Cascade versus mechanism: The diversity of causal structure in science

The British Journal for the Philosophy of Science(2022)

Cited 4|Views2
No score
Abstract
According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term “mechanism” gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept–it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science. This paper provides an analysis of the cascade concept in science and the causal structure it refers to. 2 I examine the main features of this causal structure, analogies it is associated with, and strategies used to study it. While scientific work supports distinguishing the cascade and mechanism concepts, this analysis is not merely descriptive. Instead, it provides a theoretical framework for how these concepts should be understood. This framework matters for our assessment of the causal structure of the world, how we study this structure, use it to produce particular outcomes, and communicate about it to others. Before proceeding with this analysis, two clarifications are in order. First, I do not suggest that scientists always use these causal terms in the distinct ways indicated in this analysis, but that they often do and should use them in this way. This reveals normative features of this work and an important way that philosophy can contribute to science, namely, by making suggestions for how these concepts should be understood and used. Second, my analysis of these concepts articulates clear ways in which they differ, but leaves space for some structures in science to be borderline. The presence of such cases should not prevent us from articulating useful categories that distinguish causal structures in the majority of cases, even if the distinction can (in rare cases) be blurred.
More
Translated text
Key words
causal structure,cascade,mechanism,science
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined