## 1. History, Problems, and Issues



Traditional epistemology has its roots in Plato and the ancient
skeptics. One strand emerges from Plato's interest in the
problem of distinguishing between knowledge and true belief. His
solution was to suggest that knowledge differs from true belief in
being justified. Ancient skeptics complained that all attempts to
provide any such justification were hopelessly flawed. Another strand
emerges from the attempt to provide a reconstruction of human
knowledge showing how the pieces of human knowledge fit together in a
structure of mutual support. This project got its modern stamp from
Descartes and comes in empiricist as well as rationalist versions
which in turn can be given either a foundational or coherentist twist.
The two strands are woven together by a common theme. The bonds that
hold the reconstruction of human knowledge together are the
justificational and evidential relations which enable us to
distinguish knowledge from true belief. 



The traditional approach is predicated on the assumption that
epistemological questions have to be answered in ways which do not
presuppose any particular knowledge. The argument is that any such
appeal would obviously be question begging. Such approaches may be
appropriately labeled "transcendental."



The Darwinian revolution of the nineteenth century suggested an
alternative approach first explored by Dewey and the pragmatists.
Human beings, as the products of evolutionary development, are natural
beings. Their capacities for knowledge and belief are also the
products of a natural evolutionary development. As such, there is some
reason to suspect that knowing, as a natural activity, could and
should be treated and analyzed along lines compatible with its status,
i.e., by the methods of natural science. On this view, there is no
sharp division of labor between science and epistemology. In
particular, the results of particular sciences such as evolutionary
biology and psychology are not ruled *a priori* irrelevant to
the solution of epistemological problems. Such approaches, in general,
are called naturalistic epistemologies, whether they are directly
motivated by evolutionary considerations or not. Those which are
directly motivated by evolutionary considerations and which argue that
the growth of knowledge follows the pattern of evolution in biology
are called "evolutionary epistemologies."



Evolutionary epistemology is the attempt to address questions in the
theory of knowledge from an evolutionary point of view. Evolutionary
epistemology involves, in part, deploying models and metaphors drawn
from evolutionary biology in the attempt to characterize and resolve
issues arising in epistemology and conceptual change. As disciplines
co-evolve, models are traded back and forth. Thus, evolutionary
epistemology also involves attempts to understand how biological
evolution proceeds by interpreting it through models drawn from our
understanding of conceptual change and the development of theories.
The term "evolutionary epistemology" was coined by Donald
Campbell (1974a).


### 1.1 The Evolution of Epistemological Mechanisms (EEM) versus The Evolutionary Epistemology of Theories (EET)



There are two interrelated but distinct programs which go by the name
"evolutionary epistemology." One focuses on the
development of cognitive mechanisms in animals and humans. This
involves a straightforward extension of the biological theory of
evolution to those aspects or traits of animals which are the
biological substrates of cognitive activity, e.g., their brains,
sensory systems, motor systems, etc. The other program attempts to
account for the evolution of ideas, scientific theories, epistemic
norms and culture in general by using models and metaphors drawn from
evolutionary biology. Both programs have their roots in 19th century
biology and social philosophy, in the work of Darwin, Spencer, James
and others. There have been a number of attempts in the intervening
years to develop the programs in detail (see Campbell 1974a, Bradie
1986, Cziko 1995). Much of the contemporary work in evolutionary
epistemology derives from the work of Konrad Lorenz (1977), Donald
Campbell (1974a, et al.), Karl Popper (1972, 1984) and Stephen Toulmin
(1967, 1972). 



The two programs have been labeled EEM and EET (Bradie, 1986). EEM is
the label for the program which attempts to provide an evolutionary
account of the development of cognitive structures. EET is the label
for the program which attempts to analyze the development of human
knowledge and epistemological norms by appealing to relevant
biological considerations. Some of these attempts involve analyzing
the growth of human knowledge in terms of selectionist models and
metaphors (e.g., Popper 1972, Toulmin 1972, Hull 1988; see Renzi and
Napolitano 2011 for a critique of these efforts). Others argue for a
biological grounding of epistemological norms and methodologies but
eschew *selectionist* models of the growth of human knowledge
as such (e.g., Ruse 1986, Rescher 1990).



The EEM and EET programs are interconnected but distinct. A successful
EEM selectionist explanation of the development of cognitive brain
structures provides no warrant, in itself, for extrapolating such
models to understand the development of human knowledge systems.
Similarly, endorsing an EET selectionist account of how human
knowledge systems grow does not, in itself, warrant concluding that
specific or general brain structures involved in cognition are the
result of natural selection for enhanced cognitive capacities. The two
programs, though similar in design and drawing upon the same models
and metaphors, do not stand or fall together.


### 1.2 Ontogeny versus Phylogeny



Biological development involves both ontogenetic and phylogenetic
considerations. Thus, the development of specific traits, such as the
opposable thumb in humans, can be viewed both from the point of view
of the development of that trait in individual organisms (ontogeny)
and the development of that trait in the human lineage (phylogeny).
The development of knowledge and knowing mechanisms exhibits a
parallel distinction. We can consider the growth of an
individual's corpus of knowledge and epistemological norms or of
an individual's brain (ontogeny), or the growth of human
knowledge and establishment of epistemological norms across
generations or the development of brains in the human lineage
(phylogeny). The EEM/EET distinction cuts across this distinction
since we may be concerned either with the ontogenetic or phylogenetic
development of, e.g., the brain or the ontogenetic or phylogenetic
development of norms and knowledge corpora. One might expect that
since current orthodoxy maintains that biological processes of
ontogenesis proceed differently from the selectionist processes of
phylogenesis, evolutionary epistemologies would reflect this
difference. Curiously enough, however, for the most part they do not.
For example, the theory of "neural Darwinism" as put forth
by Edelman (1987) and Changeaux (1985) offers a selectionist account
of the ontogenetic development of the neural structures of the brain.
Karl Popper's conjectures and refutations model of the
development of human knowledge is a well known example of a
selectionist account which has been applied both to the ontogenetic
growth of knowledge in individuals as well as the trans-generational
(phylogenetic) evolution of scientific knowledge. B. F.
Skinner's theory of operant conditioning, which deals with the
ontogenesis of individual behavior, is explicitly based upon the
Darwinian selectionist model (Skinner 1981).


### 1.3 Descriptive versus Prescriptive Approaches



A third distinction concerns descriptive versus prescriptive
approaches to epistemology and the growth of human knowledge.
Traditionally, epistemology has been construed as a normative project
whose aim is to clarify and defend conceptions of knowledge,
foundations, evidential warrant and justification. Many have argued
that neither the EEM programs nor the EET programs have anything at
all to do with epistemology properly (i.e., traditionally) understood.
The basis for this contention is that epistemology, properly
understood, is a normative discipline, whereas the EEM and EET
programs are concerned with the construction of causal and genetic
(i.e., descriptive) models of the evolution of cognitive capacities or
knowledge systems. No such models, it is alleged, can have anything
important to contribute to normative epistemology (e.g., Kim 1988).
The force of this complaint depends upon how one construes the
relationship between evolutionary epistemology and the tradition. 



There are three possible configurations of the relationship between
descriptive and traditional epistemologies. (1) Descriptive
epistemologies can be construed as competitors to traditional
normative epistemologies. On this view, both are trying to address the
same concerns and offering competing solutions. Riedl (1984) defends
this position. A standard objection to such approaches is that
descriptive accounts are not adequate to do justice to the
prescriptive elements of normative methodologies. The extent to which
an evolutionary approach contributes to the resolution of traditional
epistemological and philosophical problems is a function of which
approach one adopts (cf. Dretske 1971, Bradie 1986, Ruse 1986,
Radnitsky and Bartley 1987, Kim 1988). (2) Descriptive epistemology
might be seen as a successor discipline to traditional epistemology.
On this reading, descriptive epistemology does not address the
questions of traditional epistemology because it deems them irrelevant
or unanswerable or uninteresting. Many defenders of naturalized
epistemologies fall into this camp (e.g., Munz 1993). (3) Descriptive
epistemology might be seen as complementary to traditional
epistemology. This appears to be Campbell's view. On this
analysis, the function of the evolutionary approach is to provide a
descriptive account of knowing mechanisms while leaving the
prescriptive aspects of epistemology to more traditional approaches.
At best, the evolutionary analyses serve to rule out normative
approaches which are either implausible or inconsistent with an
evolutionary origin of human understanding.


### 1.4 Future Prospects



EEM programs are saddled with the typical uncertainties of
phylogenetic reconstructions. Is this or that organ or structure an
adaptation and if so, for what? In addition, there are the
uncertainties which result from the necessarily sparse fossil record
of brain and sensory organ development. The EET programs are even more
problematic. While it is plausible enough to think that the
evolutionary imprint on our organs of thought influences what and how
we do think, it is not at all clear that the influence is direct,
significant or detectible. Selectionist epistemologies which endorse a
"trial and error" methodology as an appropriate model for
understanding scientific change are not analytic consequences of
accepting that the brain and other ancillary organs are adaptations
which have evolved primarily under the influence of natural selection.
The viability of such selectionist models is an empirical question
which rests on the development of adequate models. Hull's (1988)
is, as he himself admits, but the first step in that direction. Cziko
(1995) is a manifesto urging the development of such models (cf. also
the evolutionary game theory modeling approach of Harms 1997). Much
hard empirical work needs to be done to sustain this line of research.
Non-selectionist evolutionary epistemologies, along the lines of Ruse
(1986), face a different range of difficulties. It remains to be shown
that any biological considerations are sufficiently restrictive to
narrow down the range of potential methodologies in any meaningful
way. A non-selectionist approach to evolutionary epistemology, based
on "Poincarean dynamics," has been proposed by
Barham (1990). An alternative approach, which exploits the fact that
organisms and their environments co-evole as a result of dialectical
interactions between them, has led to the development of
"non-adaptational" evolutionary epistemologies (Gontier et
al. 2006). A critical review of the problems facing the development of
the naturalistic turn in evolutionary epistemology can be found in
Callebaut and Stotz (1998).



Nevertheless, the emergence in the latter quarter of the twentieth
century of serious efforts to provide an evolutionary account of human
understanding has potentially radical consequences. The application of
selectionist models to the development of human knowledge, for
example, creates an immediate tension. Standard traditional accounts
of the emergence and growth of scientific knowledge see science as a
progressive enterprise which, under the appropriate conditions of
rational and free inquiry, generates a body of knowledge which
progressively converges on the truth. Selectionist models of
biological evolution, on the other hand, are generally construed to be
non-progressive or, at most, locally so. Rather than generating
convergence, biological evolution produces diversity. Popper's
evolutionary epistemology attempts to embrace both but does so
uneasily. Kuhn's "scientific revolutions" account
draws tentatively upon a Darwinian model, but when criticized, Kuhn
retreated (cf. Kuhn 1962, pp. 172f with Lakatos and Musgrave 1970, p.
264). Toulmin (1972) is a noteworthy exception. On his account,
concepts of rationality are purely "local" and are
themselves subject to evolution. This, in turn, seems to entail the
need to abandon any sense of "goal directedness" in
scientific inquiry. This is a radical consequence which few have
embraced. Pursuing an evolutionary approach to epistemology raises
fundamental questions about the concepts of knowledge, truth, realism,
justification and rationality.


### 1.5 Expanding the Circle



Although Campbell and Popper both pointed to the continuity between
the evolution of human knowledge and the evolution of knowledge in
non-human organisms, much of the early work in evolutionary
epistemology focused on the human condition. However, recent empirical
investigations by psychologists, cognitive ethologists, cognitive
neuroscientists and animal behaviorists have revealed that animals,
both primates and non-primates, have much more sophisticated cognitive
capacities than were previously suspected (Panksepp 1998, Heyes and
Huber 2000, Rogers and Kaplan 2004, Lurz 2011, van Schaik 2010). From
an evolutionary perspective this is not surprising given the shared
evolutionary heritage that all animals share. Taking Darwin seriously
means reconsidering and reassessing the nature of human knowledge in
the light of our increased awareness of the cogntive capabilities of
the members of other species. In addition, once a firm empirical basis
of the scope and limits of animal cognitive capacities has been
established we will be in a position to reassess our philosophical
evaluations of the mental lives of animals and their epistemic and
moral status as well. Further field research promises to revolutionize
our understanding of the sense in which human beings are one among the
animals.



The KLI Theory Lab of the Konrad Lorenz Institute publishes a journal
devoted to issues in evolutionary epistemology in addition to other
applications of biological theory,
 *Biological Theory: Integrating Development, Evolution and Cognition*.


## 2. Formal Models



Every scientific enterprise requires formal and semi-formal models
which allow the quantitative characterization of its objects of study.
The attempt to transform the philosophical study of knowledge into a
scientific discipline which approaches knowledge as a biological
phenomenon is no different. Much of the evolutionary epistemology
literature has been concerned with how to conceive of knowledge as a
natural phenomenon, what difference this would make to our
understanding of our place in the world, and with answering objections
to the project. There are, as well, a number of more technical
projects which attempt to provide the theoretical tools necessary for
a naturalistic epistemology. 


### 2.1 Static Optimization Models



In the simplest sort of model, an organism has to deal with an
environment that has two states, \(S\_1\) and \(S\_2\), and has two
possible responses \(R\_1\) and \(R\_2\). We suppose that what the
organism does in each state makes a difference to its fitness.
Fitnesses are usually written characterized by a matrix \(W\). 



The individual elements of the matrix \(W\_{ij}\) are the fitness
consequences of response \(i\) in state \(j\). So, for instance,
\(W\_{21}\) denotes the fitness consequences of \(R\_2\) in \(S\_1\). If
we let \(W\_{11}\) and \(W\_{22}\) equal one and \(W\_{12}\) and
\(W\_{21}\) equal zero, then there is a clear evolutionary advantage to
performing \(R\_1\) in \(S\_1\) and \(R\_2\) in \(S\_2\).



However, the organism must first detect the state of the environment,
and detectors are not in general perfectly reliable. If the organism
responds automatically to the detector, we can use the probabilities
of responses given states to characterize the reliability of the
detector. We write the probability of \(R\_1\) given \(S\_1\) as
\(\Pr(R\_1 \mid S\_1)\). This allows us to calculate that responding to
the detector rather than always choosing \(R\_1\) or \(R\_2\) will be
advantageous just in case the following inequality holds (cf.
Godfrey-Smith 1996):

 
\[
\frac{\Pr(R\_2 \mid S\_2)}{1-\Pr(R\_1 \mid S\_1)} \gt 
\frac{\Pr(S\_1)(W\_{11}-W\_{21})}{(1-\Pr(S\_1))(W\_{22}-W\_{12})}
\]


This simple model demonstrates that whether or not flexible responses
are adaptive depends on the particular characteristics of the fitness
differences that the responses make, the probability of the various
states of the environment, and the reliability of the detector. The
particular result is calculated assuming that detecting the
environmental state and the flexible response system is free in
evolutionary terms. More complete analyses would include the costs of
these factors.



Static optimization models like the one outlined above can be extended
in several ways. Most obviously, the number of environmental states
and organismic responses can be increased, but there are other
modifications that are more interesting. Signal detection theory, for
instance, models the detectors and cues in more detail. In one
example, a species of "sea moss" detects the presence of
predatory sea slugs via a chemical cue. They respond by growing
spines, which is costly. The cue in this case, the water-borne
chemical, comes in a variety of concentrations, which indicate various
levels of danger. Signal detection theory allows us to calculate the
best threshold value of the detector for the growing of
spines.



Static models depict evolutionary processes in terms of fitness costs
and benefits. They are static in the sense that they model no actual
process, but merely calculate the direction of change for different
situations. If fitness is high, a type will increase, if low it will
decrease. When fitnesses are equal, population proportions remain at
stable equilibrium. Dynamic models typically employ the kinds of
calculations involved in static models to depict actual change over
time in population proportions. Instead of calculating whether change
will occur and in what direction, dynamic models follow change.


### 2.2 Population Dynamics



Population dynamics, sometimes referred to as "replicator
dynamics", offers a tractable way to model the evolution of
populations over time under the kinds of selective pressures that can
be characterized by static optimization models. This is often
necessary, since the dynamics of such populations are often difficult
to predict purely on the basis of static considerations of payoff
differences. The so-called "replicator dynamics" were
named by Taylor and Jonker (1978) and generalized by Schuster and
Sigmund (1983) and Hofbauer and Sigmund (1988). They trace their
source back to the seminal work of R.A. Fisher in the 1920s and 30s.
The generalization covers evolutionary models used in population
genetics, evolutionary game theory, ecology, and the study of
prebiotic evolution. The models can be implemented either
mathematically or computationally, and can model either stepwise
(discrete) or continuous evolutionary change. 



Population dynamics models the evolution of populations. A population
is a collection of individuals, which are categorized according to
type. The types in genetics are genes, in evolutionary game theory,
strategies. The types of interest in epistemological models would be
types of cognitive apparatuses, or cognitive strategies -- ways
of responding to environmental cues, ways of manipulating
representations, and so forth. Roughly, EEM models focus on the
inherited and EET models focus on the learned. The evolution of the
population consists in changes of the relative frequency of the
different types within the population. Selection, typified by
differential reproductive success, is represented as follows. Each
type has a growth rate or "fitness", designated by \(w\),
and a frequency designated by \(p\). The frequency of type \(i\) at
the next generation \(p'\_i\) is simply the old frequency multiplied by
the fitness and divided by the mean fitness of the population
"\(\overline{w}\)".

 
\[p'\_i = p\_i \cdot w\_i \frasl \overline{w}
\]


Division by \(\overline{w}\) has the effect of
"normalizing" the frequencies, so that they add up to one
after each is multiplied by its fitness. It also makes evident that
the frequency of a type will increase just in case its fitness is
higher than the current population average. 


#### Fitness



Fitnesses, which should be understood simply as the aggregation of
probable-growth factors that drive the dynamics of large populations,
may depend on a variety of factors. Fitness components differ from
variation components in that they affect population frequencies
proportionally to those frequencies, that is to say, multiplicatively.
Fitness components in biological evolution include mortality and
reproductive rate. In cultural evolution, they include transmission
probability and rejection probability. Within either sort of model,
what matters is how fitnesses change as a result of other changing
factors within the model. In the simplest cases, fitnesses are fixed
and the type with the highest fitness inevitably dominates the
population. In more complex cases, fitnesses may depend on variable
factors like who one plays against, or the state of a variable
environment. Most commonly, variable fitnesses are calculated using a
payoff matrix like the one above. In general, to calculate the
expected fitness of a type, one multiplies the fitness a type would
have in each situation times the likelihood that individuals in the
population will confront that situation and adds the resulting
products. 

 
\[
w\_i = S\_A \Pr(A)\cdot W\_{iA}
\]


where \(W\_{iA}\) is type \(i\)'s fitness in situation \(A\).
This sort of calculation assumes that the effects of the various
situations are additive. More complex situations can be modeled, of
course, but additive matrices are the standard. It should be noted,
however, that matrix-driven evolution can exhibit quite complex
behavior. For instance, chaotic behavior is possible with as few as
four strategies (Skyrms 1992).



Some relationships may be represented without a matrix. Boyd and
Richerson (1985), for instance, were interested in a special kind of
frequency dependent transmission bias in culture, where being common
conferred an advantage due to imitators "doing as the Romans
do." In such a case, the operative fitness of the type is just
the fitness as calculated according to the usual factors, and then
modified as a function of the frequency of the type.


#### Continuity and Computation



The conceptual bases of replicator dynamics are quite straightforward.
Getting results typically requires one of two approaches. In order to
prove more than rudimentary mathematical results, one typically needs
to derive a continuous version of the dynamics. The basic form is 



\[
dp\_i /dt = p(w\_i - \overline{w})
\]


with fitnesses calculated as usual. Mathematical approaches have been
quite productive, though the bulk of theoretical results apply
primarily to population genetics. See Hofbauer and Sigmund (1988) for
a compendium of such results, as well as a reasonable graduate-level
introduction to the mathematical study of evolutionary processes. 



The second approach is computational. With the increase in power of
personal computers, computational implementation of evolutionary
models become increasingly attractive. They require only rudimentary
programming skills, and are in general much more flexible in the
assumptions they require. The general strategy is to create an array
to hold population frequencies and fitnesses, and then a series of
procedures (or methods or functions) which


1. calculate fitnesses,
2. update frequencies with the new fitnesses, and
3. manage interface details like outputting the new state of the
population to a file or the screen.



A loop then runs the routines in sequence, over and over again. Most
modelers are happy to put their source code on the internet, which is
probably the best place to find it. 


#### Modeling Cultural Evolution



Part of the difficulty in understanding cognitive behavior as the
product of evolution is that there are at least three very different
evolutionary processes involved. First, there is the biological
evolution of cognitive and perceptual mechanisms via genetic
inheritance. Second, there is the cultural evolution of languages and
concepts. Third, there is the trial-and-error learning process that
occurs during an individual's lifetime. Moreover, there is some
reason to agree with Donald T. Campbell that understanding human
knowledge fully will require understanding the interaction between
these processes. This requires that we be able to model both processes
of biological and cultural evolution. There are by now a number of
well-established models of biological evolution. Cultural evolution
presents more novelty. 



Perhaps the most popular attempt to understand cultural evolution is
Richard Dawkins' (1976) invention of the "meme."
Dawkins observed that what lies at the heart of biological evolution
is differential reproduction. Evolution in general was then the
competitive dynamics of lineages of self-replicating entities. If
culture was to evolve, on this view, there had to be cultural
"replicators", or entities whose differential replication
in culture constituted the cultural evolutionary process. Dawkins
dubbed these entities "memes", and they were characterized
as informational entities which infect our brains, "leaping from
head to head" via what we ordinarily call imitation. Common
examples include infectious tunes, and religious ideologies. The main
difficulty with this approach has been the problem of how to provide
specifications for the basic entities. The identity conditions of
genes can be given, in theory, in terms of sequences of base pairs in
chromosomes. There appears to be no such fundamental
"alphabet" for the items of cultural transmission.
Consequently, the project of "memetics" as a contending
basis for evolutionary epistemology is on hold pending an adequate
understanding of its basic ontology. The online
 Journal of Memetics
 contains some early papers on memetics. Although the journal has
ceased publication, the existing papers are still accessible online.
See Atran (2001) and Sperber (2001) for reservations about the
viability of memetic models of cultural evolution. For a recent
defense of this approach, see Dennett (2017).



Population models have been used to good effect in modeling cultural
transmission processes. Evolutionary game theory models are frequently
claimed to cover both processes in which strategies are inherited and
those in which they are imitated. This application is possible in the
absence of any specification of the underlying nature of strategies,
for instance, whether they are to be thought of as
"things" which are replicated, or whether they are
properties or states of the individuals whose strategies they are.
This is sometimes referred to as the "epidemiological
approach", though again, the comparison to infection is due to
the quantitative tools used in analysis rather than to any
presupposition regarding the underlying ontology of cultural
transmission (Sperber 1996, Sperber and Hirschfield 2004).



Some recent work focuses on the capacities of organisms to modify
their environments in ways that affect the selection pressures that
they face. In classical models of organism-environment interactions,
organisms adapted to their environments by "fiting" into
pre-existing niches. Richard Lewontin's dialectical model of
organism-environment interactions emphasized the extent to which
organisms "construct" niches rather than merely
accommodating themselves to those already present in the environment
(Lewontin 1982). There is now a large body of literature on niche
construction (see *inter alia* Laland et al. 2000 and
Odling-Smee et al. 2003) The application of this idea to issues in
evolutionary epistemology takes the form of appeals to cultural niches
and cognitive niches. Cultural niches are created by the capacity of
organisms, in general, to the extent that having culture can be
attributed to them, and human beings, in particular, to learn from one
another and from the constructed cultural modifications of their
environments. For human beings, such constructions take the forms of,
among others, language, educational institutions, communication
systems and other forms of information manipulation and transfer.
Cognitive niches are created by the capacities for constructing mental
models of the environment that, in turn, enable organisms, especially
human beings, during the course of their lifetimes to systematically
and efficiently exploit the resources of their environments (Pinker
2003, 2010; Whiten and Erdal 2012, Whiten and van Schaik 2007, Laland
and O'Brien 2011)


### 2.3 Multi-Level Evolution



The kind of levels involved in evolutionary epistemology are quite
different than the kind of levels of selection which are discussed
much more often in the "levels of selection" debate in
evolutionary biology. In evolutionary biology, the
"levels" of selection under discussion are levels of
scale. The debate concerns whether genes are always the
"units" or "targets" of selection, or whether
selection can occur on higher levels, like organisms, groups, and
species. The levels involved in evolutionary epistemology, on the
other hand, are levels of the regulatory hierarchy involved in the
control of behavior. These include the genetic bases of cognitive and
perceptual hardware, concepts, languages, techniques, beliefs,
preferences, and so forth. Note that in the case of evolutionary
epistemology, the terms "levels" and
"hierarchy" may be impressionistic. There is often no
clear arrangement of levels at all. 



There are at least two different approaches that have been taken to
modeling multi-level evolution.


1. Dual Transmission Models: Boyd and Richerson (1985) adapted models
from genetics to model a case in which a trait (cooperation) was
affected both by genetic and cultural evolution. It was first shown
that a genetically determined bias on cultural transmission could be
selected for in a migratory population. The bias made it easier to
pick up local customs, increasing the likelihood of imitation beyond
that determined by the frequency and perceived value of the behavior.
Once this bias was in place, its effect was strong enough to overcome
the perceived costs involved in cooperative behavior. The model
yielded two important results. First, it provided a novel mechanism
according to which cooperative behavior can stabilize in migratory
populations. But more importantly, it demonstrated that cultural
evolution cannot be predicted purely on the basis of genetic
fitnesses.
2. Multiple Population Models: Harms (1997) constructed a multi-level
dynamic population model of bumblebee learning. Mutual information
between distributions of sensor types, overt foraging behaviors, and
internal foraging preferences, on the one hand, and environmental
states, on the other, was assessed and compared to average fitness of
the population states. It was shown that information present in overt
behaviors may be underutilized, and that exaptation of sensor
mechanisms for preference formation can bring about the utilization of
that information.


### 2.4 Meaning



Full descriptive accounts of truth and justification both demand a
theory of meaning. Until a sign has meaning, it cannot be true or
false. Moreover, determining the meaning of justificatory claims may
provide a descriptive theory of justification. Presumably, what makes
a claim of justification true is the basis of that justification. If
meaning is conventional, then the evolution of meaning becomes an
instance of the evolution of conventions. 



Models of the evolution of conventions have in one case been extended
to apply to meaning conventions. Skyrms (1996, chapter 5) gave an
evolutionary interpretation of David Lewis' (1969) model
of rational selection of meaning conventions. Skyrms was able to show
that there is strong selection on the formation of "signaling
systems" in mixed populations with a full set of coordinated,
countercoordinated, and uncoordinated strategies. It is significant
that the structure of the model and the selective process by which
meaning conventions emerge and are stabilized largely parallels the
account of the evolution of meaning given by Ruth Millikan (1984).



In the simplest version, the model is constructed as follows: We
imagine that there are two states of affairs \(T\), two acts \(A\),
and two signals \(M\). Players have an equal chance of being in either
the position of sender, or receiver. Receivers must decide what to do
based purely on what the sender tells them. In this purely cooperative
version, each player gets one point if the receiver does \(A\_1\) if
the state is \(T\_1\) or \(A\_2\) if the state is \(T\_2\).



Since players will be both sender and receiver, they must have a
strategy for each situation. There are sixteen such strategies, and we
suppose them to be either inherited (or learned) from biological
parents, or imitated on the basis of perceived success in terms of
points earned. Strategies \(I\_1\) and \(I\_2\) are signaling systems,
in that if both players play the same one of these two strategies they
will always get their payoff. \(I\_3\) and \(I\_4\) are anti-signaling
strategies, which result in consistent miscoordination, though they do
well against each other. All of the other strategies involve \(S\_3,
S\_4, R\_3\), or \(R\_4\), which results in the same act being performed
no matter what the external state is.




|  |  |
| --- | --- |
|  | *Sender Strategies* |
| \(S\_1\) | Send \(M\_1\) if \(T\_1\); \(M\_2\) if \(T\_2\) |
| \(S\_2\) | Send \(M\_2\) if \(T\_1\); \(M\_1\) if \(T\_2\) |
| \(S\_3\) | Send \(M\_1\) if \(T\_1\) or \(T\_2\) |
| \(S\_4\) | Send \(M\_2\) if \(T\_1\) or \(T\_2\) |


  



|  |  |
| --- | --- |
|  | *Receiver Strategies* |
| \(R\_1\) | Do \(A\_1\) if \(M\_1\); \(A\_2\) if \(M\_2\) |
| \(R\_2\) | Do \(A\_2\) if \(M\_1\); \(A\_1\) if \(M\_2\) |
| \(R\_3\) | Do \(A\_1\) for \(M\_1\) or \(M\_2\) |
| \(R\_4\) | Do \(A\_2\) for \(M\_1\) or \(M\_2\) |


  



|  |
| --- |
| *Complete
Strategies* |
| \(I\_1\): | \(S\_1,R\_1\) |  | \(I\_9\): | \(S\_3,R\_1\) |
| \(I\_2\): | \(S\_2,R\_2\) |  | \(I\_{10}\): | \(S\_3,R\_2\) |
| \(I\_3\): | \(S\_1,R\_2\) |  | \(I\_{11}\): | \(S\_3,R\_3\) |
| \(I\_4\): | \(S\_2,R\_1\) |  | \(I\_{12}\): | \(S\_3,R\_4\) |
| \(I\_5\): | \(S\_1,R\_3\) |  | \(I\_{13}\): | \(S\_4,R\_1\) |
| \(I\_6\): | \(S\_2,R\_3\) |  | \(I\_{14}\): | \(S\_4,R\_2\) |
| \(I\_7\): | \(S\_1,R\_4\) |  | \(I\_{15}\): | \(S\_4,R\_3\) |
| \(I\_8\): | \(S\_2,R\_4\) |  | \(I\_{16}\): | \(S\_4,R\_4\) |



Simulation results showed that virtually all initial population
distributions become dominated by one or the other of the two
signaling system strategies. The situation becomes more complex when
more realistic payoffs are introduced, for instance, that the sender
incurs a cost rather than automatically sharing the benefit that the
receiver gets from correct behavior for the environment. Even in such
situations, however, the most likely course of evolution is domination
by a signaling system.