Understanding Nonmodular Functionality: Lessons from Genetic Algorithms

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
JournalPhilosophy of Science
Volume80
Issue number5
Pages (from-to)637-649
Number of pages12
ISSN0031-8248
Publication statusPublished - Dec 2013
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 611 Philosophy

Cite this