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Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Published online by Cambridge University Press:  01 January 2022

Abstract

Evolution is often characterized as a tinkerer creating efficient but messy solutions. We analyze the nature of the problems that arise when trying to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem—solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show, first, that evolutionary designs are often hard to understand because they exhibit nonmodular functionality and, second, that breaches of modularity wreak havoc on our strategies of causal and constitutive explanation.

Type
General Philosophy of Science
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

The authors are listed in alphabetical order. Previous versions of this article were presented in the POS seminar and at the Models and Simulations 5 conference in Helsinki. We thank all the participants, as well as the audience at the PSA, for their valuable comments.

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