I examine the adequacy of the causal graph-structural equations approach to causation for modeling biological mechanisms. I focus in particular on mechanisms with complex dynamics such as the PER biological clock mechanism in Drosophila. I show that a quantitative model of this mechanism that uses coupled differential equations – the well-known Goldbeter model – cannot be adequately represented in the standard causal graph framework, even though this framework does permit causal cycles. The reason is that the model contains dynamical information (...) about the mechanism that concerns causal properties but that does not correspond to variables that could be subject to independent interventions. Thus, a representation of the mechanisms as a causal structural model necessarily suppresses causally relevant information. (shrink)

In 1968, Motoo Kimura submitted a note to Nature entitled “Evolutionary Rate at the Molecular Level,” in which he proposed what has since become known as the neutral theory of molecular evolution. This is the view that the majority of evolutionary changes at the molecular level are caused by random drift of selectively neutral or nearly neutral alleles. Kimura was not proposing that random drift explains all evolutionary change. He does not challenge the view that natural selection explains adaptive evolution, (...) or, that the vertebrate eye or the tetrapod limb are products of natural selection. Rather, his objection is to “panselectionism’s intrusion into the realm of molecular evolutionary studies”. According to Kimura, most changes at the molecular level from one generation to the next do not affect the fitness of organisms possessing them. King and Jukes (1969) published an article defending the same view in Science, with the radical title, “Non- Darwinian Evolution,” at which point, “the fat was in the fire” (Crow, 1985b). The neutral theory was one of the most controversial theories in biology in the late twentieth century. This chapter will review the debate over the netural theory subsequent to Kimura. (shrink)

Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with (...) the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. (shrink)

Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...) the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. (shrink)

Pearl and Woodward are both well-known advocates of interventionist causation. What is less well-known is the interesting relationship between their respective accounts. In this paper we discuss the different perspectives of causation these two accounts present and show that they are two sides of the same coin. Pearl’s focus is on leveraging global network constraints to correctly identify local causal relations. The rules by which global causal structures are composed from distinct causal relations are precisely defined by the global constraints. (...) Woodward’s focus, however, is on the use of local manipulation to identify single causal relations that then compose into global causal structures. The rules by which this composition takes place emerge as a result of local interventionist constraints. We contend that the complete picture of causality to be found between these two perspectives from the interventionist tradition must recognise both the global constraints of the sort identified by Pearl and the local constraints of the sort identified by Woodward, and the interplay between them: Pearl requires the possibility of local interventions and Woodward requires a global statistical framework within which to build composite causal structures. (shrink)

I consider the question of whether success-linked theories of content – theories like those of Ramsey (1927), Millikan (1984) and Blackburn (2005) which take there to be a definitional link between representational content and behavioral success – are consistent with the plausible claim that we can use content-attributions to explain behavioral success. Peter Godfrey-Smith (1996) argues that success-linked theories of content are too closely linked to success to be able to explain it. Against this, I present a plausible account of (...) how content-attributions make available good explanations of behavioral success, and argue that if we want our content-attributions to be able to do this explanatory work, then we actually need to embrace a success-linked theory of content. (shrink)