Plausibility versus richness in mechanistic models

Philosophical Psychology 26 (1):139-152 (2013)
Abstract
In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of ?how-possibly,? to ?how-plausibly,? to ?how-actually? models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models
Keywords Explanation  mechanism  mechanistic explanation  plausibility  models  richness
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References found in this work BETA
William Bechtel (2005). Explanation: A Mechanist Alternative. Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.

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Citations of this work BETA
Raoul Gervais & Erik Weber (2013). Inferential Explanations in Biology. Studies in History and Philosophy of Biological and Biomedical Sciences 44 (3):356-364.
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Karl Schlechta (1996). Completeness and Incompleteness for Plausibility Logic. Journal of Logic, Language and Information 5 (2):177-192.
Michael H. Albert & Rami P. Grossberg (1990). Rich Models. Journal of Symbolic Logic 55 (3):1292-1298.
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