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Stable Models and Causal Explanation in Evolutionary Biology

Published online by Cambridge University Press:  01 January 2022

Bruce Glymour
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Abstract

Models that fail to satisfy the Markov condition are unstable because changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. Such error arises because non-Markovian models fail to track the causal relations generating the values of response variables. This has implications for discussions of the level of selection: under certain plausible conditoins most standard models of group selection will not satisfy the Markov condition when fit to data from real populations. These models neither correctly represent the causal structure generating nor correctly explain the phenomena of interest.

Type
The Role of Causal Explanations in Understanding Natural Selection
Information
Philosophy of Science , Volume 75 , Issue 5: Proceedings of the 2006 Biennial Meeting of the Philosophy of Science Association. Part II: Symposia Papers. Edited by Cristina Bicchieri and Jason Alexander , December 2008 , pp. 571 - 583
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

Thanks to Chris French and Maria Glymour for conversations and aid in coding. Work on the simulations was supported by a grant from Kansas State University's Center for the Understanding of Origins, whose support I gratefully acknowledge.

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