David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophy of Science 80 (5):637-649 (2014)
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
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Gualtiero Piccinini & Carl Craver (2011). Integrating Psychology and Neuroscience: Functional Analyses as Mechanism Sketches. Synthese 183 (3):283-311.
Steven Pinker & Alan Prince (1988). On Language and Connectionism. Cognition 28 (1-2):73-193.
Petri Ylikoski & Jaakko Kuorikoski (2010). Dissecting Explanatory Power. Philosophical Studies 148 (2):201–219.
Citations of this work BETA
No citations found.
Similar books and articles
Lisa Gannett (1999). What's in a Cause?: The Pragmatic Dimensions of Genetic Explanations. [REVIEW] Biology and Philosophy 14 (3):349-373.
Oscar Vilarroya (2001). From Functional Mess to Bounded Functionality. Minds and Machines 11 (2):239-256.
A. J. Chien (1996). Why the Mind May Not Be Modular. Minds and Machines 6 (1):1-32.
Kenneth O. Stanley, Robert T. Pennock & Charles Ofria, On the Performance of Indirect Encoding Across the Continuum of Regularity.
Thomas Bartz-Beielstein (2008). How Experimental Algorithmics Can Benefit From Mayo's Extensions to Neyman–Pearson Theory of Testing. Synthese 163 (3):385 - 396.
Christian Huyck & Ian Mitchell (2005). It is Not Evolution, but a Better Game Would Need a Better Agent. Behavioral and Brain Sciences 28 (4):499-500.
Jeff Edmonds (2008). How to Think About Algorithms. Cambridge University Press.
Donald Ervin Knuth (2010). Selected Papers on Design of Algorithms. Center for the Study of Language and Information.
Russell Powell (2010). The Evolutionary Biological Implications of Human Genetic Engineering. Journal of Medicine and Philosophy 37 (1):22.
Martin Peterson (2011). Is There an Ethics of Algorithms? Ethics and Information Technology 13 (3):251-260.
Marcin Miłkowski (2009). Is Evolution Algorithmic? Minds and Machines 19 (4):465-475.
Joseph Ramsey & Clark Glymour, Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks From Microarray Expression Levels.
Hermann Wagner & Dirk Kautz (1998). Evolutionary Conservation and Ontogenetic Emergence of Neural Algorithms. Behavioral and Brain Sciences 21 (2):285-286.
Peter Carruthers (2006). The Case for Massively Modular Models of Mind. In Robert J. Stainton (ed.), Contemporary Debates in Cognitive Science. Blackwell.
Added to index2012-10-18
Total downloads24 ( #102,580 of 1,696,514 )
Recent downloads (6 months)7 ( #79,559 of 1,696,514 )
How can I increase my downloads?