Models, Mechanisms, and Coherence

Abstract

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim, and to provide us with a better understanding of what mechanisms are and how they explain. While there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic explanation? In this paper, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential.

Against Computational Perspectivalism

“…3 Though typically the mechanistic view is framed as being committed to realism about scientic posits, it need not be (Colombo et al 2015).…”

On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists

“…To construct a mechanistic explanation of a system exhibiting a particular phenomenon, one must decompose that system into its relevant parts and explain how they are organized as well as how they interact to produce the phenomena (Colombo et al, 2014;Craver & Bechtel, 2007;Machamer et al, 2000). A mechanistic model explains a system's phenomena in virtue of its parts, their operations, and their organization, which can together produce the phenomena that is to be explained via a set of orchestrated interactions (Bechtel & Abrahamsen, 2005).…”

“…We restrict ourselves to features of a mechanistic explanation that are most immediately relevant, but broader and deeper accounts of the topic are well established in the philosophy of science (Craver, 2007). For further details, we point interested readers to relevant and important debates concerning computational mechanisms (Miłkowski, 2013), the existence of mechanisms in dynamic complex systems (Chemero & Silberstein, 2008), and the question of whether mechanisms are necessarily linked to scientific realism (Colombo, Hartmann, van Iersel, Models, & Coherence, 2014; Fig. 2).…”

On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists

“…Such explanations offer a mechanism that must do the work in a causal way [15,1], rather than arriving at the state of "work done" via a set of correlative relations or temporal sequence of events. Finally, this explanation must allow for an accurate manipulation of the system [11]. Consider a dirt bike 1 .…”