Evolutionary Epistemology is a naturalistic approach to epistemology, which emphasizes the importance of natural selection in two primary roles. In the first role, selection is the generator and maintainer of the reliability of our senses and cognitive mechanisms, as well as the “fit” between those mechanisms and the world. In the second role, trial and error learning and the evolution of scientific theories are construed as selection processes.2. Formal Models
2.1 Static Optimization Models
2.2 Population Dynamics
2.3 Multi-Level Evolution
2.4 Meaning
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[size=30]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 (1974).
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 1974, Bradie 1986, Cziko 1995). Much of the contemporary work in evolutionary epistemology derives from the work of Konrad Lorenz (1977), Donald Campbell (1974, 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.[/size]
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