Modeling multiscale patterns: active matter, minimal models, and explanatory autonomy

Both ecologists and statistical physicists use a variety of highly idealized models to study active matter and self-organizing critical phenomena. In this paper, I show how universality classes play a crucial role in justifying the application of highly idealized 'minimal' models to explain and understand the critical behaviors of active matter systems across a wide range of scales and scientific fields. Appealing to universality enables us to see why the same minimal models can be used to explain and understand behaviors across these different systems despite drastic differences in the causes and mechanisms responsible for the behaviors of interest. After analyzing these cases in detail, I argue that accounts that focus on identifying common causes or mechanisms in order to explain patterns are unable to accommodate these cases. In contrast, I argue that the justification for using these minimal models is that they are within the same universality class as real systems whose causes and mechanisms are known to be different. I also use these cases to identify several different kinds of explanatory autonomy that have important implications for how scientists ought to approach the modeling of multiscale phenomena.

Automated summary

Both ecologists and statistical physicists use highly idealized models to study active matter and self-organizing critical phenomena. In this paper, the author shows how universality classes can justify the use of minimal models to explain the critical behaviors of various systems, despite differences in causes and mechanisms. The author argues that identifying common causes or mechanisms is insufficient in explaining these cases, and instead, the use of minimal models is justified by their membership in the same universality class as real systems with different causes and mechanisms.