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Artificial Life and the simulation of culture
Domenico Parisi
Institute of Psychology
National Research Council
parisi@ip.rm.cnr.it
1. Culture
Humans are unlike other animals in that most of their behavior has a social origin. Nonhuman animals
behave in ways that are mostly genetically inherited or are learned in the course of an individual’s
interactions with the nonsocial environment. In contrast, most of what an individual human being does,
knows, or wants has been learned from other individuals or from the artifacts made by other individuals.
Behavior which is learned from others is called cultural behavior (Boyd and Richerson, 1985; Boesch, 1996).
If a group of people live in such conditions that they tend to learn their behaviors from each other, these
people will share the same behaviors. A set of behaviors shared by a group of people because these people
have learned the behaviors from each other is called a culture. When the conditions for learning from each
other are not met, for example because groups of people live far from each other and without contacts, each
group of people will have its own culture which will be different from the culture of the other groups.
A behavior which individual A learns from individual B can be said to be transmitted from B to A. Cultural
transmission has some analogies with biological transmission. In biological transmission genetic information
is encoded in the DNA and is acquired by individual A from individual B as a result of biological
reproduction. In cultural transmission individual A acquires some behavior from individual B because A
imitates B or is taught or persuaded by B or interacts with artifacts made by B. The analogy between biology
and culture goes beyond transmission and it extends to evolution, that is, to change over time in genetic or
cultural information. In fact, biology and culture appear to be both instances of a more abstract and general
process of evolution which results from the selective transmission of variants in a population of variants and
the constant addition of new variants (Dennett, 1996). The parallels are as follows. In biological evolution
the population of variants is the set of individual genotypes together with the set of phenotypes that result
from these genotypes through the process of individual development. In cultural evolution the population of
variants is the set of different behaviors exibited by a group of individuals and learned from each other.
Selective transmission in biology consists in the fact that some individuals have more offspring than other
individuals so that some individual genotypes leave more copies and become more frequent in the population
than other individual genotypes. In culture some behaviors are learned or reproduced by more individuals
than other behaviors and therefore become more frequent in the culture. In biological evolution new variants
are added to the genetic pool of a species through genetic mutations, which are chance modifications of the
inherited genetic material, and through sexual recombination, which creates new genotypes that are new
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combinations of portions of the genotype of one parent with portions of the genotype of the other parent. In
cultural evolution new variants are added to the cultural pool of a culture because of “errors” of transmission
analogous to genetic mutations, because new behaviors are created or invented by recombining in new ways
some aspects of already existing behaviors, and because of imports from other cultures. The selective
reproduction of variants and the constant addition of new variants are what causes both biological evolution
and cultural evolution.
Given the analogies between biological and cultural evolution it seems to be appropriate and fruitful to use
the same theoretical framework to model both types of evolution. However, biological and cultural evolution
have both commonalities and differences. Therefore, one should be careful not to look for traits that belong
only to biological evolution in cultural evolution, and viceversa. A different issue is the study of interactions
that may exist between biological and cultural evolution as empirical processes which are both found in
humans and which may influence each other, now or in the past. For example, if one asks why is it that
humans are the only animals that have culture (at least in a well developed form), a possibile answer is that
humans have a species-specific biologically evolved genetic basis for culture. Then, an important research
goal is to describe this genetic basis for culture.
Before we proceed we would like to somewhat extend our definition of culture. We have defined culture as
the set of behaviors exibited by a group of people that have learned such behaviors from each other.
However, people learn from each other not only behaviors but also ideas, knowledge, and goals. Therefore, a
more complete definition of culture is the set of behaviors, ideas, knowledge, and goals that a group of
people learn from each other. Another extension concerns the transmission mechanism. People acquire
behaviors, ideas, etc., from other individuals in a number of different ways. They learn from others by
spontaneously imitating other individuals or by being explicitly taught by other individuals. They learn both
verbally and nonverbally. They learn by interacting directly with other people and they learn by interacting
with the artifacts produced by other people. Furthermore, much human behavior has a social origin and is
culturally transmitted simply because we are influenced by other people through persuasion, propaganda, and
advertising. A third extension of our definition of culture concerns artifacts. Humans are unique in the
animal world not only because of the social origin of most of what they do, know, and want, but also because
they create artifacts. Artifacts are modifications of the external environment that tend to make the external
environment more favourable to one’s survival and well-being. That both culture and artifacts characterize
humans is no coincidence because one could argue that the same genetic basis underlying cultural behavior
in humans also underlies the creation of artifacts by humans and because artifacts are culturally transmitted
and are a means of cultural transmission. Artifacts are culturally transmitted because the behaviors, ideas,
and knowledge that allow humans to create artifacts are culturally transmitted. They are a means of cultural
transmission because behaviors, ideas, and knowledge can be culturally acquired by an individual as the
individual interacts with the artifacts and not directly with other humans beings. Hence, our definition of
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culture should be extended to artifacts. Culture is the set of behaviors, ideas, knowledge, goals, and artifacts
that are socially transmitted in a group of human beings. Being a part of culture, artifacts evolve like
behaviors, ideas, knowledge, and goals by being selectively reproduced (copied, bought, used) and by the
constant addition of new artifacts.
2. How to model culture
Notwithstanding its importance for understanding human behavior and human societies and the many efforts
of a number of disciplines including anthropology, sociology, primatology, archeology, palethnology,
historical linguistics, and history itself, we still do not have a mature and scientifically respectable science of
culture. A mature and scientifically respectable science of culture would be a science with well-articulated
theories that identify the important entities, mechanisms, and processes underlying and explaining cultural
phenomena, their origins and evolution, and that make specific predictions that can be tested using the
empirical evidence. No such a science exists. The disciplines that in one way or another concern themselves
with culture tend to be nontheoretical, that is, to be purely descriptive and/or narrative, or to limit themselves
to questions of typology, definition of cultural and societal stages, or periodization. Moreover, the
disciplinary fragmentation is an obstacle to the identification of relationships and cross-field explanations
that are critical for a mature science. (Cf. the natural sciences where chemical facts are explained in terms of
physics and biological facts in terms of chemistry.)
Some progress has been done recently with the development of quantitative and formal models of cultural
phenomena (Cavalli-Sforza and Feldmann, 1981; Boyd and Richerson, 1985). Many of these models are
extensions to culture of quantitative and formal models used in genetics and population biology. They tend to
be mathematical models that specify how an aggregate variable is quantitatively related to one or more other
aggregated variables or how an aggregated variable changes with time. These quantitative models, although
important, have three limitations. One limitation is that they tend to apply only to a restricted range of
phenomena that have to do with culture, while many important phenomena seem to be difficult or impossible
to deal with using this type of models. Quantitative/formal models apply to popolational or social/collective
phenomena but they tend to ignore the individual at both the neural and the behavioral level. A second
limitation is that one ends up with a series of distinct models of different aspects of culture but it is more
difficult, using the quantitative/formal approach, to link up the different phenomena with one other in order
to arrive at an integrated picture of the entire range of cultural phenomenon and of their causes and
consequences. The third limitation is the most serious one. In this field as in other scientific fields, models
that try to capture the quantitative relationships among aggregate variables and to determine how aggregate
variables change with time can be said to remain at the surface of the phenomena. What is required to
understand the phenomena more deeply are models that try to identify the entities, mechanisms, and
processes that underly the observed phenomena and explain them by allowing us to see how the phenomena
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are dynamically generated by these entities, mechanisms, and processes. If successful, these models will then
explain why two or more aggregated variables are related in the way described by the purely quantitative
models and why some particular aggregate variable changes with time in the way described by these
quantitative models.
In this paper we adopt a different approach to modeling culture: Artificial Life. Artificial Life is an approach
to the study of all the phenomena of the living world that attempts to understand these phenomena by
reproducing them in a computer. Therefore, Artificial Life models are not expressed verbally or as
mathematical formulae but they are expressed as computer programs. They are simulations. Artificial Life
models are not restricted to biological phenomena. The living world exhibits both phenomena that are
properly studied by the biological sciences and, in the case of humans, also phenomena that are studied by
the cognitive and social sciences. The latter phenomena include the phenomena of culture: what makes (or
made) culture possible, how culture is transmitted, what varieties of “things” are culturally transmitted, what
mechanisms are responsible for the selective nature of cultural transmission, what mechanisms are
responsible for the addition to new variants to the cultural pool of a human group and, more generally, for
the cultural evolution in human groups.
Artificial Life models are agent-based models, that is, models that assume a set of “agents” (e.g., individual
organisms) that interact with each other and with the rest of the environment in such a way that from their
interactions global phenomena emerge that cannot generally be predicted or deduced from a knowledge of
the “agents” and of the “rules” that govern their interactions. If the “agents” are individual (human)
organisms, the biological orientation of Artificial Life induces to model their behavior as governed by a
nervous system physically contained in a body which in turn is contained in a physical environment, where
each individual organism is a member of a biologically and culturally evolving population of organisms.
An Artificial Life approach to culture has advantages with respect to both the traditional approaches and the
more recent quantitative/formal models. Since Artificial Life models are expressed as computer programs,
they force the researcher to formulate explicit and quantitative theories that make specific predictions about
empirical phenomena since the results of a simulation are the empirical predictions drawn from the theory
incorporated in the simulation. Furthermore, once a simulation has been constructed the simulation functions
as a virtual experimental laboratory in which the researcher can observe the (simulated) phenomena in
controlled conditions and can manipulate these conditions to observe the consequences of his/her
manipulations as in a real experimental laboratory. This allows the study of culture to go beyond the purely
descriptive/narrative work or the vague “theories” of the traditional disciplines and to have access to a
powerful experimental instrument that can compensate for the lack of the real experimental laboratory. But
Artificial Life models of culture can also overcome the limitations of the quantitative/formal models derived
from population genetics. Artificial Life models can apply to a wider range of cultural phenomena and to the
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interactions among the different kinds of phenomena relevant for culture at the genetic, neural, behavioral,
social, and environmental level. Furthermore, they can identify the entities, mechanisms, and processes that
underlie the observed relationships among aggregated variables described by the quantitative models.
3. Some simulations of cultural transmission and evolution
3.1 Basic model of cultural tramission and evolution
Our basic definition of culture is behavior which is learned from others. In this section we describe a simple
model of learning from others and of the evolutionary emergence of culturally transmitted behaviors. The
behavior of an individual is governed by a neural network and the neural network learns using the
backpropagation procedure (Rumelhart and McClelland, 1986). The evolutionary emergence of culture is
modeled using a genetic algorithm (Holland, 1975; Mitchell, 1998).
A population of individuals lives in an environment that contains a certain number of resource elements.
(The simulations described in this and the following paragraph were done with Daniele Denaro. Cf. Denaro
and Parisi, 1996.) The behavior of each individual is controlled by a neural network with input units
encoding the location of the nearest resource element and the output units encoding motor behavior that
allows the individual to move in the environment. When an individuals reaches a resource element, it
captures the resource element. At the beginning of the simulation the individuals’ neural networks are
assigned random connection weights. Therefore, the behavior of the individuals of the initial generation is
rather inefficient from the point of view of resource procurement. These individuals tend to move randomly
or stereotypically and, therefore, they are able to capture a very limited number of resource elements.
However, for purely random reasons some individuals happen to have better connection weights than other
individuals and these connection weights allow them to behave more efficiently, that is, to capture more
resource elements than their less lucky conspecifics.
Just before the individuals of the first generation die (length of life is identical for all individuals and all
individuals die at the same time), a second generation of individuals is created. These individuals are born
with random connection weights like the individuals of the first generation but, at the beginning of their life,
they are given a chance to learn to procure the resources by imitating the behavior of the individuals of the
first generation (Figure 1).
Figure 1. A simple model of learning by imitating another individual. Both the “learner” neural network
(left) and the“model” neural network are exposed to the same input and each responds by generating some
output. The “learner” neural network learns by using the output of the “model” network (right) as teaching
input in the backpropagation procedure. The teaching input tells the “learner” network how to modify its
connection weights in such a way that after a certain number of learning cycles the “learner” network
responds to the input in the same way as the “model” network.
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The best individuals of the first generation are selected as “models” of the individuals of the second
generation and, furthermore, when an individual of the second generation learns the behavior of resource
procurement by imitating its “model”, some noise is added to the transmission process by slightly
(randomly) modifying the teaching input. This noise has the consequence that in most cases a “learner” turns
out to be less good at procuring the resources than its “model” but in a few cases a “learner” can outperform
its “model”. Of course, the individuals that happen to perform better than their “models” are more likely to
be selected at the end of their life as the “models” of the individuals of the next generation.
The process is repeated for a certain number of generations. The capacity to procure the resources tends to
increase with each successive generation and after a certain number of generations it reaches a steady state
level which is clearly much better than the initial level (Figure 2).
Figure 2. Evolutionary increase in the ability to procure the resources present in the environment in a
population in which the ability is culturally transmitted and it evolves purely culturally. Top: best individual;
Middle: average individual; Bottom: worst individual in each generation.
3.2 Interactions between cultural and biological evolution
In the simulation just described it was the researcher who selected the best individuals of each generation as
the “models” of the next generation. In a second simulation the individuals themselves evolve an ability to
identify the best individuals of the preceding generation in order to adopt them as “models”. This ability to
select the best individuals of the preceding generation as “models” is genetically inherited. The ability is
encoded in the genotype of each individual as a number that measures the individual’s level of ability in
selecting good “models”. An individual transmits this ability to its offspring. Biological reproduction is
selective in that the individuals that are better at procuring resources (an ability culturally learned from
“models”) are more likely to have offspring than less able individuals. When an individual generates an
offspring, the offspring inherits the same level of ability of its single parent (reproduction is nonsexual), with
the addition of a random genetic mutation that can either slightly increase or decrease the offspring’s level of
ability to select good “models” compared with its parent’s level.
At the beginning of the simulation all individuals are assigned a random genotype, which means that the
ability to select good “models” is not well developed in the initial population. Therefore, in the early
generations not much is learned from the “models” since the “models” are not selected appropriately.
However, because of the selective biological reproduction and the constant addition of random genetic
mutations to the inherited genotypes the average ability to select good “models” progressively increases and
at the end of the simulation it is much more developed than it was in the initial population.
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The simulation we have just described is an example of cooperation between cultural evolution and
biological evolution. The ability of resource procurement is culturally transmitted (i.e., learned from
“models”) whereas the ability to select good “models” is genetically transmitted (i.e., it is encoded in the
inherited genetic material). The two types of evolution are in a reciprocal causal relation. The biological
evolution of the ability to select good “models” is made possible by the cultural evolution of the ability of
resource procurement. If there were no cultural transmission/evolution of the ability to procure resources,
there would be no selective pressure for the evolutionary emergence of the genetically transmitted ability to
select good “models”. In fact, individuals that are born with a higher level of the ability to select good
“models” are not, by this fact alone, more likely to reproduce. Being a good judge of the quality of potential
“models” is of importance only because it allows an individual to learn more, that is, from better “models”.
On the other side, if there were no genetic transmission of the ability to select good “models”, the culturally
transmitted ability to procure resources would not emerge evolutionarily because individuals would not learn
much from “models” because they would be unable to identify good “models”. Hence, biological evolution
causes cultural evolution or at least makes it possible.
Another example of an interaction (reciprocal causality) between cultural and biological evolution is
contained in the following simulation. Imagine that in order to learn by imitating another individual (the
“model”), it is necessary for the “learner” to be physically near the “model” - in order to be able to observe
the behavior to be imitated. In this third simulation each individual has two distinct neural networks, not just
one as in the two preceding simulations. The first network is the network that allows the individual to find
the resource elements distributed in the environment. As in the preceding simulations the connection weights
of this network are randomly assigned at birth and the weights are gradually modified as the individual learns
by imitating a “model”. The second network also allows the individual to move in the environment but the
input units of this second network encode the location of “models” rather then the location of resource
elements. The connection weights of the second network are encoded in the inherited genotype. As in the
previous simulation the individuals that collect more resources are more likely to have offspring and random
mutations are added to the connection weights of the second neural network which are encoded in the
genotype these individuals transmit to their offspring.
At the beginning of the simulation the connection weights of the second network are randomly assigned to
all individuals. Hence, initially, the individuals are unable to move in the environment in such a way that
they remain in proximity to “models”. The members of the initial generations tend to wander in the
environment and to remain at a distance from potential “models”. Since in order to learn they must be
physically near a “model”, this implies that they cannot learn by imitating the “models” and therefore they
are not very good at procuring the resources. However, the connection weights of the second network are
progressively changed by the selective biological evolutionary process and by the genetic mutations and,
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after a certain number of generations, the members of the population can be seen on the computer’s screen to
have a tendency to approach and remain in proximity to “models” (Figure 3).
Figure 3. Spatial distribution of a population of individuals at the beginning of evolution (a) and after a
certain number of generations (b). In (b) the individuals tend to aggregate around "models" in order to be
able to learn from them.
In this simulation, too, there is cooperation between cultural and biological evolution. The ability to
approach and remain in proximity to a “model” does evolve genetically but not because approaching and
remaining in proximity to a “model” by themselves increase an individual’s reproductive chances. The
ability evolves genetically because of the pressure of cultural evolution. Approaching and remaining in
proximity to a “model” makes it possibile for an individual to learn the capacity to procure resources by
imitating the “model”. Hence, genetic evolution is dependent on cultural evolution. However, cultural
evolution also is dependent on genetic evolution in that the culturally transmitted ability to procure resources
would not evolve if individuals would not possess a genetically transmitted and evolved ability to approach
and remain in proximity to “models”
3.3 Darwinian and Lamarckian evolution
In the simulations described sofar a purely Darwinian model of cultural evolution was adopted. A purely
Darwinian model of evolution assumes that what is transmitted does not change after transmission has taken
place. Evolution is due to changes that occur as part of the transmission process: genetic mutations and
sexual recombination in biological evolution and random noise added to “model-to-learner” transmission in
cultural evolution. (We discuss other mechanisms for adding new variants to a cultural pool below.) After
the transmission process is completed there is no further change in what is transmitted. Hence, the
individuals of one generation transmit to the individuals of the next generation exactly what has been
transmitted to them by the individuals of the preceding generation. A Lamarckian model of evolution, on the
other hand, assumes that there is change in what has been transmitted after transmission has taken place.
What is transmitted to an individual is changed - possibly improved on - by the individual before it is
transmitted to another individual.
While biological evolution is accurately modeled by a purely Darwinian model, cultural evolution tends to
be Lamarckian. After an individual has learned some particular behavior from its “model”, the individual can
modify the behavior, possibly making it better, so that any other individual that will learn the behavior from
the individual will learn a new version of the behavior which is different and possibly better than the
originary version.
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Unlike the preceding simulations, the new simulation adopts a Lamarckian model of cultural evolution.
(These simulations, using a somewhat different model of the individual organisms, were done with Gianluca
Baldassarre. Cf. Baldassarre and Parisi, 1999.) Each generation of a population of individuals learns to
capture the resources present in the environment by imitating the individuals of the preceding generation.
This is like the preceding simulations. But in the new simulation by interacting with the natural rather than
with the social environment the individuals can also learn during the course of their entire life to improve
what they have learned from their “models”. In this way they can generate better versions of the behavior of
seeking resources they have learned from their “models”.
We use a reinforcement learning algorithm to model individual learning. As in the preceding simulations an
individual is born with random connection weights and the individual learns to procure the resources by
imitating a “model”. After this cultural learning is completed, the individual starts learning individually. In
each input/output cycle of its life the individual is provided with a reinforcement signal that tells the
individual how good its output is given the input. The neural network controlling the individual’s behavior
uses these reinforcement signals to modify its connection weights in such a way that the performance of the
individual in capturing the resources present in the environment gradually becomes better than it was after
completion of cultural learning. At the end of life, if the individual is selected as a “model” by some
“learner” of the next generation, the behavior it offers to the “learner” as a model to be imitated tends to be
different, and better, than the originary behavior the individual has learned at the beginning of its life from its
“model” of the preceding generation.
We have compared various conditions. In one condition, which replicates the simulations described in the
preceding paragraphs, there is selection of “models”, noise during the transmission process, and no
individual learning during life. In a second condition there is no selection of “models” (all the individuals of
one generation function as “models” of the individuals of the next generation), no noise during the
transmission process, and the individuals learn nonsocially during their life. In a third condition there is both
selection of “models” and individual learning but no noise is added to the transmission process. The results
show that all three conditions lead to an evolutionary improvement in the capacity to capture the resource
elements. (The results of the second condition are shown in Figure 4.) The best results are those obtained in
the first condition and the results obtained in the third condition are slightly better than those obtained in the
second condition. These differences seem to indicate that noise in the transmission process is critical for
cultural evolution. In fact, transmission using our imitation learning model without noise (and without
individual learning) leads to the progressive loss (dissipation) of the ability which is transmitted (Denaro and
Parisi, 1996). (For some discussion of the differences among the various conditions, see Baldassarre and
Parisi, 1999.) In any case we expect that if there is individual learning during life whose results are
transmitted to the next generation (Lamarckian evolution) in addition to cultural transmission with both
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selection of “models” and transmission noise, the results will be better than if there is no such learning
(purely Darwinian evolution).
Figure 4. Cultural evolution of the ability to find the resource elements when the ability is culturally
transmitted without selection of “models” and without noise in the transmission process but there is
individual learning during an individual’s life which causes an improvement in the ability which will be
transmitted to the next generation. The figure shows the decrease in time needed to reach a resource element
by the average individual across 100 generations.
3.4 Cultural transmission of reinforcement signals (values)
In the simulations described sofar what is culturally transmitted are directly behaviors. However, much
cultural learning consists in learning from others not directly behaviors but reinforcement signals (values)
that then can be used to learn behaviors. Baldassarre (2000) has extended the model of imitation learning of
Figure 1 to the cultural transmission of reinforcement signals. The “learner” learns to assign the appropriate
reinforcement value to environmental inputs by using the reinforcements signals generated by the “model” as
teaching input in the backpropagation procedure. Once an individual has culturally learned from the “model”
to assign the appropriate reinforcement value to environmental inputs, the individuals can learn to capture
the resources present in the environment using these reinforcement signals.
In Baldassarre’s simulations both the "model" and the "learner" start from a primary reinforcement signal
which they automatically generate in response to the goal state (e.g. ingesting food) and which is hardwired
by the researcher, and then they learn to generate secondary renforcement signals in response to inputs
preceding the goal state (e.g. seeing food) on the basis of the primary reinforcement signal (which we may
assume to be genetically inherited) (Baldassarre and Parisi, 2000). But while the "model" learns to generate
the secondary reinforcement signals using only its own experience with the world, the "learner" learns these
secondary reinforcement signals both from the "model" and from its own experience of the world. Notice
that one could have biological evolution evolve the primary reinforcement signals themselves.
3.5 Cultural transmission of artifacts
Behaviors and (reinforcerment) values are not the only things which are culturally transmitted and can
evolve culturally. Artifacts also are culturally transmitted and they evolve in successive generations of
artifacts. Artifacts are modifications of the external environment that organisms cause to increase their
reproductive chances. Artifacts are culturally transmitted as each generation reproduces the artifacts of the
preceding generation. If the reproduction of artifacts is selective and new variants are added in each
generation, artifacts evolve. When it is extended to artifacts, cultural evolution becomes technological
evolution.
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A population of individuals lives in an environment with randomly distributed resources and it genetically
evolves an ability to capture the resources present in the environment. Each resource element captured by the
individual contains energy that increases the individual’s reproductive chances by one unit. In addition, each
individual is assigned a set of artifacts which, as they are used by the individual, tend to increase the
reproductive value of the captured resources, acting as a multiplier of the energy contained in the resources.
Artifacts vary in quality. The best artifacts can increase the reproductive value of a captured resource
element up to a factor of 2 while the worst possible artifact increases the reproductive value of a resource
element by a factor of 1 (no increase in reproductive value of the resource element). (These simulations have
been done with Massimiliano Ugolini. Cf. Ugolini and Parisi, 1999.)
At the beginning of the simulation the artifacts have random properties and therefore their quality is rather
poor. Each generation of individuals inherits the artifacts of the preceding generation. This means that each
individual is assigned a certain number of artifacts of the preceding generation as models to be reproduced
(copied) and the individual uses the copies of the artifacts it has produced to increase the reproductive value
of the resources it is able to capture in the environment. To reproduce an artifact an individual has at its
disposal a special neural network with input units encoding the properties of a model artifact and output units
encoding the properties of the new artifact which is produced by copying the model artifact. This neural
network learns to reproduce the artifacts using the backpropagation procedure. The properties of the model
artifact function not only as input to the network but also as teaching input for the backpropagation
procedure. In each cycle the network compares its output (the copied artifact) with the teaching input (the
model artifact) and it changes its connection weights in such a way that after a certain number of learning
cycles the copied artifacts tends to be similar to the model artifacts. After the copying process has been
completed, the individual uses the copied artifacts to increase the reproductive value of the resource elements
it captures in the environment.
If the artifacts of the preceding generation which are used as models are selected appropriately and if some
noise is added to the copying process (more precisely, to the teaching input encoding the properties of the
model artifact), the quality of the artifacts evolves and it reaches a steady state level which is higher than the
initial level.
How are the artifacts selected for reproduction? In our simulations we have experimented with different
schemes for the selection of the artifacts to be used as models for reproduction (Boyd and Richerson, 1985).
We arbitrarily identify one particular artifact as the best possibile artifact (increasing the reproductive value
of a resource element by a factor of 2) and we measure the quality of each individual artifact as the artifact’s
distance from the best artifact in terms of shared properties. The simulations show, predictably, that the best
results from the point of view of the evolution of the quality of artifacts are obtained if the artifacts selected
for reproduction are those with properties most similar to the properties of the best artifact (perfect
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knowledge of artifact quality) (Figure 5). However, this condition of perfect knowledge is unrealistic. We
cannot assume that the individuals know what are the properties of the absolutely best artifact. The only
thing they can observe and compare is the energy (fitness) of individuals using different artifacts. In our
simulations the energy of an individual is the number of resource elements captured by the individual
multiplied by the average quality of the artifacts used by the individual. We have experimented with various
criteria for the selection of the artifacts that rely on the observed energy of the individuals using the artifacts.
Figure 5. Evolutionary increase in energy across 600 generations in a population with artifacts and in a
population without artifacts. (Top two curves: Best individual; Bottom two curves: Average individual. The
curves for the population with artifacts are higher than those of the population without artifacts in both
cases.) (a). Evolutionary improvement in artifact quality when the quality of the artifacts to be selected for
reproduction is directly assessed by the researcher by measuring the distance of an artifact’s properties from
the properties of the best possible artifact (b).
One criterion is selecting for reproduction the artifacts used by the individuals with most energy in each
generation. The reasoning behind this choice is that since these are the best individuals they must be using
the best artifacts. However, as we said, the energy of an individual is a function of two independent (at least
in our simulations) factors: how good is the individual at capturing resource elements and how good are the
artifacts used by the individual. An individual may have high energy not because it uses good artifacts but
because the individual is very good at capturing the raw resource elements. Hence, the artifacts used by the
best individuals may not be the best artifacts. In fact, our simulations show that selecting for reproduction the
artifacts used by the best individuals of the preceding generation does not result in a particulary good
evolution of the quality of the artifacts. This selection criterion is equivalent to Boyd and Richerson’s (1985)
“indirect bias”: you select for reproduction some trait (artifact, behavior, etc.) because it is associated with
another trait which you know is good. In our case, you select for reproduction some particular artifact
because it is associated with an individual with high energy. “Indirect bias” can be an inevitable or even
useful strategy in many circumstances but it does not necessarily produce good results.
Another criterion for the selection of the artifacts is equivalent to Boyd and Richerson’s “direct bias”. This
criterion implies a more direct and accurate evaluation of the quality of the artifacts because it tries to
eliminate the confounding factor represented by the ability to capture resources of the individuals using the
artifacts. We have implemented Boyd and Richerson’s direct bias in the following way. In two different
simulations either all the individuals of the preceding generation or the best individuals of the preceding
generation test all the artifacts in such a way that any single artifact happens to be tested (used) by many
different individuals. We then measure the average energy of all the individuals that have used one and the
same artifact and we take this average as an index of the quality of the artifact. In this way we factor out the
ability level of the individuals using the artifact and obtain a more direct index of artifact quality. In fact, this
selection criterion produces better results than the previous one from the point of view of the evolution of the
quality of artifacts. Furthermore, using only the best individuals of the preceding generation to test the
13
artifacts produces better results than using the entire population because the variance in the ability to procure
raw resources within the subpopulation of the best individuals is less than the variance in the entire
population.
Boyd and Richerson suggest a third criterion for cultural selection, the “frequency bias”. According to this
criterion, the artifacts selected for reproduction are those used by more individuals in the preceding
generation. Since our simulations are so designed that all artifacts tend to be used by the same number of
individuals, we could not test this criterion in our simulations. But we think that simulating the causes and
the effects of the “frequency bias” in artifact reproduction may be very important.
All the results described sofar have been obtained in simulations in which population size is fixed, lifelength
is identical for all individuals, and individuals reproduce all at the same time (as in all simulations described
sofar). If population size can vary, individuals have different lifelengths as a function of the quantity of
energy they are able to procure, and they reproduce periodically until they die as they reach some maximun
age, two further results are obtained. First, since the artifacts augment the quantity of energy extracted by the
individuals from the captured resources, the availability of artifacts and the evolutionary improvement of
their quality leads to longer lives for the individuals and, as a consequence, to an increase in population size.
Second, the presence of artifacts in a population of individuals increases the social differences in energy
among the individuals. The difference in energy (=wealth) between the best individual and the average
individual in a population with artifacts tends to be greater than the difference in a population without
artifacts (Ugolini and Parisi, 1999).
In another set of simulations we have examined the role of some further factors that may affect the evolution
of artifacts (Parisi and Ugolini, 2000). First, we have simulated a population in which there is no selective
reproduction of artifacts but an individual inherits the artifacts used by its (single) parent as models to be
reproduced. This leads to a very slow evolutionary improvement in artifact quality. In fact, the average
quality of artifacts does not change much evolutionarily, not unlike what happens if the artifacts selected for
reproduction are chosen randomly.
A second variable explored is the size of the pool of the artifacts from which the artifacts are selected for
reproduction. We contrast two conditions. In one condition the artifacts selected for reproduction by a
newborn individual are drawn from the pool of all the artifacts currently used by the entire population. In
another condition an individual selects the artifacts to be reproduced not from the artifact pool of the entire
population but only from the more restricted artifact pool of the spatially enclosed subpopulation of which
the individual is a member (enclave). The results indicate that the quality of artifacts and, as a consequence,
population size are higher in the non-subdivided population than in the population subdivided into
subpopulations living in enclaves.
14
In this simulation artifacts are selected for reproduction using the criterion of perfect knowledge (see above).
If the artifacts are randomly selected for reproduction (and presumably also if the artifacts are inherited from
one’s parents) the two different conditions of living with and without artifacts do not make much of a
difference from the point of view of the evolution of artifact quality. On the other hand, artifact variability, as
indicated by the number of artifact lineages still existing at steady state, is higher in the subdivided
population than in the non-subdivided population.
4. Other research issues
In the preceding Section we have described a number of simulations of the cultural evolution of behaviors
and artifacts using the framework of Artificial Life. There are a number of further researcher issues that it is
possible to take up using this same framework. In this final Section of the paper we describe some of these
research directions.
4.1 Other ways of learning from others
Cultural transmission is learning by interacting with other individuals. However, learning by directly
interacting with other individuals is not the only way in which behaviors can be culturally transmitted.
Behaviors (and ideas, knowledge, goals, and values) can be culturally transmitted by interacting not directly
with other individuals but by interacting with the artifacts made by them. We have simulated one way in
which this can happen: an individual can acquire the behavior of producing artifacts by copying existing
artifacts. Another possibility is that a “learner” learns to produce an artifact by imitating the behavior of a
“model” while the “model” is produsing an artifact. But even simply using an artifact, without actually
producing it, can lead to the acquisition of behaviors, perceptual capacities, and even goals and values.
Another interesting research issue is that the reproduction of artifacts may consists in selling and buying
artifacts, which links up cultural to economic behavior.
In our model of imitation learning the individuals from which one learns have been called “models” rather
than “teachers” because these individuals do not play an active role in the transmission process. The behavior
of the “model” which is imitated by the “learner” is not exhibited and has not emerged for the purpose of
transmission but for independent reasons. However, the behavior of providing the “learner” with a model to
be imitated or with reinforcement signals may be acquired by a "teacher" specifically for this purpose. One
could simulate the conditions and processes that lead to the emergence of this type of teaching behavior and
this could be very illuminating for understanding cultural transmission and evolution. Notice that teaching
behavior can evolve biologically and be genetically inherited or it can itself be learned from others and
culturally inherited. In the latter case cultural transmission would have found a way of perpetuating itself.
15
Cultural transmission through language is obviously very important in human cultural transmission. Its study
presupposes that we are able to simulate the emergence and use of language in a population of neural
networks. Like artifacts, language has two roles in cultural transmission and evolution. First, language itself
is a behavior which is culturally transmitted and which evolves culturally, although on a significant speciesspecific genetic basis. Second, language is the medium through which many behaviors (and concepts, ideas,
knowledge items, values, etc.) are culturally transmitted. The cultural transmission of behaviors through
language can take place both explicitly and implicitly. An individual may learn culturally by being explicitly
told by another individual, the “teacher”, the behavior which the individual, the “learner”, should acquire.
But one can also learn through language implicitly, i.e., by listening to or reading what other peole say or
write or, even more fundamentally, by the simple fact of learning a language.
Language is culturally transmitted as newborn individuals acquire a language by imitating the language used
by the individuals of the preceding generation. Language (language production) is one form of behavior.
Therefore, the model of cultural transmission of behavior by imitating a “model” (or perhaps a “teacher”)
can also be used to model the cultural transmission of language. A “model” and a “learner” are both exposed
to the same input from the environment, both produce some signal in response to this input, and the “learner”
uses the linguistic signal produced by the “model” as its teaching input in the backpropagation procedure.
After a certain number of learning cycles, the “learner” will produce the same signals produced by the
“model” in response to the same inputs, i.e., it will use the same linguistic signals to name or talk about the
same objects, events, and situations. If the reproduction of signals is selective (only the best individuals of
the preceding generation function as “language models” or only the best signals are reproduced) and new
variants of signals are added in each generation, an initially random pool of signals will progressively evolve
and become an efficient language. (For some simulations of the evolution of language using the present
framework, cf. Cangelosi and Parisi, 1998.)
Language can also function as a medium for the cultural transmission of concepts. Culturally inherited
linguistic signals can influence the categorizations and discriminations that are among the most important
functions of nervous systems (neural networks). Neural networks are systems for transforming or mapping
activation patterns into other activation patterns. Categorization consists in making internal activation
patterns that map different inputs more similar if the organism must respond to the different inputs with the
same output. Conversely, discrimination is making internal activation patterns more different if the organism
must respond to similar inputs with different outputs. A concept is a “cloud” of internal activation patterns
that are relatively close to each other in the abstract space of internal activation patterns and are relatively
distant from other “clouds” representing other concepts. If environmental inputs are accompanied by
linguistic signals in the experience of an individual, the linguistic signals will influence which input patterns
16
are put together in the same “cloud” of internal patterns (categorization) in the individual’s neural network
and which input patterns are put into different “clouds” (discrimination).
4.2 Why culture is only (or mostly) human?
Any modelling effort aimed at cultural evolution should explain why culture is mostly restricted to humans
while other animal species either do not have culture at all or only have very limited forms of culture. Why
so many behaviors exhibited by humans are culturally transmitted whereas this is not the case for any other
animal species (Tomasello, 1999)? If cultural transmission takes place through language, i.e. by being told,
this can be explained by the fact that language only exists in humans (but then one should explain why only
humans have language). Similarly, if cultural transmission is through artifacts, the limitation of culture to
humans can be explained by the fact that only humans have well developed artifacts (and, again, one has to
explain why well developed artifacts are restricted to humans).
But what about cultural transmission through the imitation of others? In order to answer this question we
should look more closely at the cognitive and social capacities that underly imitation learning and that may
be species-specific to humans. In our model of imitation learning we take it for granted that individuals have
a tendency to pay attention to how other individuals (the “models”) behave, because otherwise they would
not be able to compare their behavior (network’s output) with the “model’s” behavior. Furthermore, we
presuppose that individuals have a tendency/capacity to learn from “models”, i.e., to modify their mind (their
network’s connection weights) in ways that make their behavior progressively more similar to the “models’”
behavior. Notice that learning by imitating another individual implies an ability to compare one’s own
behavior with the behavior of the other individual. In our model of learning by imitation the backpropagation
procedure compares the output of the “learner’s” neural network with the output of the “model’s” neural
network and on the basis of the discrepancy between the two outputs it changes the connection weights of
the “learner’s” network. But in order for two outputs (or, for any two things) to be compared, the two outputs
must encode the same type of information or, more technically, they must be two different output patterns
that belong to the same multidimensional space of output patterns. Now, it is not at all clear that for the
“learner” his or her movements as encoded in the motor output units or in the proprioceptive input associated
with the movements can be compared with the movements of the “model” which are visually perceived. It is
true that the existence of “mirror neurons” has been demonstrated (Gallese, Fadiga, Fogassi and Rizzolatti,
1996) which encode at the same time one’s own movements and the visually perceived movements of other
individuals. But “mirror neurons” exist in monkeys which are not very good at imitating (Visalberghi and
Fragazy, 1990).
It is possible, then, that the ability to imitate requires an ability to predict the perceived consequences of
movements so that the “learner” can compare his or her own movements and the movements of the “model”
17
not as movements but in terms of the effects that the movements have on the environment. Learning a
language through imitation would be easy because the sounds resulting from the “learner’s” phonoarticulatory movements and from the “model’s” phono-articulatory movements can be easily compared. If
this analysis is correct, cultural transmission through imitation further presupposes in the individual that
learns by imitating another individual an ability to learn to predict the consequence of its own movements
and of the movements of others.
We believe that any model of cultural transmission through imitation should include a detailed, behavioral
model of the ability to imitate. We have advanced some hypotheses concerning three cognitive prerequisites
of the ability to imitate: the tendency/ability to pay attention to the behavior of others (and to its
consequences), the tendency/ability to learn by comparing one’s behavior with the behavior of other
individuals, and the ability to learn to predict the consequences of one’s own movements and of the
movements of others. It is possible that the genetic basis for these cognitive prerequisites has evolved only in
humans. Notice that these cognitive abilities may be crucial also for imitating the behavior of another
individual who is constructing an artifact, for producing artifacts using existing artifacts as models, and even
for predicting the performance of planned new artifacts.
4.3 Mechanisms for adding new variants in cultural evolution
What are the mechanisms that add new variants to a cultural pool of behaviors or artifacts? In our
simulations we have used the equivalent of random genetic mutations by adding some statistical noise to the
teaching input in the backpropagation procedure. However, in cultural evolution there may be the equivalent
of sexual recombination in biological evolution which may consist in learning from many different “models”
by recombining together in one’s behavior pieces learned from different “models” or by creating new
artifacts that are new combinations of properties of different existing artifacts, including behaviors and
artifacts from different pools of variants (cultures). But cultural evolution possesses an additional mechanism
for adding variants which has no equivalent in biological evolution: new variants in cultural evolution may
be added as a result of planned design. When we will be able to simulate more complex neural networks that
are able to predict and plan, we will also be able to simulate this powerful mechanism of cultural evolution.
(For networks that learn to predict see Nolfi, Elman, and Parisi, 1994. Gianluca Baldassarre and I are
currently doing some simulations on predicting and planning in neural networks.)
4.4 Vertical, oblique, and horizontal transmission
Cavalli-Sforza and Feldman (1981) have observed that while genetic transmission is only vertical (from
parents to offspring), cultural transmission can also be oblique (between generations but not from parents to
offspring) and horizontal (among individuals of the same generation). Both the causes and the effects of
18
these various kinds of cultural transmission can and should be studied with Artificial Life simulations. For
example, one could simulate the evolution of a pool of behaviors or artifacts in a population that intially is
restricted to vertical cultural transmission, then it adds oblique transmission, and finally horizontal
transmission. Which conditions favour or make possible this type of transition? What are its consequences?
For example the transition would result in a progressive increase in the speed of cultural evolution that might
correspond to the progressive increase in the speed of the evolution of artifacts in the transition from the
Lower Paleolithic to the Upper Paleolithic and then to the Neolithic. (Hewlett and Cavalli-Sforza, 1986, have
found that in a population of living hunter-gatherers - a tribe living in the Central African Republic - 80% of
the abilities possessed by an adult male are culturally inherited from the individual’s father, i.e. vertically.
Cultural evolution is not particularly fast in hunter-gatherers, Paleolithic, societies. Cf. our simulation of
artifact evolution in a population in which an individual inherits the artifacts of its parent.)
In what kind of social groups has cultural transmission first evolved? Cultural transmission may involve
behaviors, e.g., teaching, that create a reproductive advantage for the some other individual at a reproductive
cost for the individual exhibiting the behavior. Therefore, one might hypothesize that cultural transmission
has first evolved in groups of genetically related individuals and it has then extended to other groups. In
some simulations (Pedone and Parisi, 1997) it has already been shown that biologically inherited altruistic
behaviors tend to emerge in groups of genetically related individuals rather than in groups of unrelated
individuals and that these behaviors can allow groups of genetically related individuals to outcompete groups
of unrelated individuals in group selection. This phenomenon may have a parallel in cultural evolution.
(Rendell and Whitehead, in press, think that living in stable matrilineal groups may have favoured the
emergence of culturally transmitted behaviors in cetaceans.)
4.5 Social or cognitive?
If one wants to explain the emergence of culture and the increasing speed of cultural evolution in humans,
one has a choice between a cognitive and a social explanation. For example, in the European Upper
Paleolithic (30-40 thousands years ago) there has been a “cultural explosion” with the appearence of cave art,
sculptures, personal decoration, more complex artifacts pools. Was this “cultural explosion” due to a
possibly genetically based change in cognitive abilities or was it a result of changed social conditions? We
have discussed some hypotheses about the cognitive prerequisites of cultural transmission and cultural
innovation. But some of the simulations we have described may also indicate a role for social factors in the
emergence and increasing importance of culture. For example, our simulations of small populations living in
small enclaves in contrast to larger populations living in larger environments show that the size of the artifact
pool from which one selects the artifacts to be reproduced limits the speed of artifact evolution. This might
indicate that increasing group size throughout the Middle and Upper Paleolithic (Stiner, Munro, and
Surovell, 2000) and the Neolithic may have had a role in the increasing pace of cultural evolution in these
19
periods and, more generally, it can be a first step towards simulating the role of physical or cultural isolation
in the evolution of culture and technology (Diamond, 1997).
Another example is our simulation showing that the evolution of artifacts leads to an increase in both
population size and social stratification. These simulation results may be related to two empirical (historical)
findings: the increase in both population size and social stratification in Neolithic societies with agricultural
technologies and practices and a larger and more differentiated artifact pool compared to Upper Paleolithic
societies.
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21
22
Number of food elements eaten
200
150
100
50
0
0
50
100
Cultural generations
150
200
23
A
B
24
160
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
0
10
20
30
40
50
60
70
80
90
10 0
25
350
2
300
1.9
Quality of artifac ts
Ene rgy
250
200
150
100
1.8
1.7
1.6
1.5
50
1.4
0
1
50
100
150
200
250
300
350
400
450
500
550
Generations
Average 20 best
Average 20 best w /out artif .
(a)
600
1.3
1
Average
A verage w /out artif .
50
100
150
200
250
300
350
Generations
(b)
400
450
500
550