Evolutionary Epistemology

1. History, Problems, and Issues

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 (1974).

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

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). 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

1.3 Descriptive versus Prescriptive Approaches

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

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 1972, 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.

The interested reader should consult the extensive bibliography originally developed by Donald Campbell and maintained by Gary Cziko at <http://faculty.ed.uiuc.edu/g-cziko/stb/>.

The KLI Theory Lab of the Konrad Lorenz Institute in Vienna offers an extensive bio- and bibliographical data base covering eighteen research areas related to evolution and cognition research. The entry for “evolutionary epistemology” contains links to authors and texts as well as a brief introduction and overview of the field. It is an interactive database and the Institute encourages authors to submit their own relevant bibliographies for inclusion in the database. The database can be accessed at <http://www.kli.ac.at/ >.

2. Formal Models

2.1 Static Optimization Models

The individual elements of the matrix W_ij are the fitness consequences of response i in state j. So, for instance, W₂₁ denotes the fitness consequences of R₂ in S₁. If we let W₁₁ and W₂₂ equal one and W₁₂ and W₂₁ equal zero, then there is a clear evolutionary advantage to performing R₁ in S₁ and R₂ in S₂.

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 R₁ given S₁ as Pr(R₁|S₁). This allows us to calculate that responding to the detector rather than always choosing R₁ or R₂ will be advantageous just in case the following inequality holds (cf. Godfrey-Smith 1996):

Pr(R₂|S₂) /(1 Pr(R₁|S₁))   >   Pr(S₁)(W₁₁ W₂₁)/(1Pr(S₁))(W₂₂ W₁₂)

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 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 "".

p_i   =   p_i w_i /

Fitness

w_i   =   S_A Pr(A)W_iA

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

dp_i/dt   =   p(w_i )

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.

Modeling Cultural Evolution

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 with providing 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 contending basis for evolutionary epistemology is on hold pending an adequate understanding of its basic ontology. [See the online Journal of Memetics for more information.]

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.

2.3 Multi-Level Evolution

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

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 A₁ if the state is T₁ or A₂ if the state is T₂.

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 I₁ and I₂ are signaling systems, in that if both players play the same one of these two strategies they will always get their payoff. I₃ and I₄ are anti-signaling strategies, which result in consistent miscoordination, though they do well against each other. All of the other strategies involve S₃, S₄, R₃, or R₄, which results in the same act being performed no matter what the external state is.

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