Online research in philosophy

In this paper I examine two aspects of Hempel’s covering-law models of explanation. These are (i) nomic subsumption and (ii) explication by models. Nomic subsumption is the idea that to explain a fact is to show how it falls under some appropriate law. This conception of explanation Hempel explicates using a pair of models, where, in this context, a model is a template or pattern such that if something fits it, then that thing is an explanation. A range of well-known counter-examples to Hempel’s models has led his successors to seek alternatives. Problems with limited amendments have encouraged some theorists of explanation to abandon nomic subsumption. So, in particular, causal components have come to be regarded as essential, even though Hempel had intended his model to capture causal explanation as well.1 Here I want to examine the prospects for retaining nomic subsumption by rejecting the other feature of Hempel’s approach – explication by models. An examination of the counter-examples will suggest that it is a mistake to imagine that a limited quantity of information about laws and antecedent conditions will be able to provide an actual explanation – other information, about explanations, may be relevant. This in turn leads me to examine what I shall call structural approaches. They are structural because the status of something as an explanation depends on its fitting into a structure of explanations. There are two structural approaches I shall examine. One is holistic – it proposes that we consider explanation hand-in-hand with the concept of law. This account of explanation inherits its holistic nature from the holistic (or sys- tematic) character of laws of nature. The second supervenience view I shall consider is not global as the holistic approach is. Instead it concentrates on the ‘vertical’ structure of explanations, whereby the existence of a nomic explanation at one level reflects explanations on lower levels on which it supervenes. These structural approaches were first proposed in Bird Synthese 120: 1–18, 1999. © 1999 Kluwer Academic Publishers..

: Among the current philosophical accounts of causation two are the most prominent. The first is James Woodward's interventionist counterfactual approach; the second is the mechanistic approach advocated by Peter Machamer, Lindley Darden, Carl Craver, Jim Bogen and Stuart Glennan. Thecounterfactual approach takes it that causes make a difference to their effects, where this difference-making is cashed out in terms of actual and counterfactual interventions. The mechanistic approach takes it that two events are causally related if and only if there is a mechanism that connects them. In this paper I examine them both in some detail. After pointing out some important problems that both approaches face, I argue that there is a sense in which the counterfactual approach is more basic than the mechanistic one in that a proper account of mechanisms depends on counterfactuals while counterfactuals need not be supported (or depend on) mechanisms. Nonetheless, I also argue that if both approaches work in tandem in practice, they can offer us a better understanding of aspects of Hume's secret connexion and hence a glimpse of it.

This paper compares the relative merits of two alternatives to traditional accounts of causal explanation: Jim Woodward's counterfactual invariance account, and the Mechanistic account of Machamer, Darden, and Craver. Mechanism wins (a) because we have good causal explanations for chaotic effects whose production does not exhibit the counterfactual regularities Woodward requires, and (b)because arguments suggested by Belnap's and Green's discussion of prediction (in'Facing the Future' chpt 6)show that the relevant counterfactuals about ideal interventions on non-deterministic and deterministic systems lack truth value.

Among the current philosophical attempts to understand causation two seem to be the most prominent. The first is James Woodward’s counterfactual approach; the second is the mechanistic approach advocated by Peter Machamer, Lindley Darden, Carl Craver, Jim Bogen and Stuart Glennan. The counterfactual approach takes it that causes make a difference to their effects, where this difference-making is cashed out in terms of actual and counterfactual interventions. The mechanistic approach takes it that two events are causally related if and only if there is a mechanism that connects them. On the face of it, the two approaches need not be in conflict. The mechanisms might satisfy (or depend on) certain interventionist counterfactuals and, conversely, the interventionist counterfactuals might be made true by the presence of certain mechanisms. But, overall, both approaches tend to be imperialistic. Advocates of each argue that their own approach fairs much better than their opponents’. The question then is this: are we forced to choose between the mechanistic approach and the counterfactual one? In this paper, I argue that, as they stand, both approaches face some important problems that need to be fixed. I shall also argue that there is a sense in which the counterfactual approach is more basic than the mechanistic, though the former will benefit from a better understanding of the mechanisms that are at work in causal connections. So both approaches can work together to offer a better understanding of causation. If they work in tandem, they can offer us a glimpse of what Hume famously called “the secret connexion”. But in so far as the ‘secret connexion’ is an intrinsic relation between the causal relata, neither of the above approaches tells us what this relation is.

The controversy about intentional explanation of action concerns how these explanations work. What kind of model allows us to capture the dependency or relevance relation between the explanans, i.e. the beliefs and desires of the agent, and the explanandum, i.e. the action? In this paper, I argue that the causal mechanical model can do the job. Causal mechanical intentional explanations consist in a reference to the mechanisms of practical reasoning of the agent that motivated the agent to act, i.e. to a causally relevant set of beliefs and desires. Moreover, the causal mechanical model can provide in efficient and unproblematic applications, unlike action explanations using ceteris paribus laws or counterfactuals. The drawback of the latter models of explanation is their modal requirement: the explanans must mention or implies sufficient and/or necessary conditions for the explanandum. Such a requirement is too strong when it comes to intentional explanation of action.

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors.

Traditional accounts of explanation fail to illuminate the explanatory relevance of “models that are descriptively false” in the sense that the regularities they entail fail to obtain. In this paper, I propose an account of explanation, which I call ‘explanation by concretization’, that serves to explicate the explanatory relevance of such models. Starting from a highly abstract and idealized model, causal explanations of the absence of regularities are sought by adding complexity to the model or by concretizing it. Whether this process is successful depends on whether the abstractions and idealizations in the basic model succeed in isolating a mechanism, i.e. in representing how it operates when interfering factors are absent. This account is developed in the context of economics and contrasted to those of Daniel Hausman and Nancy Cartwright. I go on to provide an account of how unrealistic models can be used for providing understanding of the way mechanisms work.

Causal explanation proceeds by citing the causes of the explanandum. Any model of causal explanation requires a specification of the relation between cause and effect in virtue of which citing the cause explains the effect. In particular, it requires a specification of what it is for the explanandum to be causally dependent on the explanans and what types of things (broadly understood) the explanans are. There have been a number of such models. For the benefit of the unfamiliar reader, here is a brief statement of some major views. On David Lewis’s account, c causally explains e if c is connected to e with a network of causal chains. For him, causal explanation consists in presenting portions of explanatory information captured by the causal network. On Wesley Salmon’s reading, c causally explains e if c is connected with e by a suitable continuous causal (i.e., capable of transmitting a mark) process. On the standard deductive-nomological reading of causal explanation, for c to causally explain e, c must be a nomologically sufficient condition for e. And for John Mackie, for c to causally explain e there must be event-types C and E such that C is an inus-condition for E.53 In a series of papers and a book, James Woodward (1997, 2000, 2002, 2003a, 2003b) has put forward a ‘manipulationist’ account of causal explanation. Briefly put, c causally explains e if e causally depends on c, where the notion of causal dependence is understood in terms of relevant (interventionist) counterfactual, that is counterfactuals that describe the outcomes of interventions. A bit more accurately, c causally explains e if, were c to be (actually or counterfactually) manipulated, e would change too. This model ties causal explanation to actual and counterfactual experiments that show how manipulation of factors mentioned in the explanans would alter the explanandum. It also stresses the role of invariant relationships, as opposed to strict laws, in causal explanation. Explanation in this model consists in answering a network of “what-if-things-had-been-different questions”, thereby placing the explanandum within a pattern of counterfactual dependencies (cf. Woodward 2003a, p..

Any account of extrapolation from animal models to humans must confront two basic challenges: explain how extrapolation can be justified even when there are causally relevant differences between model and target, and explain how the suitability of a model can be established given only limited information about the target. We argue that existing approaches to extrapolation—either in terms of capacities or mechanisms—do not adequately address these challenges. However, we propose a further elaboration of the mechanisms approach that provides a better treatment of this issue. The central concept in our proposal is what we term comparative process tracing.

Resurgent interest in both mechanistic and counterfactual theories of explanation has led to a fair amount of discussion regarding the relative merits of these two approaches. James Woodward is currently the pre-eminent counterfactual theorist, and he criticizes the mechanists on the following grounds: Unless mechanists about explanation invoke counterfactuals, they cannot make sense of claims about causal interactions between mechanism parts or of causal explanations put forward absent knowledge of productive mechanisms. He claims that these shortfalls can be offset if mechanists will just borrow key tenets of his counterfactual theory of causal claims. What mechanists must bear in mind, however, is that by pursuing this course they risk both the assimilation of the mechanistic theories of explanation into Woodward’s own favored counterfactual theory, and they risk the marginalization of mechanistic explanations to a proper subset of all explanations. An outcome more favorable to mechanists might be had by pursuing an actualist-mechanist theory of the contents of causal claims. While it may not seem obvious at first blush that such an approach is workable, even in principle, recent empirical research into causal perception, causal belief, and mechanical reasoning provides some grounds for optimism.