Even though the evidence‐based medicine movement (EBM) labels mechanisms a low quality form of evidence, consideration of the mechanisms on which medicine relies, and the distinct roles that mechanisms might play in clinical practice, offers a number of insights into EBM itself. In this paper, I examine the connections between EBM and mechanisms from several angles. I diagnose what went wrong in two examples where mechanistic reasoning failed to generate accurate predictions for how a dysfunctional mechanism would respond to intervention. (...) I then use these examples to explain why we should expect this kind of mechanistic reasoning to fail in systematic ways, by situating these failures in terms of evolved complexity of the causal system(s) in question. I argue that there is still a different role in which mechanisms continue to figure as evidence in EBM: namely, in guiding the application of population‐level recommendations to individual patients. Thus, even though the evidence‐based movement rejects one role in which mechanistic reasoning serves as evidence, there are other evidentiary roles for mechanistic reasoning. This renders plausible the claims of some critics of evidencebased medicine who point to the ineliminable role of clinical experience. Clearly specifying the ways in which mechanisms and mechanistic reasoning can be involved in clinical practice frames the discussion about EBM and clinical experience in more fruitful terms. (shrink)

In this field guide, I distinguish five separate senses with which the term ‘mechanism’ is used in contemporary philosophy of science. Many of these senses have overlapping areas of application but involve distinct philosophical claims and characterize the target mechanisms in relevantly different ways. This field guide will clarify the key features of each sense and introduce some main debates, distinguishing those that transpire within a given sense from those that are best understood as concerning distinct senses. The ‘new mechanisms’ (...) sense is at the center of most of these contemporary debates and will be treated at greater length; subsequent senses of mechanism will be primarily distinguished from this one. In part I of this paper, I distinguish two senses of the term ‘mechanism’, both of which are explicitly hierarchical and nested in character, such that any given mechanism is comprised of smaller sub-mechanisms, in turn comprised of yet smaller sub-sub-mechanisms and so on. While both of the senses discussed here are anti-reductive, they differ in their focus on scientific practice versus metaphysics, in the degree of regularity they attribute to mechanisms, and in terms of their relationships to the discussions of mechanisms in the history of philosophy and science. (shrink)

The ontic conception of scientific explanation has been constructed and motivated on the basis of a putative lexical ambiguity in the term explanation. I raise a puzzle for this ambiguity claim, and then give a deflationary solution under which all ontically-rendered talk of explanation is merely elliptical; what it is elliptical for is a view of scientific explanation that altogether avoids the ontic conception. This result has revisionary consequences for New Mechanists and other philosophers of science, many of whom have (...) assimilated their conception of explanation to the ontic conception. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...) argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical (...) science. I argue that at least sometimes which strategy is appropriate is not a pragmatic issue, but turns on the nature of the target. Moreover, the distinction reveals a counterintuitive pattern of progress in some historical explanation: shifting from simple to complex. Sometimes, historical scientists rightly abandon simple, unified explanations in favour of disunified, complex narratives. Finally I compare narrative and mechanistic explanation, arguing that mechanistic approaches are inappropriate for complex narrative explanations. (shrink)

According to the new mechanistic approach, an acting entity is at a lower mechanistic level than another acting entity if and only if the former is a component in the mechanism for the latter. Craver and Bechtel :547–563, 2007. doi:10.1007/s10539-006-9028-8) argue that a consequence of this view is that there cannot be causal interactions between acting entities at different mechanistic levels. Their main reason seems to be what I will call the Metaphysical Argument: things at different levels of a mechanism (...) are related as part and whole; wholes and their parts cannot be related as cause and effect; hence, interlevel causation in mechanisms is impossible. I will analyze this argument in more detail and show under which conditions it is valid. This analysis will reveal that interlevel causation in mechanisms is indeed possible, if we take seriously the idea that the relata of the mechanistic level relation are acting entities and accept a slightly modified notion of a mechanistic level that is highly plausible in the light of the first clarification. (shrink)

Imagine you and your friend Pierre agreed on meeting each other at a café, but he does not show up. What is the difference between a friend’s not showing up meeting? and any other person not coming? In some sense, all people who did not come show the same kind of behaviour, but most people would be willing to say that the absence of a friend who you expected to see is different in kind. In this paper, I will spell (...) out this difference by investigating laypeople’s conceptualisation of absences of actions in four experiments. In languages such as German, French, Italian, or Polish, people consider a friend’s not coming an omission. Any other person’s not coming, in contrast, is not considered an omission at all, but just a mere nothing. This use of the term omission differs from the usage in English, where ‘omission’ refers to all kinds of absences. In addition, ‘omission’ is not even an everyday term, but invented by philosophers for the sake of philosophical investigation. In other languages, ‘omission’ is part of an everyday vocabulary. Finally, I will discuss how this folk concept of omission could be made fruitful for philosophical questions. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

Mechanisms are now taken widely in philosophy of science to provide one of modern science’s basic explanatory devices. This has raised lively debate concerning the relationship between mechanisms, laws and explanation. This paper focuses on cases where a mechanism gives rise to a ceteris paribus law, addressing two inter-related questions: What kind of explanation is involved? and What is going on in the world when mechanism M affords behavior B described in a ceteris paribus law? We explore various answers offered (...) by ‘new mechanists’ and others before setting out and explaining our own answers: mechanistic explanations are a species of oldfashioned covering-law explanation and this often accounts in part for their explanatory power; and B is what it takes for some set of principles that govern the features of M’s parts in their arrangement in M all to be instanced together. (shrink)

Biological processes are often explained by identifying the underlying mechanisms that generate a phenomenon of interest. I characterize a basic account of mechanistic explanation and then present three challenges to this account, illustrated with examples from molecular biology. The basic mechanistic account is insufficient for explaining nonsequential and nonlinear dynamic processes, is insufficient for explaining the inherently stochastic nature of many biological mechanisms, and fails to give a proper framework for analyzing organization. I suggest that biological processes are best approached (...) as a multidimensional gradient—with some processes being paradigmatic cases of mechanisms and some being marginal cases. (shrink)

Most defenders of the new mechanistic approach accept ontic constraints for successful scientific explanation (Illari 2013; Craver 2014). The minimal claim is that scientific explanations have objective truthmakers, namely mechanisms that exist in the physical world independently of any observer and that cause or constitute the phenomena-to- be-explained. How can this idea be applied to type-level explanations? Many authors at least implicitly assume that in order for mechanisms to be the truthmakers of type-level explanation they need to be regular (Andersen (...) 2012; Sheredos 2015). One problem of this assumption is that most mechanisms are (highly) stochastic in the sense that they “fail more often than they succeed” (Bogen 2005; Andersen 2012). How can a mechanism type whose instances are more likely not to produce an instance of a particular phenomenon type be the truthmaker of the explanation of that particular phenomenon type? In this paper, I will give an answer to this question. I will analyze the notion of regularity and I will discuss Andersen's suggestion for how to cope with stochastic mechanisms. I will argue that her suggestion cannot account for all kinds of stochastic mechanisms and does not provide an answer as to why regularity grounds type-level explanation. According to my analysis, a mechanistic type- level explanation is true if and only if at least one of the following two conditions is satisfied: the mechanism brings about the phenomenon more often than any other phenomenon (comparative regularity) or the phenomenon is more often brought about by the mechanism than by any other mechanism/causal sequence (comparative reverse regularity). (shrink)

An important strategy in the discovery of biological mechanisms involves the piecing together of experimental results from interventions. However, if mechanisms are investigated by means of ideal interventions, as defined by James Woodward and others, then the kind of information revealed is insufficient to discriminate between modular and non-modular causal contributions. Ideal interventions suffice for constructing webs of causal dependencies that can be used to make some predictions about experimental outcomes, but tell us little about how causally relevant factors are (...) organized together and how they interact with each other in order to produce a phenomenon. I argue that lab research relies on more elaborated types of interventions targeting in a controlled fashion multiple variables at the same time in order to probe the temporal organization of causally-relevant factors along distinct causal pathways and to test for non-modular interaction effects, thus providing crucial spatial-temporal constraints guiding the formulation of more detailed mechanistic explanations. (shrink)

In this chapter we examine the relation between mechanisms and laws/counterfactuals by revisiting the main notions of mechanism found in the literature. We distinguish between two different conceptions of ‘mechanism’: mechanisms-of underlie or constitute a causal process; mechanisms-for are complex systems that function so as to produce a certain behavior. According to some mechanists, a mechanism fulfills both of these roles simultaneously. The main argument of the chapter is that there is an asymmetrical dependence between both kinds of mechanisms and (...) laws/counterfactuals: while some laws and counterfactuals must be taken as primitive (non-mechanistic) facts of the world, all mechanisms depend on laws/counterfactuals. (shrink)

Is evidential pluralism possible when we move to the social sciences, and if so, to what degree? What are the analytical benefits? The answer put forward in this article is that there is a tradeoff between how serious social science methodologies take the study of mechanisms and the analytical benefits that flow from evidential pluralism. In the social sciences, there are a range of different approaches to studying mechanisms, differentiated by the degree to which the ‘process’ is unpacked theoretically, and (...) whether the approach takes seriously the particular nature of social phenomena and the epistemological consequences that flow from this, as in realist approaches to the study of mechanisms, or whether more neopositivist-based foundational assumptions are adopted. Depending on which approach to study mechanisms is used, evidential pluralism is either: easy but superficial, very productive but challenging, or almost impossible because of the fundamental differences between the types of claims being made and the forms of evidence used. (shrink)

Examples from the sciences showing that mechanisms do not always succeed in producing the phenomena for which they are responsible have led some authors to conclude that the regularity requirement can be eliminated from characterizations of mechanisms. In this article, I challenge this conclusion and argue that a minimal form of regularity is inextricably embedded in examples of elucidated mechanisms that have been shown to be causally responsible for phenomena. Examples of mechanistic explanations from the sciences involve mechanisms that have (...) been shown to produce phenomena with a reproducible rate of success. By contrast, if phenomena are infrequent to the point that they amount to irreproducible observations and experimental results, they are indistinguishable from the background noise of accidental happenings. The inability to detect or measure the phenomenon of interest against the background noise of accidental correlations makes it impossible to elucidate a mechanism by experimental means, to demonstrate that a proposed mechanism actually produces the phenomenon, and ultimately to justify why a hypothetical scenario involving an irregular mechanism should be preferred over attributing irreproducible happenings to chance. (shrink)

In what follows, I suggest that it makes good sense to think of the truth of the probabilistic generalizations made in the life sciences as metaphysically grounded in stochastic mechanisms in the world. To further understand these stochastic mechanisms, I take the general characterization of mechanism offered by MDC :1–25, 2000) and explore how it fits with several of the going philosophical accounts of chance: subjectivism, frequentism, Lewisian best-systems, and propensity. I argue that neither subjectivism, frequentism, nor a best-system-style interpretation (...) of chance will give us what we need from an account of stochastic mechanism, but some version of propensity theory can. I then draw a few important lessons from recent propensity interpretations of fitness in order to present a novel propensity interpretation of stochastic mechanism according to which stochastic mechanisms are thought to have probabilistic propensities to produce certain outcomes over others. This understanding of stochastic mechanism, once fully fleshed-out, provides the benefits of allowing the stochasticity of a particular mechanism to be an objective property in the world, a property investigable by science, a way of quantifying the stochasticity of a particular mechanism, and a way to avoid a problematic commitment to the causal efficacy of propensities. (shrink)

Mechanism realists assert the existence of mechanisms as objective structures in the world, but their exact metaphysical commitments are unclear. We introduce Local Hierarchy Realism (LHR) as a substantive and plausible form of mechanism realism. The limits of LHR reveal a deep tension between two aspects of mechanists’ explanatory strategy. Functional decomposition identifies locally relevant entities and activities, while these same entities and activities are also embedded in a nested hierarchy of levels. In principle, a functional decomposition may identify entities (...) engaging in causal interactions that crosscut the hierarchical structure of composition relations, violating the mechanist’s injunction against interlevel causation. We argue that this possibility is realized in the example of ephaptic coupling, a subsidiary process of neural computation that crosscuts the hierarchy derived from synaptic transmission. These considerations undermine the plausibility of LHR as a general view, yet LHR has the advantages that (i) its metaphysical implications are precisely stateable; (ii) the structure it identifies is not reducible to mere aggregate causation; and (iii) it clearly satisfies intuitive and informal definitions of mechanism. We conclude by assessing the prospects for a form of mechanism realism weaker than LHR that nevertheless satisfies all three of these requirements. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.

This chapter examines the relationship between laws and mechanisms as approaches to characterising generalizations and explanations in science. I give an overview of recent historical discussions where laws failed to satisfy stringent logical criteria, opening the way for mechanisms to be investigated as a way to explain regularities in nature. This followed by a critical discussion of contemporary debates about the role of laws versus mechanisms in describing versus explaining regularities. I conclude by offering new arguments for two roles for (...) laws that mechanisms cannot subsume, one epistemically optimistic and one pessimistic, both broadly Humean. Do note that this piece is not primarily Hume exegesis; it is more of a riff in the key of Hume. (shrink)

This chapter discusses several kinds of reduction that are often found in the biomedical sciences, in contrast to reduction in fields such as physics. This includes reduction as a methodological assumption for how to investigate phenomena like complex diseases, and reduction as a conceptual tool for relating distinct models of the same phenomenon. The case of Parkinson’s disease illustrates a wide variety of ways in which reductionism is an important tool in medicine.

The paper defends the claim that the mechanistic explanation of information processing is the fundamental kind of explanation in cognitive science. These mechanisms are complex organized systems whose functioning depends on the orchestrated interaction of their component parts and processes. A constitutive explanation of every mechanism must include both appeal to its environment and to the role it plays in it. This role has been traditionally dubbed competence. To fully explain how this role is played it is necessary to explain (...) the information processing inside the mechanism embedded in the environment. The most usual explanation on this level has a form of a computational model, for example a software program or a trained artificial neural network. However, this is not the end of the explanatory chain. What is left to be explained is how the program is realized (or what processes are responsible for information processing in the artificial neural network). By using two dramatically different examples from the history of cognitive science I show the multi-level structure of explanations in cognitive science. These examples are (1) the explanation of human process solving as proposed by A. Newell & H. Simon; (2) the explanation of cricket phonotaxis via robotic models by B. Webb. (shrink)

Článek podává systematický kritický přehled o pojetí mechanismu v tzv. nové mechanistické filosofii. Nejdříve je popsán vznik a hlavní principy NMF. Je ukázáno, že NMF vznikla do značné míry jako kritická reakce na, do té doby převažující, logický empirismus. Dále jsou představeny hlavní definiční znaky mechanismu, které jsou po té jednotlivě rozebrány. Na závěr jsou diskutovány přednosti a omezení NMF. Je argumentováno, že NMF nabídla novou a realističtější perspektivu na způsob, jakým se věda dělá a jak se dochází k vědeckým (...) objevům. Má i konstruktivní preskriptivní charakter, neboť povzbuzuje vědce k přemýšlení o vysvětlení jevů prostřednictvím mechanismů. Na straně druhé, pro vysvětlení komplexních jevů jsou heuristiky dekompozice a lokalizace, které tvoří jádro „tradičního" mechanistického přístupu, pouze prvotními aproximacemi a je potřeba je doplňovat dalšími, dynamičtějšími přístupy. (shrink)

According to an influential epistemological tradition, science explains phenomena on the basis of laws, but the last two decades have witnessed a neo-mechanistic movement that emphasizes the fundamental role of mechanism-based explanations in science, which have the virtue of opening the “black box” of correlations and of providing a genuine understanding of the phenomena. Mechanisms enrich the empirical content of a theory by introducing a new set of variables, helping us to make causal inferences that are not possible on the (...) basis of macro-level correlations. However, the appeal to mechanisms has also a methodological price. They are vulnerable to interference effects; they also face underdetermination problems, because the available evidence often allows different interpretations of the underlying structure of a correlation; they are strongly context-dependent and their individuation as causal patterns can be controversial; they present specific testability problems; finally, mechanism-based extrapolations can be misleading due to the local character of mechanisms. At any rate, the study of mechanisms is an indispensable part of the human sciences, and the problems that they raise can be controlled by quantitative and qualitative methods, and an epistemologically informed exercise of critical thinking. (shrink)