Network explanations and explanatory directionality

Abstract

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

Metabolic Physiological Networks: The Impact of Age

“…Nevertheless, there is, of course, no cross-sectional design that can account for the correlated changes that occur within a given individual ( Hofer and Sliwinski, 2001 ). In addition, although our study is transversal in nature, and the generated networks are static, certain network properties, such as a low characteristic path lengths and high clustering coefficient in small world networks, are known to affect dynamic properties, such as the velocity of the spread of a disease ( Jansson, 2020 ).…”

“…In addition, although our study is transversal in nature, and the generated networks are static, certain network properties, such as a low characteristic path lengths and high clustering coefficient in small world networks, are known to affect dynamic properties, such as the velocity of the spread of a disease (Jansson, 2020). Our work provides the layout for evidence-based rationale for adding (or replacing) other CVD risk factors (e.g., CRP or family history) to the definition of MetS (Kahn et al, 2005).…”

General theory of topological explanations and explanatory asymmetry

“…4 In short, correct topological explanations cannot rely on gross misrepresentations of a system's topological properties or physical properties. This point can also be framed in terms of conditions of application, which are elaborated in great detail in another contribution [9] in this theme issue.…”

Sex Differences in the Physiological Network of Healthy Young Subjects

“…Through networks, biological systems can be described by both graph-theoretical metrics as well as visual analysis ( Aittokallio and Schwikowski, 2006 ; Merico et al, 2009 ; Pavlopoulos et al, 2011 ). This approach provides the possibility of observing the aggregate behavior of the system, offering insights on the function and structure of the system ( Arnold et al, 2009 ; Jansson, 2020 ).…”