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). 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, S1and S2, and has two possible responses R1 and R2. 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 Wij 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 S1and 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)/(1−Pr(S1))(W22−W12)
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.

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Expanding the Circle

2. Formal Models

2.1 Static Optimization Models

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Expanding the Circle

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