How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to (...) the would-be understander, such flagging is only a first step. Are there any further and more advanced ways that idealized models aid understanding? Yes, I propose: the manipulation of idealized models can provide considerable insight into the reasons that some causal factors are difference-makers and others are not, which helps the understander to grasp the nature of explanatory connections and so to better grasp the explanation itself. (shrink)

Peter Menzies has developed a novel version of the exclusion principle that he claims to be compatible with the possibility of mental causation. Menzies proposes to frame the exclusion principle in terms of a difference-making account of causation, understood in counterfactual terms. His new exclusion principle appears in two formulations: upwards exclusion — which is the familiar case in which a realizing event causally excludes the event that it realizes — and, more interestingly, downward exclusion, in which an event causally (...) excludes its realizer. This paper shows that one consequence of Menzies’s proposed solution to the problem of mental causation is a ubiquitous violation of the principle of closure — a fact that forces him into a trilemma to which we see no satisfactory response. (shrink)

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)

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)