Valerie Racine (Western New England University), Wes Anderson (Western New England University)
Within recent discussions about how to conceptualize and understand the behavior of biological systems, such as the genome, philosophers have presented arguments for non-reductionism. John Dupré (2012), in particular, develops a non-reductionist framework for conceptualizing and reasoning about these sorts of entities and their related phenomena. He argues that a reductionist methodology in biology is useful to understand the capacities of components of biological systems, but a non-reductionist methodology is required to understand what the system actually does in virtue of what their components are actually doing. More specifically, Dupré argues that when we do understand how a system actually behaves, then we have some understanding of how the system as a whole enacts the actual behavior of its components; i.e. we have some understanding of downward causation.
We present an argument against Dupré’s non-reductionism. We do so by providing an explicitly causal representation of research on the role of micro-RNA in regulating certain pathways in lung cancer (Johnson et al. 2005) with the representational and inferential tools of causal modeling (Spirtes et al. 2000; Pearl 2009). We show that in such cases particular care is needed to define the appropriate variables measured on selected units to understand this system’s behavior. With well-defined variables, we need not appeal to downward causation at all.
Using our case study, we claim that what Dupré calls the “reductionist principle” can be consistent with research on what biological systems and their components actually do. But, we provide reasons for thinking that the traditional reductionist/non-reductionist divide distracts from what is essential to understanding the behavior of biological systems in their actual settings. We argue that what is required for an understanding of the behavior of these systems is an understanding of its causal structure and the joint frequency distribution over the exogenous variables in the system.
Thus, we aim to show that the tools of causal modeling can be instrumental for understanding what these systems and their components actually do. Developing these types of causal representations of the behavior of biological systems is more fruitful than focusing on intrinsic properties of the components of a system, interaction-relations of components, or downward causation because it provides researchers a framework to make causal inferences about their system of interest.
