This essay develops an inferential account of model explanation, based on Mauricio Suárez’s inferential conception of scientific representation and Alisa Bokulich’s counterfactual account of model explanation. It is suggested that the fact that a scientific model can explain is essentially linked to how a modeler uses an established model to make various inferences about the target system on the basis of results derived from the model. The inference practice is understood as a two-step activity, with the first step involving making (...) counterfactual statements about the model itself and the second step involving making hypothetical statements transferring over claims derived from the model onto the target. To illustrate how this two-step activity proceeds, an agent-based simulation model is discussed. (shrink)

Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...) in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion. (shrink)

Stephen Yablo’s notion of proportionality, despite controversies surrounding it, has played a significant role in philosophical discussions of mental causation and of high-level causation more generally. In particular, it is invoked in James Woodward’s interventionist account of high-level causation and explanation, and is implicit in a novel approach to constructing variables for causal modeling in the machine learning literature, known as causal feature learning (CFL). In this article, we articulate an account of proportionality inspired by both Yablo’s account of proportionality (...) and the CFL account of variable construction. The resulting account has at least three merits. First, it illuminates an important feature of the notion of proportionality, when it is adapted to a probabilistic and interventionist framework. The feature is that at the center of the notion of proportionality lies the concept of “determinate intervention effects.” Second, it makes manifest a virtue of (common types of) high-level causal/explanatory statements over low-level ones, when relevant intervention effects are determinate. Third, it overcomes a limitation of the CFL framework and thereby also addresses a challenge to interventionist accounts of high-level causation. (shrink)

Polger and Shapiro argue that their official recipe, a criterion for judging when the phenomenon of multiple realization exists, renders MR less widespread than its proponents have assumed. I argue that, although Polger and Shapiro’s criterion is a useful contribution, they arrive at their conclusion too hastily. Contrary to Polger and Shapiro, I claim that the phenomenon of multiple realization in the biological world, judged by their criterion, is not as scarce as they suggest. To show this, an updated official (...) recipe, namely a multiple mechanistic realization thesis, integrating Polger and Shapiro’s criterion with a compositional conception of realization, is developed. Then, three examples of varied kinds are examined, showing that cases of MR are not so hard to find in the biological world. (shrink)

In this essay I suggest that we view design principles in systems biology as minimal models, for a design principle usually exhibits universal behaviors that are common to a whole range of heterogeneous (living and nonliving) systems with different underlying mechanisms. A well-known design principle in systems biology, integral feedback control, is discussed, showing that it satisfies all the conditions for a model to be a minimal model. This approach has significant philosophical implications: it not only accounts for how design (...) principles explain, but also helps clarify one dispute over design principles, e.g., whether design principles provide mechanistic explanations or a distinct kind of explanations called design explanations. (shrink)

I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy, and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Based on a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another (...) to produce a phenomenon of interest. (shrink)

This essay considers a mixed-effects modeling practice and its implications for the philosophical debate surrounding reductive explanation. Mixed-effects modeling is a species of the multilevel modeling practice, where a single model incorporates simultaneously two (or even more) levels of explanatory variables to explain a phenomenon of interest. I argue that this practice makes the position of explanatory reductionism held by many philosophers untenable because it violates two central tenets of explanatory reductionism: single-level preference and lower-level obsession.

Polger and Shapiro (2016) claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs which recur in different organisms and species and perform specific functions. I show that network motifs with entirely different underlying causal (...) structures can perform the same function of interest. The article also considers the scope problem of multiple realization. (shrink)

This article argues for the explanatory autonomy of psychology drawing on cases from the multilevel modeling practice. This is done by considering a multilevel linear model in personality and social psychology, and discussing its philosophical implications for the reductionism debate in philosophy of psychology. I argue that this practice challenges the reductionist position in philosophy of psychology, and supports the explanatory autonomy of psychology.