Philosophy of Science 75 (5):571-583 (2008)
|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. †To contact the author, please write to: Bruce Glymour, Department of Philosophy, 201 Dickens Hall, Kansas State University, Manhattan KS, 66506; e‐mail: email@example.com.|
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