here are
prodigious. With only very vague priors about how most of
these genes operate, the exercise, of necessity, is also a
specification search. Since the data must simultaneously
identify the sets of loci that are involved, statistical tests of
significance are adversely affected.
Theoretical Perspectives on the Biological Basis
of Preferences
A theoretical literature on the biological basis of economics
has developed in parallel with the empirical literature
sketched above. Since this literature does not so far consider the relationship of innate and enculturated preferences, we present only a brief sketch. We will also focus
attention on two strands—papers that consider the basis of
attitudes to risk and those that consider the basis of time
preference.8
Robson (1996) considered the evolution of attitudes to
risk. The model involved a single age class, but where there
was both idiosyncratic (independent) and aggregate
(shared) risk. If all the risk involved is idiosyncratic, then it
is straightforward to obtain the expected utility theorem.
When some of the risk is aggregate, on the other hand, the
preferences favored by natural selection do not even satisfy
‘‘probabilistic sophistication,‘‘ since individuals do not care
only about the outcomes they may experience and the
probabilities with which these outcomes occur. The preferences selected embody a particular type of negative
interdependence across individuals.9 Individuals strictly
prefer idiosyncratic risk over precisely comparable aggregate risk.
Rogers (1994) is a path-breaking paper that attempts to
derive time preference from evolutionary biology. The crux
of the argument is as follows. Consider a 25-year-old
7
For a perspective from molecular biology see Kuhnen and Chiao
(2009). For a discussion within economics see Beauchamp et al.
(2011).
8
Robson (2001) considers the still more basic question: What is the
evolutionary reason for utility functions in the first place? Robson
(2001) is an early survey and prospectus of this theoretical literature
and related empirical work outside economics. Robson and Samuelson (2011) is a more recent survey focusing on the evolutionary basis
of preferences.
9
See Curry (2001) for the interpretation of these preferences.
123
woman who saves resources for her daughter who is
newborn now to be given to this daughter 25 years from
now when she will also be 25. This construction means that
an unknown function will cancel out from the first-order
condition. The only factor that is left is 1/2 representing the
attenuated interest the current mother has in her daughter
given sexual reproduction. This generates a pure rate of
time preference of around 2 %. There are various problems
with this argument that were pointed out by Robson and
Szentes (2008). A key one is that repeating this argument
with a 20-year-old or a 30-year-old would imply different
rates of time preference. That is, all but one of these possible transfers must involve corner solutions. Robson and
Szentes (2012) reconsider this issue in a somewhat different but more tractable model than that used by Rogers.
They find that sexual reproduction might even reduce
impatience.
Finally, Robson and Samuelson (2009) consider the
evolution of time preference in the presence of aggregate
uncertainty, bringing together the two strands. They find
that the presence of aggregate uncertainty effectively raises
the implied rate of pure time preference. This is desirable
since a simple model with only idiosyncratic risk implies a
pure rate of time preference that is the sum of the rate of
mortality and the rate of population growth, and this sum
seems to fall short of plausible estimates of the pure rate of
time preference.10
An exciting challenge is to integrate more closely the
empirical and the theoretical literature. It is towards this
goal that we sketch a model of the biological basis for
innate and enculturated preferences.
Mechanisms for Cultural Transmission of Preferences
Models of the cultural transmission of preferences have
been advanced in economics. These consider a rich variety
of enculturating mechanisms and channels. Although they
do not rule out a role for genetics, it is often only considered implicitly. Two key papers in this literature are
Bisin and Verdier (1998) and (2000). A feature of the
model they develop concerns the maintenance of culture
within a society with subgroups of varying sizes. Parents
care about the preferences of their children, but inculcation
of children is costly. A minority is more liable to the
dilution of its culture, since minority children are likely to
acquire the culture of the majority. Minority parents
therefore have a stronger incentive to inculcate their children, which is a force tending to stabilize the size of the
minority.
10
See also Robson and Samuelson (2007).
Genes, Culture, and Preferences
another allows incremental adaptations toward the
appropriate phenotype through a process of experimentation. Through such a process of trial and error,
by steadily improving on previous knowledge, environmentally optimal phenotypes can emerge without
individuals understanding the causal mechanisms
behind these optimal responses. See Boyd et al.
(2011).
Evolution of Culture
We define culture as social transmitted information that has
a lasting effect on an individual’s behavioral phenotype
(Cavalli-Szforza and Feldman 1981; Boyd and Richerson
1985). This excludes from consideration information
resulting merely in transient behavioral responses—e.g., a
shout of ‘‘fire’’ in a crowded room. An individual can
acquire cultural information from her parents, elder members of her clan (vertical transmission), or her peers (horizontal transmission). The essential feature of culture is
social learning that results in the non-genetic transfer of
skill, thought, and feeling from person to person (Boyd and
Richerson 1985, pg. 35). There are several channels
through which these transfers might occur—by simple
imitation, by the transmission of beliefs (the probabilities
people assign to various outcomes), or by altering preferences directly.
What are the Evolutionarily Relevant Features
of Culture?
We take our capacity for culture as determined by biological evolution. In this sense our approach is ultimately
biological. However, an important question here is why
culture? More precisely, why would Nature design an
organism with proclivities that can be molded through
enculturation? Another way of posing the question is to
ask: In what types of environments do flexible preferences
yield an evolutionary advantage over a rigid alternative?
This is perhaps the first question we should address in
developing our coevolutionary approach.
The following observations serve as a guide.
C1 Cultural evolution can operate on a faster scale than
genetic evolution. An individual’s cultural parents can
be different from her genetic parents. A person can be
a cultural parent to their genetic offspring but to many
others as well. Thus culturally transmitted information can more rapidly spread throughout a population
than genetic information. Additionally, an individuals’ phenotype can change throughout her life history.
In this manner cultural selection might operate on an
individual’s phenotype various times while the person’s genotype is fixed.
C2 Guided variation. Individuals can select which
behaviors to imitate (or parents select which behaviors to reinforce) through a process of rational
calculation. As a result, acquired phenotypes might
better be adapted to the environment (Durham 1992).
C3 Cumulative culture. Cultural transmission allows
societies to solve problems that no individual, or
group of individuals, can solve at a point in time. The
transmission of information from one individual to
Culture allows Nature to overcome information
problems.
C4
C5
There are limits on the accuracy of information that
Mother Nature can transmit genetically. Nature might
not be able to program aversions or proclivities to
some stimuli. In particular it not might be able to
condition a utility function to be responsive to certain
changes, or to very small changes in input.
Society has information that the individual does not.
The individual has information that Nature does not.
But members of previous generations, or betterinformed peers, might have other information not
available to the agent.
Towards a Model of Innate and Enculturated
Preferences
Consider preferences over a variable x which we take to be
wealth, for specificity. The fitness consequences of a given
value of x depend on the state s. From an a priori point of
view, this state is random, with a pdf for the prior distribution
given as f(s). The fitness consequences of a particular state s
and wealth level x are given by the fitness function / (s, x).
Innate preferences are then given by the expected fitness
deriving from wealth x and the a priori state distribution. That
R
is, UðxÞ ¼ /ðs; xÞf ðsÞds. The function U will be the
appropriate von Neumann Morgenstern utility function to
assess gambles over x. That is, if a gamble over x is given by
the pdf p(x), then the expected fitness from choosing p is
R
R
R
clearly pðxÞ /ðs; xÞf ðsÞdsdx ¼ UðxÞpðxÞdx.
However, society knows more about the distribution
(see C5–6 in the previous section). Perhaps an informative
signal v is observed by some small set of individuals. It can
then advantageously be transmitted by enculturation to
everyone else. Assume that the pdf for the posterior distribution is given by g(s|v). That is, the enculturated prefR
erences are then given as VðxÞ ¼ /ðs; xÞgðsjvÞds. This
again is again the appropriate von Neumann Morgenstern
utility function to assess gambles over x. That is, if a
gamble over x is given by the pdf p(x),
R thenR the expected
fitness from choosing p is clearly pðxÞ /ðs; xÞgðsjvÞ
R
dsdx ¼ vðxÞpðxÞdx.
123
N. Robalino, A. J. Robson
Note that deep preferences never change with enculturation here—these are always described by the fitness
function /. Rather what changes is the information that is
available about the statistical link between wealth x and
fitness which is induced by the random state s. However,
the effect would be indistinguishable from a shift in
observable preferences.
The conventional stance on decision making under
uncertainty is that preferences should be defined over final
outcomes. These outcomes are usually thought of as being
deterministic, but what really matters is that any stochasticity is invariant. Wealth is often taken to be a plausible
example of a suitable such outcome. We are not adhering
to this stance here. Indeed, it seems plausible that the link
between wealth and evolutionarily success is stochastic and
subject to updating by new information.
It is also worth noting that this approach permits
unambiguous welfare judgments, despite the presence of
different utility functions. That is, it is clear that ‘‘right’’
utility function incorporates any additional information.
That is, the ‘‘right’’ utility function is V(x) rather than U(x),
given the signal v.
An Example
This example is motivated by the desire to obtain simple
closed-form solutions, rather than a desire for generality.
Suppose that s* N(l,r2), where the variance r2 is given,
but the mean l is random, with l* N(l0,r20) as the prior
distribution. The signal v is taken to be a draw from the
combined distribution just described.11 The theory of
‘‘conjugate priors’’ (see de Groot 1970, for example)
implies that the posterior distribution of l is given as
0
l Nðl00 ; r20 Þ, where
l00
¼
l0
r20
1
r20
þ rv2
þ
1
r2
and
0
r20 ¼
1
r20
1
þ r12
ð2Þ
The prior pdf f as defined for the general case is the pdf
for the prior combined distribution. It follows readily that f
is the pdf for N(l0,r20 ? r2). The posterior pdf g is the pdf
for the posterior combined distribution. Similarly, it
0
follows that g is the pdf of Nðl00 ; r20 þ r2 Þ, where l00 and
0
r20 are given by (2).
Suppose now that the fitness function is quadratic given
by /(x,s) = - (s - x)2.12 It is straightforward to evaluate
11
It is straightforward to allow the signal to be multiple draws from
this combined distribution.
12
This is unrealistic since it allows marginal fitness to be negative
when x [ s, as must be the case for any fixed x if s is small enough.
Since s is normally distributed, this possibility always exists.
However, if the mean of s is large and positive, this possibility will
be very unlikely, and is ignored for the present expository purpose.
123
the expectation of such a quadratic utility under a normal
distribution. It follows that
Z
UðxÞ ¼ /ðx; sÞf ðsÞds ¼ ðl0 xÞ2 r2 r20
and
VðxÞ ¼
Z
0
/ðx; sÞgðsjvÞds ¼ ðl00 xÞ2 r2 r20
using (2). Thus the effect of the signal is simply to shift the
constant that appears in the quadratic term of the prior
utility function.13 Indeed l00 given by (2) is linearly
increasing in v, and l00 [ l0 if and only if v [ l0.14
Thus innate and enculturated preferences belong to a
particular family of quadratic preferences. The effect of
enculturation is limited to a particular shift in the parameter
in the quadratic term. However, this shift is observationally
meaningful, since risk aversion varies with this parameter.
We have, in particular, that the coefficients of absolute and
relative risk-aversion are, respectively, rA ðxÞ ¼ l0 1x [ 0
0
and rR ðxÞ ¼ l0 xx [ 0, if l00 x [ 0. Thus both rA (x) and
0
rR (x) are decreasing in the signal v for every fixed x as long
as x\l00 always.
It would be an econometric issue to recover the
parameters l0 and l00 . Given these, however, we have that
l00 ¼ l0 þ
vl0
r2
1
r2
þ r12
:
0
That is, the enculturated parameter, l00 , equals the innate
parameter, l0, plus an increment that is linear in the signal v.
Conclusions
This paper begins by presenting a survey of empirical work
that bears on the causal link between genetics and economic behavior and the underlying economic characteristics. We looked first at the classical approach due originally
to Fisher (1918) that aims to disentangle the contributions
of ‘‘Nature‘‘ and ‘‘Nurture.’’ In particular, we looked at
13
Although the additive constant term is also affected by the signal,
this irrelevant to choice, of course.
14
The expected utility for these utility functions can be expressed in
terms of the mean and the variance of the distribution. Consider a
gamble over x given by the pdf p(x). Suppose the mean of this gamble
is p and the variance is v(p). It follows readily that
Z
pðxÞUðxÞdx ¼ ðl0 pÞ2 vðpÞ r2 r20
and
Z
0
pðxÞVðxÞdx ¼ ðl00 pÞ2 vðpÞ r2 r20 :
Genes, Culture, and Preferences
several papers that use data on fraternal and identical twins.
These data permit the variance in attitudes to risk or the
rate of time preference to be decomposed into a component
due to genetics and another due to the environment. Both
components are sizeable and significant statistically.
We next considered several papers that demonstrated the
contribution of culture by considering the natural experiment of adoptees who were essentially assigned at random
to their new families. At the other extreme, we also
examined a literature that tries to demonstrate an effect of
genes directly by looking for sets of genetic loci that affect
economic characteristics of interest.
There is a theoretical literature on the biological basis of
preferences that has so far grown without much interaction
with the empirical work. We looked briefly at a few of
these papers concerning attitudes to risk and time preference. We sketched theoretical and empirical work on
mechanisms of cultural transmission—work that considers,
for example, a parental role and a role for peers as
alternatives.
We then briefly reviewed the largely theoretical work
that has been done on the evolutionary underpinnings of
culture. Finally, we sketched a model of the evolutionary
basis for innate and enculturated attitudes to risk.
Acknowledgements Robalino and Robson acknowledge financial
support from the Human Evolutionary Studies Program at Simon
Fraser University; Robson also acknowledges that of a Canada
Research Chair.
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