The concept of mechanism has been an important organizing principle inscience and philosophy since at least the early modern period(Dijksterhuis 1950 [1961]; Boas 1952). The nature of that organizingprinciple, and precisely how it scaffolds the organization of materialknowledge, has changed considerably over time. In late twentiethcentury philosophy of science, the term “mechanism” cameto stand for a kind of theoretical structure according to which somecapacity or behavior of a whole or an endstate of a process isexplained in terms of the organization and activities of components orantecedents. The goal of discovering mechanisms is an explicit,guiding aim for many contemporary sciences, especially the specialsciences.

But what is a mechanism? Why is mechanistic knowledge so important?How is it related to the primary goals of science, such as prediction,explanation and control? Are there general strategies guiding thesearch for mechanisms? Are mechanisms “real”? How aremechanisms at different levels related to one another? What isrequired of a mechanistic explanation? And what are the characteristicfallacies of mechanistic explanation? This family of questions, andothers besides, became a major focus in the philosophy of science atthe turn of the twenty-first century. The philosophers who took upthese questions in earnest tended to approach the topic throughdetailed case studies from key developments in twentieth centurybiology, with particular attention to the assumptions, constraints,and norms revealed through scientific practice (see, e.g., Bechtel& Richardson 1993; Thagard 2000; Darden 2005; Craver 2007a; Craver& Darden 2013). Here we discuss the many areas of the philosophyof science in which the concept of mechanism has been deployed to makeexplicit and transparent key aspects of the scientific enterprise.

• 1. Why Did the “New Mechanism” Emerge in Late-Twentieth Century Philosophy of Science?
• 2. What Is a Mechanism?
  • 2.1 Depicting Mechanisms
    • 2.1.1 Phenomenon
    • 2.1.2 Parts
    • 2.1.3 Cause
    • 2.1.4 Organization
    • 2.1.5 Levels
  • 2.2 What Mechanisms Are Not
  • 2.3 What Are Not Mechanisms
• 3. Why Does So Much Scientific Practice Revolve Around Mechanistic Explanation?
  • 3.1 Etiological and Constitutive Causal-Mechanical Explanations
  • 3.2 Limits of the CL Model of Constitutive Explanations
  • 3.3 Purely Phenomenal Models
  • 3.4 Constitutive Explanatory Relevance
    • 3.4.1 Against Causation Across Levels of Mechanisms
    • 3.4.2 Constitutive Relevance as Mutual Manipulability
  • 3.5 Completeness and Correctness
    • 3.5.1 Completeness
    • 3.5.2 Correctness
  • 3.6 Intelligibility
• 4. How Do Scientists Discover Mechanisms?
  • 4.1 Strategies of Discovery
  • 4.2 Mechanistic Evidence and Medical Discoveries
• 5. Are Mechanisms Real?
  • 5.1 Mechanisms Are Defined Relative to Phenomena
  • 5.2 Dividing Mechanisms into Parts
  • 5.3 Functions
  • 5.4 Kinds of Mechanisms and Mechanism Boundaries
  • 5.5 Grades of Perspectival Realism
• 6. How Do Different Scientific Disciplines Relate to One Another?
  • 6.1 Explanatory Reductionism
  • 6.2 Ontological Reductionism
    • 6.2.1 Mechanism, Reduction, and Emergence
    • 6.2.2 Mechanism and Realization
  • 6.3 Interfield integration and Integrative Pluralism
• 7. Conclusion
• Bibliography
• Academic Tools
• Other Internet Resources
• Related Entries

1. Why Did the “New Mechanism” Emerge in Late-Twentieth Century Philosophy of Science?

The term “mechanism” has been used both as a laudatoryterm, expressing the highest achievement of a given domain (especiallyscience and metaphysics), and as a term of derision, representing ablinkered view of science or a narrow, lifeless, and bleakphilosophical orientation toward the world (Nicholson 2013;Dupré & Nicholson 2018). The mechanism of the seventeenthcentury, as articulated byDescartes,Gassendi, andBoyle, was not univocal but came to be associated with commitments to theunity of matter and the ultimate explicability of all change in termsof local motion and contact action (see Boas 1952; Dijksterhuis 1950[1961]; Gabbey 2004; Garber 1992; Roux 2017). This form of mechanismemerged largely as a reaction toAristotelian emphases on formal and final causes. The banner of“mechanism” was used as a rallying cry in various timesand places and sciences in the nineteenth and twentieth century (see,e.g., Allen 2005; Nicholson 2014. For example, see Loeb as discussedin Pauly 1987 orHelmholz as discussed in Sulloway 1979). It embodied the ideals of thescience: to mathematize, to find deterministic laws, to render thingsintelligible via assimilation to contact action. In many cases,advocacy for mechanism was largely a reaction to and would-bevanquisher of forms of organicism andvitalism. No single person held all these views, and arguably no single view iscommon among them all.

In contrast to these earlier instantiations, the so-called “NewMechanism” of the late twentieth and early twenty-firstcenturies developed as a reaction to and would be successor oflogical empiricist approaches to the philosophy of science. In that service, the material commitments of mechanistic viewsconflicted with the content-neutral, formal emphasis of the logicalempiricists. The methodology, moreover, took seriously the idea thatwhat one could say about the structure of an explanation, theory, orscience must be responsive not only to the demands of logical rigorbut also to the facts about science as practiced.

The origins of the New Mechanism trace, almost without exception, toprograms in the history and philosophy of science, such as theConceptual Foundations of Science (CFS) Program at the University ofChicago, and the History and Philosophy of Science Department (HPS) atthe University of Pittsburgh, or both, in the 1970s. Students andfaculty at the University of Chicago, included Lindley Darden (CFS),Jon Elster (Philosophy; Political Science), Stuart Kauffman (Biology),Richard Levins (Biology), William Lycan (Philosophy), Ken Schaffner(CFS), Bill Bechtel (CFS), Bob Richardson (CFS) and, later, StuartGlennan (CFS). A converging strand of development ran through theUniversity of Pittsburgh: Peter Machamer and Schaffner (both trainedat Chicago and later joined HPS), Wesley Salmon and John Haugeland(Philosophy), and their student, Carl Craver (HPS).

This sociological fact helps explain the case-based methodology of thenew mechanists: they were especially attentive to the details ofhistorical exemplars of science as practiced: e.g., how claims aretested, how models are related to the world, how explanations andexperiments are evaluated. They emphasized that the language ofcontemporary biomedical and physiological sciences is animated bybackground assumptions about mechanisms. That historical fact, theyargued, offers a window on the structure of the special sciencesgenerally. This historical, practice-centered approach built upon thecriticisms of logical empiricism by, for example, Kuhn (1962), Lakatos(1977), and Laudan (1977). But importantly, mechanists saw in theconcept of a mechanism a fecund, material alternative to the logicalstructure at the backbone of the increasingly anomaly-ridden, formalapproaches of logical empiricism. The new mechanists presented theirwork as a unified treatment of the norms governing work in the specialsciences that, importantly, was modeled on what scientists actuallysay and do when they make discoveries, test models, evaluateexplanations, integrate levels, and the like.

These mechanists built this alternative picture by drawing togetherstrands from philosophy, theoretical biology, and artificialintelligence and showing them at work in their case studies. HerbertSimon’s (1969)Sciences of the Artificial introducedthe idea of nested “hierarchies” of “nearlydecomposable systems” as heuristics for understanding complexsystems. Stuart Kaufman (1971 [1976]) developed an account of“articulation of parts explanations” in developmentalbiology. Lycan (1990) saw in levels of explanation a metaphysicalalternative to reductionism as a viable scientific worldview. Wimsatt(1976a, 1976b, 1997) was for many early mechanists a crucial figureconnecting this theoretical work to discussions of reduction andemergence in the philosophy of science. These strands began tocoalesce into an overarching perspective with Bechtel andRichardson’s (1993 [2010])Discovering Complexity. Thatbook introduced the idea of mechanism as a scaffold for a model ofscientific discovery grounded in reverse engineering methods such asdecomposition and localization. The next major step in thisdevelopment, discussed inSection 3, connected this budding view with Wesley Salmon’s defense of acausal-mechanical theory of scientific explanation.

2. What Is a Mechanism?

The first decade of philosophical work in the new mechanism wasdirected at defining the concept of mechanism. Glennan’sdefinition was arguably the first:

A mechanism underlying a behavior is a complex system which producesthat behavior by… the interaction of parts according to directcausal laws. (Glennan 1996: 52).

Within a decade, definitions multiplied, each with its own commitmentsand implications:

Mechanisms are entities and activities organized such that they areproductive of regular changes from start or set-up to terminationconditions. (Machamer, Darden, & Craver 2000: 3)

MECH: a necessary condition for a representation to be an acceptablemodel of a mechanism is that the representation (i) describe anorganized or structured set of parts or components, where (ii) thebehavior of each component is described by a generalization that isinvariant under interventions, and where (iii) the generalizationsgoverning each component are also independently changeable, and where(iv) the representation allows us to see how, in virtue of (i), (ii)and (iii), the overall output of the mechanism will vary undermanipulation of the input to each component and changes in thecomponents themselves. (Woodward 2002: S375)

A mechanism for a behavior is a complex system that produces thatbehavior by the interaction of a number of parts, where theinteraction between parts can be characterized by direct, invariant,change-relating generalizations. (Glennan 2002: S344)

A mechanism is a structure performing a function in virtue of itscomponent parts, component operations, and their organization. Theorchestrated functioning of the mechanism is responsible for one ormore phenomena. (Bechtel & Abrahamsen 2005: 423)

This proliferation of definitions is noteworthy for two reasons.First, it signaled that philosophers had begun to take“mechanism” seriously as a philosophical explicandum, onpar with “law”, “cause”, and“reduction” (see the entries onlaws of nature,the metaphysics of causation, andreductionism in biology). The concept of mechanism had previously been mentioned in connectionwith many purposes (see, e.g., Fodor 1968; Schaffner 1974; Wimsatt1976a; Cartwright 1989), but the concept of mechanism was leftunexplicated, an “unanalyzed term” (Schaffner 1993: 287).The characterizations reflect a desire to meet that challengedirectly.

This proliferation also quickly revealed that differentcharacterizations of mechanism were designed for different purposes:epistemology, explanation, history, metaphysics, methodology, etc.They required different things of a mechanism, with differentimplications for epistemology, metaphysics, and scientific practice(Tabery 2004; Levy 2013; Andersen 2014a, 2014b). Are the components ina mechanism entities, parts, or components? Do those units engage inoperations, activities, or interactions? Is a mechanism a complexsystem or a structure performing a function? Must the product be afunction, a regularity, an endstate? Must the complex system behaveregularly? Must all mechanisms be composed of modular components?Different answers seem appropriate in different contexts.

To capture what is common across this diversity, Glennan and othersadopted a “minimal mechanism” definition that has broadlybeen adopted (Glennan 2017; Glennan & Illari 2017a; Glennan,Illari, & Weber 2022; for additional characterizations of amechanism, see Illari & Williamson 2012; Fagan 2012; Ioannidis& Psillos 2018; 2022):

A mechanism for a phenomenon consists of entities (or parts) whoseactivities and interactions are organized so as to be responsible forthe phenomenon.

Reference to regularity is omitted to make space for ephemeralmechanisms that work only once or irregularly (Machamer 2004; Bogen2005; Skipper & Millstein 2005; Steel 2008; Glennan 2009; Leuridan2010; DesAutels 2011; Andersen 2011, 2014a, 2014b; Krickel 2014). Theidea that the product of the mechanism must be a function is alsoeliminated to incorporate mechanisms that serve no end. There is nomention in minimal mechanism of Woodward’s Simon-inspired“modularity” or “separability” requirement,allowing for mechanisms with degrees of near-decomposability. Thisminimal definition contains abstract terms (phenomenon, entities,parts, activities, interactions and responsible for) that can be madeconcrete in lax or demanding ways, depending on one’sintellectual needs.

2.1 Depicting Mechanisms

The canonical visual representation of a mechanism underlying aphenomenon is shown inFigure 1 (adapted from Craver 2007a). At the top is the phenomenon, a systemS engaged in behavior \(\psi.\) This is the behavior of themechanism as a whole. Beneath it are the parts (theXs) andtheir activities (the\(\phi\)s) organizedtogether. The dotted, vertically-oriented lines display the parts andactivities as contained within the system engaged in this behavior.This diagram emphasizes compositional relationships, and sospatio-temporal inclusion relations, between a behaving mechanism as awhole and the activities of its parts.

Figure 1: Representation of a mechanismas an acting entity (adapted from Craver 2007a). [Anextended description of figure 1 is in the supplement.]

Figure 2 is an alternative and interchangeable representation of a mechanismas an extended temporal process between inputs and outputs (adaptedfrom Craver, Glennan, & Povich 2021). Relations among inputs andoutputs constitute the behavior of the mechanism as a whole. Themechanism is made of items that mediate between those inputs andoutputs (see Pearl 2014). This figure also makes it easier tounderstand etiological mechanistic explanations, in which the endpoint\((\psi_{\textrm{out}})\) is the explanandum phenomenon and theantecedent process is its etiological (causal) history. Thisrepresentation also allows that the same entity might appear over andover again, engaged in different activities at different temporalstages of the mechanism (or the same activity again). It also allowsthat the entities might not be clearly localized and spatiallyisolated from one another (for further discussion, see Craver,Glennan, & Povich 2021; Gebharter 2014; Gebharter & Kaiser2014).

Figure 2: Representation of a mechanismas an etiological process. [Anextended description of figure 2 is in the supplement.]

Figure 3 integrates the hierarchical orientation ofFigure 1 and the processual characterization ofFigure 2, showing how levels of mechanisms appear from this processualperspective.

Figure 3: Representation of a mechanismas a process spanning multiple levels of mechanistic organization. [Anextended description of figure 3 is in the supplement.]

Consider an example. The “greenhouse effect” refers to theclimate mechanism that traps solar energy within the Earth’satmosphere. This naturally occurring process is essential to life onEarth because it allows the Earth to retain some of the sun’sheat. Human release of high levels of carbon dioxide, methane, nitrousoxide, and other gases into the atmosphere has begun to perturb thissystem. The sun emits electromagnetic radiation in the visible andultraviolet frequencies, which pass through the Earth’satmosphere. The Earth, at its normal temperatures, emits radiationmainly in the infrared. The greenhouse gases, though, specificallyabsorb electromagnetic radiation in the infrared, resulting in heatthat gets in but can’t get out.Figure 4 depicts some key components in this mechanism (e.g., the sun, Earth,the atmosphere), the relevant operations of those parts (e.g.,radiating solar energy, re-radiating heat, trapping heat), and theorganizational relationship between those elements of the mechanism(e.g., that earth issurrounded by the atmosphere, which ismade of gasses as parts). The difference left to rightdepicts the explanation of how human activities contribute to theearth’s warming.

Figure 4: A representation of theclimate mechanism responsible for a warming planet (Source:National Park Service). [Anextended description of figure 4 is in the supplement.]

Each of these interchangeable representations has five elements: (1)the phenomenon, (2) the parts, (3) what the parts are doing, and (4)the organization of the system, which (5) often includes reference tolevels. We now discuss how these elements might be furtherspecified.

2.1.1 Phenomenon

All mechanisms are mechanismsof some phenomenon, aconsequential truism first articulated by Kaufman (1971 [1976]) andsometimes dubbed “Glennan’s Law” (see, e.g., Glennan1996, 2002). For example, the mechanism of protein synthesissynthesizes proteins. The boundaries of a mechanism—what is inthe mechanism and what is not—are fixed by their relevance tothe phenomenon (see alsoSection 5). Kaiser and Krickel (2017) argue that phenomena should generally beconstrued asobject-involving occurrents, where an object (orsystem) is engaged in a certain occurring, which could be a process,an event, or a state. An object might be a cell or an ion channel;examples of occurrents include, respectively, contracting andactivating.

What is it for a mechanism to “be responsible for” aphenomenon? This might mean that the mechanismproduces thephenomenon. Such language is most appropriate when theexplanandum phenomenon is the (presumed) terminus of a causal process(as infigure 2), what Salmon (1984) dubbed the etiological aspect of causal-mechanicalexplanation. In other cases, “responsible” might be betterexplicated as “underlying”, especially when one seeks whatSalmon (1984) calls constitutive explanations.Figure 1 emphasizes a spatial interpretation of underlying: being made ofsmaller parts.Figure 2 emphasizes causal mediation: to underlie an input-output relationshipis to be the mechanism in virtue of which those inputs are transformedinto those outputs. Some combination of these two perspectives issurely required of any adequate way of thinking about mechanisms (seeSection 3.1 on Constitutive and Etiological Explanation).

Some understand “responsible for” in still other ways,e.g., in terms of “maintaining” (Craver & Darden 2013)or “controlling” (Bich & Bechtel 2022; Bechtel 2022;see also the entry onphilosophy of cell biology). These ways of talking often can be inter-translated with one another(e.g., the product is produced, the production has anunderlying mechanism) so long as one is clear about precisely what thephenomenon is supposed to be. Kästner (2021) argues that thesedifferent ways of being “responsible for” allow scientiststo integrate different kinds of explanatory knowledge about complexsystems.

2.1.2 Parts

What is a part of a mechanism? The axioms of mereology are tooabstract and formal to capture the rich sense of parthoodcharacteristic of mechanistic sciences (see Sanford 1993; Craver2007a,b; Wimsatt 1976a; see the entry onmereology). Again, different intellectual projects lead scholars to adoptstronger and weaker constraints.

In metaphysical disputes about the nature and limits of physicalism,some have committed to the idea that mechanisms are composed of“physical parts”, though the idea of being“physical” is itself a promissory note for furtheranalysis. Some emphasize features of causal processes: parts are itemsthat make intelligible contributions to some phenomenon (Haugeland1998; Machamer, Darden, & Craver 2000; Kaiser & Krickel 2017).On that view, parts are individuated only secondary to units ofactivity.

Parts have also been understood as nearly decomposable components,where the interactions within components are stronger than betweencomponents (Haugeland 1998; Simon 1969) or as modular sub-components(Woodward 2001). Glennan proposes an even stronger requirement:

The parts of mechanisms must have a kind of robustness and realityapart from their place within that mechanism. It should in principlebe possible to take the part out of the mechanism and consider itsproperties in another context. (Glennan 1996: 53)

This notion is perhaps too strong to accommodate the more ephemeralparts of some biochemical mechanisms or of the mechanisms of naturalselection (Skipper & Millstein 2005; but see Illari &Williamson 2010).

2.1.3 Cause

Four ways of unpacking the cause in causal mechanism have emerged overtime (Oleksowicz 2021). First, the conserved-quantity approach,developed by Phil Dowe (2011), takes mechanistic causation to be thetransmission of conserved quantities (e.g., momentum, mass-energy)within and by a mechanism. Second, mechanists such as Jim Bogen (2005,2008), Darden (2013), and Machamer (2004), have advocated for anactivity-based account, where the search for some general, unifyingnotion of causation is non-reductively replaced with theidentification of specific, productive activities. Such accountstypically look to science determine what are and are not activities(e.g., pulling, firing, resting) and what are the key features ofthose activities (see also Illari & Williamson 2013).Counterfactual accounts are a third variant found in the mechanismliterature, often employing a manipulationist view of counterfactualcausation. To say that one component is a cause of another is to say,roughly, that one could change the second by intervening on the first(Woodward 2002; Craver 2007a). Finally, mechanistic accounts ofcausation, provided by Glennan (1996, 2009, 2017), understandnon-fundamental causation as derivative from the concept of amechanism; the truth-makers for causal claims at one level aremechanisms at a lower level.

Each of these accounts has faced criticism. The conserved-quantityaccount, it has been noted, struggles to do useful work outsidefundamental physics and have difficulty accommodating mechanisms ofomission and double prevention (Glennan 2002; Williamson 2011).Activity-based accounts are faulted for not saying anything deeperabout what activities are (Psillos 2004), for failing to account forthe relationship between causal and explanatory relevance (Woodward2002), and for failing to make sense of cases of polygenic effects(Persson 2010). Critics of counterfactual approaches tend to emphasizedifficulties of stating truth-conditions on the relevantcounterfactuals and emphasize actual (vs. possible) causation inmechanisms (Bogen 2005, 2008; Machamer 2004). Glennan’smechanistic account has been charged with circularity, since theconcept of a mechanism invariably contains a causal element. Theseaccounts of causation are, for the most part, borrowed from classicdiscussions in metaphysics and reflect different ways of responding toHume’s causal skepticism; the problems associated with themoften turn on their (in)adequacy as a response (Andersen 2014a, 2014b;Matthews & Tabery 2017).

2.1.4 Organization

“Organization” is a distinguishing feature of mechanismsas opposed to aggregates (see entry onlevels of organization in biology; Wimsatt 1997). Aggregate properties are simple sums of the propertiesof the parts. Organization is non-aggregative arrangement, wheredifferent parts enter into a variety of organizing relations,interacting with one another to do something the individuals cannot doon their own.

There are many forms of organization, including spatial, temporal,causal, or hierarchical (Paoletti 2021). Spatial organization, forexample, includes location, size, shape, position, and orientation;temporal organization includes the order, rate, and duration of thecomponent activities (Machamer, Darden, & Craver 2000). Dynamicalmodels in particular may reveal complex temporal organization ininteractive mechanisms (Bechtel 2006, 2011, 2013; see also the entryonphilosophy of cell biology). There are also more abstract patterns of organization (Alon 2007; seeLevy 2014; Levy & Bechtel 2013).

Modular organization, in which components are nearly decomposablesub-parts of a mechanism, has been of particular interest. Somecounterfactual accounts of mechanism (Woodward 2001, 2002, 2014;Menzies 2012) require that the causal relations in mechanisms be“modular” in the sense that it should be physicallypossible to intervene on a putative cause variable in a mechanismwithout disrupting the functional relationships among the othervariables in the mechanism. Steel (2008) articulates a weaker form ofmodularity in his probabilistic analysis of mechanisms—one thatfollows directly from Simon’s (1969) idea of nearly decomposablesystems. On Simon’s view, the parts of a system have more andstronger causal relations with other components in the system thanthey do with items outside the system. The idea of modularity, and therelations among these distinct articulations, are ripe for future work(see Matthews 2019).

2.1.5 Levels

Mechanisms and theories about them typically span multiple levels (seeespecially Craver 2016). Mechanistic thinking about levels traces toSimon’s (1969) parable of the often-distracted watchmakers,Tempus and Hora. Simon argued that Tempus, who builds hierarchicallydecomposable watches, will build more watches than Hora, who buildsholistic watches, given that decomposable watches tend to fail locallywhile holistic watches tend to fail globally. For analogous reasons,Simon argues, evolved structures are more likely to be nearlydecomposable into hierarchically organized assemblages at multiplelevels of organization.

From a mechanistic perspective, levels are not monolithic divides inthe furniture of the universe (as represented by Oppenheim &Putnam 1958), nor are they fundamentally a matter of size or of theexclusivity of causal interactions within a level (Wimsatt 1976a).Rather, levels of mechanisms are defined locally within a multilevelmechanism: one item is at a lower level of mechanisms than anotherwhen the first item is a part of the second and when the first item isorganized (spatially, temporally, actively) with the other componentssuch that together they realize the second item (Craver 2016; Povich& Craver 2017). Craver defines levels of mechanisms in terms of arelationship between the behavior \((\psi)\) exhibited by a system(S) and the activity \((\phi)\) of some component part(X) of that system (seeFigure 1 above). On this account,X’s \(\phi\)-ing is at alower level of mechanistic organization thanS’s\(\psi\)-ing if and only if (i)X is a part ofS,and (ii)X’s \(\phi\)-ing is a component inS’s \(\psi\)-ing. In short, to say that something is ata lower mechanistic level than the mechanism as a whole is to say thatit is a working part of the mechanism. Alternatively, but equivalently(seeFigure 3), ifA is described as an input-output relation\((A_{\textrm{in}} \rightarrow A_{\textrm{out}}),\) then thelower-level components are the causal intermediates between input andoutput (Craver, Glennan, & Povich 2021; see also Prychytko 2021).Levels of mechanisms seem to play a central role in structuring therelations among many different models in contemporary biology (e.g.,between Mendelian and molecular genetics (Darden 2006), betweenlearning and memory and channel physiology (Craver 2007a), and betweenpopulation-level variation and developmental mechanisms (Tabery 2009,2014)).

Some crucial features of this account should be noted:

• Parts and wholes. Mechanistic levels are aspecies of part-whole relation delimited by the causal organizationcharacteristic of mechanism. Unlike Oppenheim and Putnam, however,mechanistic levels are composed by a distinctive mechanisticcomponency relation.
• Relata. The relata are the behavior of amechanism as a whole, at the higher level, and the components andactivities in the mechanism at the lower level.
• Relevance. Because the componency relation isdefined by constitutive relevance (seeSection 3.2), interlevel relations are defined relative to a specified top-mostphenomenon. Different choices of phenomena are generally associatedwith different decompositions and so a different interlevel structure(Simon 1969; Kauffman 1971 [1976]; Craver 2007a. This is why levels ofmechanism are local rather than monolithic.
• Size. Levels of mechanisms are not defined bysize relations, though a size ordering follows from the fact thatthings at lower-levels can take up no more space than things at higherlevels. Unlike size-driven views of levels (see Wimsatt 1976a), thisview allows that things of radically different sizes might be found atthe same level. Explanations might require going up a size level, aswhen facts about independent assortment are explained by reference tochromosomes during cell division (see Darden 2006).

This view of levels forms the abstract background against whichmechanistic views of reduction, emergence, interlevel causation, andinterfield relations are articulated (seeSection 6 below).

This background picture has been widely discussed and criticized.Leuridan (2012) argues that this view of levels is incoherent, perhapspresupposing the idea of part-hood it should explicate. Potochnik andMcGill (2012) argue that the concept of level mischaracterizes themessy relations found in science and insists on a homogeneouscomposition of things at a single level (though arguably this is nottrue of mechanistic levels). di Frisco (2017) argues againstmechanistic conceptions of levels, largely on the ground that it hasno content over and above claims about token composition and defendsinstead a time-scale based account of levels that will hold acrosstypes. Eronen (2013; 2015), in contrast, rejects the mechanisticnotion of levels on the grounds that it imposes no homogenouscomposition of things at a single level, which while true is taken byadvocates of the notion as one of its distinctively positive. Brooks(2017) defends the continued utility of the levels metaphor, includinglevels of mechanisms, in scientific practice. Kaiser (2015) develops abroader notion of levels in the same spirit as the mechanisticapproach, but not restricted to mechanistic part-whole relations.Bertolaso and Buzzoni (2017) respond to Eronen’s criticisms andprovide a pluralistic account of higher-level causes grounded in themechanistic literature and exemplified in recent work in cancerepigenetics. Harbecke (2015) uses the mechanistic theory plus somefeatures of his regularity account of constitution to recover a notionof level that works in the neurosciences.

Philosophical work in this area remains active; see, for example,Bechtel 1988; Craver and Bechtel 2007; Brooks 2017; DiFrisco 2017;Povich & Craver 2017; Kästner 2018; Kästner &Anderson 2018; Fazekas & Kertész 2011; DiFrisco, Love,& Wagner 2020; and Piekarski 2022; and Bechtel 2022; see also theentry onlevels of organization in biology.

2.2 What Mechanisms Are Not

The term “mechanism” has a complex history, bringing withit many dangers in the effort to use the term for a new, highlyproprietary project. New mechanists have therefore been especiallyvocal in distancing their view from some especially misleadingassociations.

1. Mechanisms are not necessarilydeterministic. They mightbe stochastic. They might be composed of stochastic activities (Bogen2005, 2008), or chancy simply because of instability in thearrangement and interaction of the component parts.
2. Mechanisms are not limited topush-pull dynamics(Machamer, Darden, & Craver 2000). Descartes’ mechanism hadthis feature, but new mechanists explicitly liberalize the notion toallow other kinds of causing.
3. Appeal to mechanisms is not necessarilyreductionistic.The assumption that mechanism and reductionism go hand-in-hand isprobably the most commonly misplaced intuition about the concept. Muchof the early work by philosophers of mechanism focused on fields likebiology and psychology, where the express purpose was to make sense ofhow mechanisms scaffold the effort to build multilevel explanations,and mechanistic organization has been a key argument in favor ofhigher-level causes (Bechtel 2009; McCauley & Bechtel 2001;Section 6 below).
4. Not all mechanisms aremachines. All machines havemechanisms within them, but machines and their mechanism areindividuated separately. Machines are designed for a function;mechanisms need not be (Darden 2006). Machines can be on or off;mechanisms are how things work and, when they involve machines, theyare machines in action (Machamer, Darden, & Craver 2000) ratherthan merely a static and inert arrangement of parts.
5. Mechanisms are often notsequential orlinear.Mechanisms can have feedback loops and cycles (Bechtel 2011). They canalso be organized so as to maintain an equilibrium, something thatmight appear static but is in fact the product of active regulation(Bechtel 2022).
6. Mechanisms are not necessarilylocalized. Components ofmechanisms might be distributed and might violate intuitions about theboundaries of objects. The assumption of localization is often animportant heuristic in the search for mechanisms (Bechtel &Richardson 1993 [2010]); however, it is an heuristic that sometimeshas to be abandoned.
7. Mechanisms are not justfictions/metaphors. When ascientist says that there is a mechanism that makes proteins in livingorganisms, she is not just using a machine metaphor; rather, she issaying that there are in fact parts and activities organized in livingorganisms such that they produce proteins.
8. Mechanisms do not necessarily comepre-packaged out in nature. Mechanisms are bounded, instead,by relevance to the phenomenon they explain.

2.3 What Are Not Mechanisms

Some object that there’s so little left to the idea of mechanismonce it sheds its historical associations and evades itsdetractors’ criticisms as to make the notion trivial(Dupré 2013; Shapiro 2017; Weiskopf 2011; Woodward 2017).

Consider just mechanistic theories of explanation: the history ofscience contains many other conceptions of scientific explanation andunderstanding that are at odds with this commitment. Some have heldthat the world should be understood in terms of divine motives. Othersemphasize explanations in terms of telos and purpose. Others have heldthat the aim of science is prediction, not explanation, and sodownplay the value of mechanistic knowledge. Still others emphasizelaws, a common contrast with mechanisms (Leuridan 2010; Craver &Kaiser 2013).

But what, the critic might push further, doesnot count as amechanism? Here are some of the more obvious contrast classes:

1. Entities (orobjects) are not mechanisms.Mechanisms do things. If an object is not doing something (i.e., ifthere is no phenomenon), then it is not a mechanism. (But seeSection 5 for how this links up with types, tokens, and kinds.)
2. Correlational patterns are not mechanisms. Mechanismsexplain at least many correlations, and many correlations can be usedto characterize causal or mechanistic relations, but correlationspatterns themselves are not mechanistic.
3. Meretemporal sequences. Wednesday follows Tuesday, butnot in a mechanistic sense, again, because they are not causallyrelated.
4. Merespatial arrangements. One can describe how parts areorganized in space without regard to whether or how those parts areorganized causally. A jigsaw puzzle, for example, is not amechanism.
5. Inferences,reasons, andarguments arenot mechanisms. Though there are mechanisms by which inference andreasoning are realized, what makes something an inference or a reasonis logical relation and not (merely) a causal relationship betweenpremise and conclusion. Likewise, mathematical proofs are notmechanisms.
6. Symmetries are not mechanisms. Many kinds of symmetry areof fundamental importance in different areas of physics (e.g.,translational symmetries, rotational symmetries). These are featuresof physical systems that are highly general facts or assumptions, notmechanisms.
7. Fundamental laws andfundamental causalrelations are not mechanisms. If a law or causal relation isfundamental, then (by definition) there is no mechanism for it.
8. Relations of logical and mathematical necessity are notmechanisms. Such truths hold in all possible worlds and so do notdepend for their truth on facts about the causal structure of thisworld.
9. Taxonomies are not mechanisms. The periodic table, forexample, does not describe a mechanism, though it contains informationabout how innumerable mechanisms work.

Importantly, to be a non-mechanism is not to be worthless. Noteverything scientists do involves the search for mechanisms. But muchof it does.

3. Why Does So Much Scientific Practice Revolve Around Mechanistic Explanation?

Those who unite under the label of mechanism in contemporaryphilosophy have by and large attempted to build a charitable anddefensible reconstruction of what “mechanism” might meanas an objective for sciences as diverse as economics, cognitivescience, molecular biology and different branches of physics. In thateffort, they emphasized the centrality of causation (over strictdeterministic laws) and the importance of integrating explanationsacross levels of organization.

Many scientists think of explanation in broadly causal-mechanicalterms: to explain a phenomenonP is to know the causes for orthe mechanisms underlyingP. This idea contrasts with thecovering law (CL) model of explanation, developed by logicalempiricists such as Ayer (1936) and Hempel (1965), according to whichexplanations are arguments with law statements as premises, and byunificationists (the U model), such as Friedman and Kitcher, whoassociate explanatory power with reducing the number of brute facts orargument schemas that must be taken for granted while maximizing scopeand stringency (see the entry onscientific explanation).

There is now broad consensus that both the CL model and the U modelfail to express accurately the norms governing the scientificpractices of constructing, evaluating, and revising scientificexplanations. One common thread of criticism (Bromberger 1966; Salmon1984; Scriven 1959) is that they cannot distinguish laws fromregularities (the light postulate vs. the fact that no golden bust ofDonald Trump is greater than 500 kg), causal laws from non-causalregularities (deriving the length of the shadow from the height of theflagpole rather than vice versa), or predictions from explanations(predicting storms from barometer readings vs. pressure changes). (Seeother entries onlaws of nature,ceteris paribus laws, and themetaphysics of causation.) At the core, the CL model, the U model, and the empiricist approacheseach exemplify, arguably preclude explicit reference to the causal andmechanistic structures scientists claim to seek when they build, test,and revise their explanations.

Considerable confusion can be avoided by carefully distinguishing therepresentations used to convey information about a mechanism (models)and the mechanisms (causal structures) themselves. The same confusionattends the CL model, in which one can emphasize arguments (models) orlaws (nomic structures). Some philosophers write and think in onticmode, emphasizing the first of these (Salmon 1989; Lewis 1986; Craver2014; Strevens 2013). Other authors write in representational mode(for example, C. Wright & Bechtel 2007). Failing to understand andtrack this difference leads often to absurdity (see Illari 2013; seealso Kästner & Haueis 2021).

3.1 Etiological and Constitutive Causal-Mechanical Explanations

Salmon (1984) distinguished two “aspects” of causalmechanical explanation, two ways of situating something in the causalstructure of the world: etiological and constitutive. Etiologicalexplanations reveal the causal history of the explanandum phenomenon.For example, the rising temperature explains why the balloon expands.Constitutive explanations, in contrast, explain a phenomenon bydescribing the mechanism underlying it, revealing its internal causalstructure. For example, the balloon expands when heated because themean kinetic energy of the molecules increases and their collectiveimpact with the container thus assumes a greater force. Salmon focusedon etiological explanations and did not develop an account of theconstitutive aspect. This lacuna became a primary focus of newmechanists.

Contra some critics (by, e.g., Woodward 2017; Rescorla 2018),it is not part of the mechanistic theory of explanation thatphenomenal laws must lack explanatory content. This charge rests onthe failure adequately to distinguish these two aspects ofexplanation. Etiological explanations can and often do cite causes foran effect (e.g., smoking as the explanation for lung cancer) withoutdescribing the mechanism linking the cause to the effect. Even if thecausal relationship is sustained by a mechanism (as in Glennan 1996)and even if assumptions about those mechanisms are crucial forevaluating evidence about causal relations (Russo & Williamson2007; Illari 2011; Illari & Ruso 2014; seeSection 4.2), etiological explanations work by providing information about how theexplanandum is situated with respect to the antecedent causalstructure. And one might satisfy this requirement by describing asingle, salient difference-maker. Constitutive explanations, incontrast, are defined by the fact that they provide information aboutthe internal causal structure of the explanandum. The gas law does notexplain why balloons expand when heated; rather it says that theyalways do. Whether or not phenomenal laws are explanatory are dependson the explanandum and the contextually salient aspect of explanationunder request.

3.2 Limits of the CL Model of Constitutive Explanations

The key norms governing the practices of building, evaluating, andrevising constitutive mechanistic explanations are poorly reflected inthe rational reconstructions of the covering law model and the Umodel. A key advance in the work on constitutive mechanisticexplanation has been to make those norms explicit. These norms frameour discussion:

1. Purely Phenomenal Explanations. That apowder has a hallucinogenic virtue does not explain, but ratherredescribes its capacity to produce hallucinations. Constitutivemechanistic explanations of that phenomenon explain why the powdercauses hallucinations.
2. Relevance. Scientific explanationsinclude only relevant factors about how a mechanism works; theyexclude irrelevant factors.
3. Correctness. To know the explanationis to know how the mechanism actually works, not merely how it mighthave worked. What counts as correct enough varies with pragmaticcontext.
4. Completeness. Mechanistic explanationscan be more or less complete for different pragmatic ends. Scientificprogress often (but by no means invariably) involves filling in schematerms for phenomena with details of the underlying mechanism.
5. Intelligibility. Explanatory modelsought to induce understanding in consumers with the relevantbackground knowledge.

The CL model,as applied to laws of nature, arguably has difficulty with each of these norms (see Craver 2007a).A redescription with appropriate bridge laws might count as anexplanation according to that model (contranorm 1). The CL model requires that explanations must be true, satisfying thespirit ofnorm 2 but in a way which famously rules out the use of idealized models inexplanations. Just as the view supplies no account of causal relevance ( norm 3), it places no constraints on the relevance of variables appearing inconstitutive explanations (except that they should be in truegeneralizations). The CL model counts any sound argument that citestrue conditions and laws as a complete explanation; this fails toaccommodate the idea that constitutive explanations might be more orless complete for a given purpose (contranorm 4). The intelligibility of CL explanations (norm 5) lies in the inferential (logical) relation between explanans andexplanandum, but whether anyone understands the explanans and what ittells us about the phenomenon is (for better or worse) not a conditionon the explanation. Similar complaints have been raised against the Umodel, the empiricists’ nearest descendant.

Early work on constitutive mechanistic explanation was motivated bythese limitations in the CL mode and the U model. It was alsomotivated as an effort to fill in an important promissory note in themost prominent work on causal explanation (Salmon 1984; Woodward2003): namely, to analyze the constitutive aspect of causal-mechanicalexplanation,

3.3 Purely Phenomenal Models

The idea of a purely phenomenal model is familiar from the practice ofscientific modeling (Mauk 2000; Kay 2018), and has been broadlyadopted by mechanists (see, e.g., Bunge 1963; 1997; Craver 2007a,2010; Glennan 2002, 2005; Kaplan & Bechtel 2011; Kaplan &Hewitson 2021; Kaplan & Craver 2011). Craver and Kaplan (2020)offer this definition:

a model,M, is a phenomenal model of a mechanism if and onlyif it describes the inputs to, modulators of, and outputs from amechanism without describing its relevant internal causal structure.(2020: 296)

To distinguish purely phenomenal models from constitutive explanatorymodels, Kaplan and Craver (2011) describe a “model-to-mechanismmapping” (3M) requirement on constitutive explanations: for amodel of a mechanism to go beyond being a purely phenomenalredescription, it must reveal some details about the underlyingmechanism. Specifically:

In successful explanatory models in cognitive and systems neuroscience(a) the variables in the model correspond to components, activities,properties, and organizational features of the target mechanism thatproduces, maintains or underlies the phenomenon, and (b) the (perhapsmathematical) dependencies posited among these variables in the modelcorrespond to the (perhaps quantifiable) causal relations among thecomponents of the target mechanism. (Kaplan & Craver 2011:611)

This passage has been misread in many telling ways. It does not assertthat a model must describe all the details of a mechanism to count asexplanatory. Nor does it assert that a model that contains moredetails about a mechanism is always better or more explanatory than amodel that contains fewer details (the so-called “more detailsbetter” thesis as originally articulated by Levy & Bechtel2013 and repeated by Levy 2014; Chirimuuta 2014; Batterman & Rice2014; for a general discussion and clarification, see Craver &Kaplan 2020). The 3M requirement is nothing more or less than theinverse of the truism that a constitutive explanation must convey someinformation about the underlying mechanism (Craver & Kaplan 2020call this SDN: Some details are necessary). Third, 3M should not beread (see, e.g., Kaplan & Craver 2011: 609–610; Kaplan 2011:347), as denying the importance of idealized and abstract models.Whether it is compatible with the minimal models discussed by, e.g.,Batterman and Rice, and what this does or not entail for such putativeexplanations, is still an area of active interest (Povich 2018;2021).

Finally, 3M is consistent, as explained above, with the thesis thatso-called phenomenological laws might convey explanatory information,as in an etiological explanation. One can explain the gas’expansion by adverting to the fact that it was heated without goinginto the molecular theory of gasses. That is compatible with 3M andthe thesis that phenomenal models (as defined above) are explanatorilyempty as constitutive explanations for those phenomena (see Siegel& Craver 2024).

3.4 Constitutive Explanatory Relevance

A central task of a theory of causal (etiological) explanation is toexplicate the notion of causal and explanatory relevance by whichthings are included or excluded from explanations. (See entries oncounterfactual theories of causation,probabilistic theories of causation, andmanipulationist theories of causation.) An analogous problem confronts any theory of constitutiveexplanation; to sort relevant components from irrelevant parts (Craver2007a; Ylikoski 2013). A mechanism (S) exhibiting(responsible for) phenomenon \((\psi)\) is composed of many differententities (x), with various properties, engaging in myriadactivities \((\phi)\) organized together (seeFigure 1 above inSection 2). The philosophical puzzle is to articulate a principle by whichentities, activities, and organizational features that contribute tothe phenomenon are sorted from those that do not. The aim is toarticulate a theory of constitutive relevance.

3.4.1 Against Causation Across Levels of Mechanisms

Can the idea of causal relevance serve as a basis for explanatoryrelevance in both constitutive and etiological explanations? Thisoption, without further elaboration, faces some significant, perhapsinsurmountable, conceptual challenges.

The fact that the behavior of a mechanism is composed in some sense ofthe entities and activities of its parts, combined with certainfamiliar assumptions about causal relations, suggests that there canbe no causal relationships between items at different levels ofmechanisms (Lewis 1986; Kim 1992; Povich and Craver 2017). Why isinterlevel causation problematic for mechanistic levels? One source ofthe problem is that the putative interlevel causes and effects are notindependent of one another (Lewis 1986); they stand in compositionalrelations. And because they compose one another, the parts and thewholes change simultaneously (in apparent violation of the temporalasymmetry of causation) and symmetrically (in apparent violation ofcausal asymmetry of cause and effect) (as Kim1992 emphasizes; seeGebharter (2014, 2022) on how to translate the mechanistic approachinto a diachronic formalism of Causal Bayes nets that nonethelessadheres to the strictures presented here). Finally, if one understandsthe activities of parts as causally between the start and finish ofthe mechanism (see Prychitko 2021; Craver, Povich, & Glennan2021), the transformation that constitutes the whole is incomplete(i.e., does not yet exist) when the part changes, and the change tothe part comes after the portion of the whole that occurs before it(Craver, Glennan, and Povich 2021). For these reasons, causationacross levels of mechanisms is at best conceptually awkward.

How can one square this conceptual awkwardness with the fact that somany scientists seem to find the idea of top-down causationilluminating? Craver and Bechtel (2007) argue that claims assertinginterlevel causation are in fact hybrid propositions, combiningnon-causal claims about relations between part and whole with causalclaims expressing relations between things not related as part andwhole. Another, compatible, alternative is to take many sensibleclaims about interlevel causation to involve a different conception of“level” from mechanistic levels, such as levels of size,levels of theories, or levels of complexity, where these reasonsagainst part-whole causal relations do not (univocally) apply. See theentry onlevels of organization in biology.

3.4.2 Constitutive Relevance as Mutual Manipulability

What is constitutive relevance if not a causal relationship betweenwholes and parts? One important starting place has been the mutualmanipulability account (MM) of constitutive relevance (originallyformulated in Craver 2005, 2007a). Craver uses MM to articulate asufficient condition on constitutive relevance. The account was basedin the experimental manipulations used to test interlevel relations inpractice. According to this account, if it can be shown

1. that the putative component is spatially withinS,
2. that some ideal interventions on the putative component(x’s \(\phi\)-ing) changes the phenomenon(S’s \(\psi\)-ing), and
3. that some ideal interventions onS’s\(\psi\)-ing changex’s \(\phi\)-ing, that issufficient to establish thatx is a component in themechanism.

MM, however, has not proved entirely satisfying. First, it offers onlya sufficient condition; Craver (2005, 2007a), for example, discussesexamples of intuitively relevant parts that do not satisfy thecondition. Second, the use of interventionist counterfactuals inconditions(ii) and(iii) suggests a causal interpretation, contravening the explicit denial ofinterlevel causation (as argued by Baumgartner & Gebharter 2016;though see Krickel 2018a,b). Third, the notion of an“ideal” intervention, borrowed from Woodward’saccount of causal relevance, cannot apply (absent clarification) toconstitutive explanations. An ideal intervention on a system cannotintervene on both the independent and the dependent variable at thesame time. However, when one intervenes to makeS \(\psi\)(or to preventS from \(\psi\)-ing), one invariably alsointervenes on the components ofS’s \(\psi\)-ing. Andwhen one intervenes on the components ofS’s\(\psi\)-ing, one often intervenes onS’s \(\psi\)-ing.Becausex’s \(\phi\)-ing andS’s\(\psi\)-ing are related as part to whole, they are not independent,and so cannot be causally related, as just discussed (see Baumgartner2010, 2013; Leuridan 2012; yet see Menzies 2012; Woodward 2015).Finally, MM appears to presuppose the idea of being “containedwithin”; the boundaries of mechanisms are themselves determinedby considerations of relevance (Leuridan 2012). These criticismssuggest to many that MM, if it is coherent, is at best an account ofthe evidence by which claims of constitutive relevance are tested (seeCouch 2011; see also Romero 2015).

A path out of these problems, dimly suggested in Craver (2007a,b), hasbegun to emerge through the work of Menzies (2012), Harinen (2018),Krickel (2018a and 2018b) and Prychytko (2021), and, most recently,Craver, Glennan, and Povich (2021). Echoing work on causal mediation(see, e.g., Pearl 2014), Menzies suggests that a component is anecessary link in a causal chain between input and output. Building onthis work, Craver, Glennan, and Povich (2021) distinguish theepistemic aims of MM from a metaphysical theory of constitutiverelevance as causal betweenness. According to that view, MM (properlyformulated) works as an epistemic condition because the interlevelexperiments, in fact, are tests of different causal claims that poseno special problem of interpretation in the language of theinterventionist theory of causation (Craver, Glennan, & Povich2021).

A second approach involves a regularity account of constitutiverelevance modeled on Mackie’s notion of understanding a cause asan INUS condition: (Mackie 1980; see also Cummins 1983). On thisaccount, a constitutively relevant component is anInsufficient butNon-redundant partof anUnnecessary butSufficientmechanism for a given phenomenon (Couch 2011; 2023; see also Harbecke2010, 2015). As a theory with impeccable empiricist credentials, thisview looks good out of the gate. Its legs will be tested as a theoryof the metaphysics and in its ability to deal with mechanism tokensrather than types.

A third approach to constitutive relevance relies on the idea thatinterventions on the behavior of a mechanism as a whole are“fat-handed” with respect to the activities of thecomponents. An intervention is fat-handed to the extent that altersmore than one causally relevant variable at once (Eberhardt andScheines 2007). Romero (2015) , soon followed by Baumgartner andGebharter (2015) argued that interventions on the behavior of amechanism as a whole are invariably fat-handed with respect tointerventions on their parts. Baumgartner and Casini (2017) use thisidea to develop an “abductive theory” of constitution,where the conclusion that parts are constituents of a mechanism isarrived at abductively from recognition that phenomena and theirconstituents are unbreakably coupled via common causes. For criticismsof this account, see Krickel (2018a; 2018b).

This debate will bear fruit especially if it is pursued with mindfulrespect to the complex relationships (not identities) betweenquestions of epistemology, metaphysics, and scientific practice. Inattempting to articulate a regulative ideal for scientific explanationone might take it as a mark of success to bring these three aspects ofthe scientific endeavor, i.e., the world, our understanding of it, andour means of knowing it, together into a mutually satisfying pictureof the science of mechanisms.

3.5 Completeness and Correctness

Theories of explanation famously wrestle with puzzles specifying thenorms of completeness and correctness for explanations. Can one saythat an explanation should be correct without asserting that noidealized model is explanatory? Can one assess explanations for theircompleteness without insisting that abstract models (which necessarilydrop detail) must be non-explanatory or explanatorily deficient insome respect? These issues, while not unique to mechanisticexplanation, have been discussed with some vigor in the effort toclarify these norms in this mechanistic context.

At the heart of this discussion is a distinction between themechanisms themselves and the models (or explanatory texts) scientistsuse to represent them. Two subtly different problems of scientificexplanation, one concerning texts and the other concerning theirreferents, are apt to be conflated, leading to considerable confusion.Although the distinction is often characterized as between an“ontic” and an “epistemic” conception, thoseterms are not always used in the same way. Contrary to the senseintroduced by (Coffa 1974; Salmon 1989), the apparent fault line isbetween those who emphasize the ontic aspect of explanation (e.g.,Salmon 1989; Strevens 2013) and those who emphasizerepresentational aspects of explanation (see Bechtel &Abrahamsen 2005). This way of casting the matter, however, is somewhatmisleading, given that no defender of the ontic approach denies thatscientists use representations to convey explanations (as emphasizedby Illari 2013). Speaking in the ontic mode, one emphasizes theworldly relations to which a correct explanatory model would refer;speaking in the representational mode, one emphasizes instead the wayinformation about the worldly relations is being conveyed. These areentirely different topics.

It is also illuminating to note the important distinction between amodel that contains explanatorily relevant information about amechanism and a mechanistic model. A model of a mechanism, in a broadsense of “of”, might be any representation (physicalmodel, mathematical structure, etc.) produced or used to conveyinformation about a mechanism, i.e., its representational target. Seethe SEP entry onmodels in science). A mechanistic model, specifically, is a model of mechanism thatrepresents the component entities and activities and shows how theyact and interact with one another (see Glennan 2005). But a model orrepresentation of a mechanism might depict, for example, the evolutionof the states of the mechanism as a whole over time or merely featuresof its anatomical layout. They would not be mechanistic in this fullersense, though they do contain mechanically relevant information. Manymodels, mechanistic or not, are typically combined in the process ofgiving detailed mechanistic explanations (see Hochstein 2016,2017).

3.5.1 Completeness

Models represent intelligibly in virtue of abstracting, i.e., droppingdetail from, their mechanistic targets. It is a pragmatic matterwhether and when a model of a mechanism is “completeenough;” it depends on the aims and expectations for which themodel is constructed. Glennan (2005) explains that there is no hardline between complete and incomplete models; they are always evolving,through articulation and refinement of their commitments, andinvariably incomplete in some sense. That said, the effort to identifygaps in one’s understanding of a mechanism often drivesmechanistic research in practice.

Building on this simple idea, Darden distinguished mechanism schemasand mechanism sketches (Darden & Cain 1989; Darden 1996. Mechanismschemas are abstract descriptions of mechanisms that can be filled inwith details to yield a specific type or token mechanism. For example,the schema, “DNA → RNA → Protein” can be filledin with a specific sequence of bases in DNA, its complement in RNA,and a corresponding amino acid sequence in the protein; the arrows canbe filled in, showing how transcription and translation work. Amechanism sketch, in contrast, is an incomplete representation of amechanism that specifies some of the relevant entities, activities,and organizational features but leaves gaps that cannot yet be filled.Black boxes, question marks, and filler-terms (such as“activate”, “cause”, or“inhibitor”) are used in practice to hold the place forsome entity, activity or process yet to be discovered. The distinctionbetween sketches and schemas is perhaps too dichotomous to accommodatewhat is in fact always a matter of relative completeness andabstraction.

All models of mechanisms abstract away from potentially obfuscatingdetails (Craver & Darden 2013; Strevens 2008; Levy & Bechtel2013), even explanatorily relevant details. But surely to assert asmuch is already to commit oneself to the idea that there is more to beknown about the mechanism than the explanatory model expresses. Craverand Kaplan suggest one should let the mechanism itself serve as ameasure of completeness: the mechanism contains all and only therelevant parts, properties, activities, and organizational featuresfor the phenomenon in question. A complete model or family of models(should such a thing be desired, if possible, in practice) woulddescribe the mechanism in all its gory details (Craver & Kaplan(2020) call this idea “Salmon completeness”). Even thisinclusive model is restricted to relevant facts about the mechanism(weeding out irrelevancies). Note also that the effort to define andendpoint of completeness for this rather normative purpose does notcommit one to the idea that Salmon-incomplete models are useless,non-explanatory, or even necessarily deficient for the purposes athand. Such a view would arguably entail that we have never had anexplanation and never will, in principle.

Some object to what they see as the mechanist’s emphasis ondetail (Chirimuuta 2014; Levy & Bechtel 2013; Batterman & Rice2014; Weiskopf 2011; Woodward 2017). They associate mechanism with anobsessive focus on smallest details without regard to relevance (asense that is, indeed, common enough in science). Such“mechanism” is often missing the forest for the trees. Butthis objection appears to be directed at a common caricature of themechanist rather than at anything any new mechanist has asserted. Forrejoinders, see Povich (2017; 2018).

In assessments of completeness, it is perhaps better to ask questionsabout the amount of explanatory information a model contains thanabout whether the model is explanatory simpliciter. A model of amechanism has explanatory value, one might think, to the extent thatit contains information about how the mechanism actually works. It isa further question how much information that model contains and so howexplanatory that model is (cf. Lewis 1986 on causal explanation. Foran exploration of the sense of “information” involved insuch claims, see Povich 2021).

Why has there been any friction here at all? For many philosophers andscientists (including at least some of the authors of this essay), itincreasingly appears that science is often unproductively driven, andoften in the name of mechanism, by a self-sustaining need to deploythe latest fancy tools to deliver ever more detailed data about themicroscopic and often irrelevant aspects of a system. To recognizecompleteness as a norm can be seen as giving comfort to theseunproductive endeavors. Another possible source of this apparentconflict arises for those who deny that there is a fact of the matterabout what belongs in the set of all and only the relevant components.It might help, however, to think of completeness as a relative notion,definable without knowing an absolute endpoint.

3.5.2 Correctness

Just as there is a difference between understanding andmisunderstanding, there is a difference between explaining andapparently explaining. Explaining is what happens when apparentexplaining succeeds. In the case of mechanistic explanations, aconstitutive explanation for a phenomenon involves providinginformation about the components, properties, activities andorganizational features that actually produce, underlie, or maintainit. Apparent explanations can be produced by models that describeparts, properties, activities and organizational features that perhapscould exhibit the phenomenon but that do not actually produce,underlie, or maintain it.

To capture these common-sense ideas, mechanists emphasize thedistinction between a how-possibly schema and a how-actually-enoughschema (Craver & Darden 2013). A how-possibly schema describes howentities and activities might be organized to produce a phenomenon. Ahow-possibly model is a hypothesis about how the mechanism works. Suchmodels might be true (enough) or false. A true (enough) how-possiblymodel is (though we may not know it) also a how-actually (enough)model. A how-actually-enough schema describes how entities andactivities are in fact (or close enough) organized to produce thephenomenon. The term “how-actually-enough” captures theidea that the requisite “accuracy” of a mechanistic modelcan vary considerably from one pragmatic context to another (Weisberg2013). A false how possibly model (Dray 1968; Brandon 1984) is a falsehypothesis about how the mechanism works. (For a subtly differenttake, see Brainard 2020).

Some object to the idea of correctness on the grounds that models ofmechanisms often introduce idealizing assumptions to bring therelevant feature of the mechanism most clearly into view: infinitepopulations, frictionless planes, perfect geometrical shapes arepresumed in order to strip the model of detail that does not matterfor, or would only obstruct, the intended purposes of the model. Thisfact poses a significant problem for anyone who claims a model must betrue to be explanatory (such as the covering law model and somerepresentationalist theories). Those who emphasize the ontic aspect ofexplanation typically allow that idealized models convey informationabout features of the mechanism. A model might be correct in somerespects and not others. And how correct a model must be depends,again, on how the model is being used in practice (see Povich 2015;2018). Some mechanists and critics simply resist the idea that thereis a fact of the matter about how a mechanism works. Arguably, most ofthe recent work on mechanisms has been carried out against a backdropof tacit or explicit commitments to some form of perspectival realism(see Section 5 of the entry onscientific realism).

3.6 Intelligibility

A model that makes use of representational structures that theaudience is unprepared to comprehend will fail as a vehicle ofunderstanding. This banal observation has nothing to do withexplanation per se but with human communication more generally;communication fails when an audience is unequipped to digest themessage. We should therefore separate the intelligibility of the modelor textper se from the intelligibility of the model as arepresentation of an explanation specifically. Call this latterexplanatory intelligibility. Our question now: How do models rendermechanisms explanatorily intelligible?

The most obvious answer is perhaps: By providing amechanisticmodel: a model that represents the component entities andactivities and shows how they act and interact with one another suchthat they are responsible (etiologically, constitutively) for thephenomenon (see Glennan 2005). Many things contribute to theintelligibility of mechanistic models. They represent an orderlytemporal sequence of changes and so exhibit stages by which somethingunfolds. They depict these orderly changes as the result of componentforms of change that are taken antecedently as unproblematic or,perhaps, simpler (in the sense that they must be organized togetherwith other activities to produce the phenomenon) than the phenomenonitself. Third, they represent a kind of teleological perspective, inwhich an endpoint of the mechanism is achieved as a consequence of theactivities at earlier stages. Fourth, mechanistic models, whenpossible, represent spatial arrangements that explain why theactivities unfold as they do: things are inside or outside a membrane,things change conformations, things are fitted to one another in akind of joint productivity (Fagan 2012). One might say thatmechanistic models provide a species of narrative intelligibility (asGabe Siegel suggests, by personal communication) by which one comes tosee how an end-state is produced through the activities of actorsarranged in time and space.

But one can convey information about a mechanism with models that arenot “mechanistic” in this sense. Giving a mechanisticexplanation often involves combining information contained in manydifferent models, only some of which are mechanistic models (seeHochstein 2016) Dynamical models, for example, can be used to describethe temporal evolution of a phenomenon or the temporal arrangement ofa mechanism’s parts (Kaplan & Bechtel 2011; Kaplan 2011;Ross 2015). Network models can be used to describe, for example,spatial and causal information about the organization of parts (Levy2013, 2014; Levy & Bechtel 2013) or general topologicalconstraints on the behavior of the mechanism as a whole (see Huneman2010). Levy, in particular, emphasizes the role of abstraction onnetwork diagrams in generating abstract descriptions of higher-levelpatterns in the organization of complex systems, repeated motifs oforganization that behave in relevantly similar ways (Levy 2014) acrossvery different kinds of mechanisms. So-called minimal models show thata phenomenon results not from any particular arrangement of componentparts but rather from general constraints assumed to govern anyarrangement of the relevant kinds of component parts (see, e.g.,Batterman & Rice 2014; Povich 2018). Cascades and causal pathways(Ross 2021) are often used to convey information about possible canalsof causal influence and directions of change possible in a systemresponsible for many phenomena without describing any one set ofactual causal interactions responsible for a single phenomenon.

Hochstein (2016, 2017) argues that most explanations require manydifferent models, each of which provides a piece of explanatoryinformation that must be integrated with the others to understand knowhow the mechanism works. Because they make different idealizingassumptions and abstract away from different features of themechanism, any conjunction of all the models is bound to containcontradictions. The assumption of a one to one correspondence betweenmodels and explanations is a philosophical fiction belied byscientific practice. The idea that all models must be mechanistic toconvey information about the mechanism is a simple conflation. Whatintelligibility we recover in our quest to know the causal structureof the world often emerges at the intersection of different halftruths and outright lies, different kinds of models that tell usdifferent things about the parts, their interactions, and theirorganization across multiple levels of mechanistic organization. Or soadvocates of the ontic conception are prone to say on the topic ofintelligibility.

4. How Do Scientists Discover Mechanisms?

In contrast to those who would draw a hard line between discovery andjustification and assign reflections on justification to psychologistsand educational scholars (Reichenbach 1938; Popper 1959; Hanson 1958;Laudan 1980; and Nickles 1985; see also entries onscientific discovery,scientific method,Hans Reichenbach, andKarl Popper), the concept of mechanism has proved an important inroad into thinkingabout heuristics of scientific discovery (see esp. Bechtel &Richardson 1993 [2010]; Darden 2006; Craver & Darden 2013). Thisdiscussion, unfortunately, has remained fairly disconnected from theformal, computational literature on causal discovery and search(Spirtes, Clymour, & Scheines 1993 [2000]; Pearl 2009). Usefulwork can be directed at exploring what, precisely, each of theseapproaches to discovering the world’s causal structures has tooffer, their limits, and their possibilities of fruitful interaction(though see Gebharter 2017; Gebharter and Kaiser 2014). Recent work oncausal mediation affords another fruitful avenue of collaboration,especially in light of recent developments in understandingconstitutive relevance (see Weinberger 2019).

The material structure of a mechanism, even minimal mechanism, offersinroads in thinking concretely about how to reason one’s waythrough a scientific discovery problem. Historical exemplars ofmechanism discovery (e.g., Harvey’s circulation of the blood,Crick’s central dogma, Hodgkin and Huxley’s work on theaction potential) reveal a taxonomy of reasoning strategies at play indiscovering the mechanism for a phenomenon.

This orientation and approach trace to Bechtel and Richardson’sDiscovering Complexity (1993 [2010]), which is organizedaround a flowchart of choice-points in mechanism discovery. Theprocess of searching for mechanisms begins with a provisionalcharacterization of the phenomenon. Then follow strategies forlocalizing the mechanism within the system anddecomposing the phenomenon into distinct sub-functions. Theflowchart also contains various “exit ramps” for whathappens when these assumptions fail. Craver and Darden (2013) discussdecomposition as just one of a dozen or so strategies scientists useto address different questions in the search for mechanisms (see alsothe entries onphilosophy of cell biology andexperiment in biology).

4.1 Strategies of Discovery

Darden’s work on discovery strategies in Mendelian genetics(Darden 1986, 1991) expanded as she explored more specific strategiesof mechanism discovery (Darden 2006, 2009, 2018). Darden characterizesthe process of mechanism discovery as extended, iterative, andpiecemeal; it is composed of cycles of construction, evaluation, andrevision of mechanism schemas in light of theoretical and empiricalconstraints.

Construction strategies are strategies for generating new mechanismschemas. Darden shows that scientists oftenborrow a schematype from another area of science, as when selection-typemechanisms were borrowed to understand how the immune system works, orassemble a mechanism from known modules of functional activity(modular sub-assembly) (Darden 2006). Sometimes, scientistsknow one part of the mechanism and attempt to work forward(forward chaining) or backward (backward chaining)through to the other parts and activities. Far from beingphilosophically inscrutable, discovery proceeds as scientists use whatthey know about the working entities and activities in the mechanismto infer what could come next or before (Darden 2006; see also theentries for how this worked in the cases ofgenetics andmolecular biology). More formal approaches to causal discovery abstract from the materialdetails required to explicate this common form of reasoning in science(cf. Norton 2003).

Robotic simulation is a construction strategy, premised onthe idea that building something is a way of coming to understand itor of best demonstrating your understanding (e.g., Jacques Loeb 1912as described in Pauly 1987; Haugeland 1998; Dretske 1994). EdoardoDatteri discusses this robotic strategy at work in learning howsensorimotor mechanisms such as chemotaxis, phonotaxis, andhippocampal navigation contribute in ways that theoretical models ofthe same systems cannot (Datteri 2009; Datteri & Tamburrini2007).

Evaluation strategies involveconstraint-based reasoning toshape the space of possible mechanisms for a given phenomenon in lightof available evidence. Some of these involve discovering aspects of amechanism’s organization through observation; others involvemanipulating the mechanism with patterns of interventions. Forexample, one might intervene to remove a putative component to see ifand how the mechanism functions in its absence (inhibitoryexperiments). Or one mightstimulate that component to see ifone can drive the mechanism or modulate its behavior. Or one mightactivate a mechanism by placing it in the precipitatingconditions for the phenomenon and observe how the entity or activitychanges as the mechanism works. Craver (2007a) discusses these underthe heading of “interlevel experiments” (see also Harinen2018). Craver and Darden (2013) discuss many complex ways of arranginginterventions and detections to answer different questions about amechanism’s organization.

Revision strategies involve considering varieties of error and theirplausible sources: in the experimental apparatus, in data analysis,or, as in the case of “monster anomalies” and“special case” anomalies, in the target population itself.Possibilities for revision might involve adjusting any of the majorfeatures of a mechanism: the phenomenon, various parts, theiractivities, their organization; one often exploits the near-modularorganization of a mechanism to tweak one’s theory in one partwhile leaving much of the rest as it is.

The effort to describe such strategies, and their application toproblem areas in science, has been a productive area of research (see,for example, Bechtel 2009, 2019; Braillard 2015; Gervais & Weber2015; Zednik 2015; van Eck & Mennes 2018). Open projects includethe continued exploration of strategies at work in different areas ofscience, for different kinds of mechanisms, and to answer differentkinds of questions about them. Finally, as mentioned above, therelationship between these heuristics and causal discovery methods inthe computational sciences seems prime for exploration (see Spring& Illari 2019, and also the entry oncomputer simulation in science).

4.2 Mechanistic Evidence and Medical Discoveries

The concept of mechanism has become an important fulcrum in disputesin the philosophy of medicine that, in fact, ramify across themechanistic sciences (see the entries onphilosophy of medicine andphilosophy of biomedicine). One dispute concerns the relation between statistical and mechanisticevidence in the evaluation of a causal hypothesis. Mechanisticevidence is understood to be evidence about the mechanism’sparts and their diverse forms of organization. Is one more valuablethan the other? Is “mechanistic evidence” of minimalvalue, as indicated by its position on the hierarchy of evidence inevidence-based medicine? Or is “mechanistic evidence”required to render statistical forms of evidence reliable andmeaningful?

Thagard (1998, 2000) uses the discovery thatH. pyloribacteria cause ulcers as an exemplar for investigating how causal andmechanistic discoveries are made. Thagard draws attention to bothstatistical evidence that ulcers are associated withH.pylori and to mechanistic evidence that can explain how the agentof infection could persist in a hostile environment, to argue for thecausal hypothesis. Russo and Williamson argue that both types ofevidence are typically required to justify causal inference; thecorrelational evidence establishes that there is a difference-makingrelation between some cause and some effect, while the mechanisticevidence establishes how exactly the cause produces itseffect—the “Russo-Williamson Thesis” (Russo &Williamson 2007).

Philosophers have since refined this thesis, pointing out that“type of evidence” could refer to different methodologiesfor gathering evidence or to different objects of evidence.Difference-making methodologies include observational studies andrandomized controlled trials, while mechanistic methodologies mightinclude interventionist experiments or research involving non-humananimal models; likewise, the object of evidence could be the evidenceof an associated difference or it could be the evidence concerning themechanism linking the cause and effect (Illari 2011; Campaner 2011;Scholl 2013; Fiorentino & Dammann 2015; Vineis, Illari, &Russo 2017). Evidence-based medicine hierarchies, which rank differentkinds of evidence in terms of their epistemic strength, tend toprioritize evidence from difference-making methodologies (such asrandomized controlled trials and meta-analyses) over mechanisticevidence; in reply, these philosophers argue that the different typesof evidence are on a par (each with its own strengths and weaknesses)and advocate for integrating difference-making and mechanisticevidence, a sentiment which aligns with the emphasis on mechanismintegration discussed below (Clarke et al. 2013, 2014).

Steel (2008) focuses on strategies for extrapolating from a samplepopulation or a model organism to the structure of a mechanism in thetarget. Steel considers how researchers get around what he calls theextrapolator’s circle: determining how we could know that themodel and the target are similar in causally relevant respects withoutalready knowing the causal relationship in the target. Steel breaksthe extrapolator’s circle by developing a mechanisms-basedextrapolation strategy—the strategy ofcomparative processtracing. One need not compare the mechanism in its entirety, butcan find convincing evidence by targeting attention at keysimilarities. Craver (2022) uses this idea to argue for the relativesignificance of bottle-neck points or bow-tie structures in complexmechanisms (see entries onexperiment in biology andmolecular biology). For additional discussion, see Illari and Russo 2014; Darden, Pal,Kundu, and Moult 2018; Parkkinen et al. 2018; and Auker-Howlett &Wilde 2020.

5. Are Mechanisms Real?

Are mechanisms real? Are they objective bits of furniture in theworld? Does the world come “pre-carved” into mechanisms?Is there a causal structure of things independent of its registrationin the human mind? Not surprisingly, mechanists differ in theircommitments on these metaphysical questions, as they do on causation,constitution, relevance and a host of other specific matters. Yetbecause this question persists, it is worth surveying some things thatmechanists have said about the topic. In doing so, it becomes apparentthat the dominant thread of mechanistic research defends a rathernuanced and individually varying blend of the perspectival and theobjective. Here we lay out some key components of a view that deservesstatus as something like a default view.

The perspectival element of this default view appears most prominentlyin discussions of the importance of the phenomenon in framingmechanistic explanations, in discussions of functional explanation,and in discussions of the relationship between mechanisms and naturalkinds. The objective component of the default view, rooted in factsabout causation and constitution that are independent of humanperspective, only comes into view against the backdrop of theseperspectival determinants.

5.1 Mechanisms Are Defined Relative to Phenomena

A central claim across many areas of the mechanism literature is thatmechanisms are defined only relative to a phenomenon. Bechtel andRichardson (1993 [2010]) begin their decision-tree of mechanismdiscovery with the characterization of the phenomenon. Glennan (1996:52) writes, “One cannot even identify a mechanism without sayingwhat it is that the mechanism does”. More recently, Craver(2013: 141) writes, “In a slogan, mechanisms are the mechanismsof the things that they do”. This idea, which traces at least toStuart Kauffman’s discussion of articulation of partsexplanations, is now sometimes called “Glennan’sLaw” (see, e.g., Glennan 1996, 2002). On all these views,mechanisms are defined only relative to the phenomenon they cause,underlie, or otherwise explain (seeSection 2.2 point 8). Which phenomena we are interested in causing, creating, or explainingis surely a perspectival matter. So some kind of perspectival elementis presumed in most of the literature on mechanisms.

Notice, however, that the choice of phenomenon is not entirelyarbitrary. As Craver (2007a) notes, it has to exist (unlike the id orwitches), it has to have causal powers, it has to survive the kind ofscrutiny that scientific constructs routinely survive. Indeed, asBechtel and Richardson note, mechanism discovery typically involves“reconstituting the phenomenon”, shifting ourunderstanding of what we are trying to explain as we learn more aboutit and as we seek its putative explanation. We should not forget thatthe choice of which phenomena are appropriate targets for explanationis in part an empirical matter, a matter of how things have to go whenyou look at things more closely and try to explain them.

So at the level of phenomena, we find a mixture of perspectival andobjective constraints. Our explanatory and translational interestsfocus attention on key portions of the causal structure of the world.But it is possible for us to be objectively wrong about where we aredirecting our interests. Indeed, being wrong is probably common, asthe causal structure of the world is often very messy. Craver, forexample, tends to follow William James: it is “busy and buzzingconfusion” (Craver 2013: 140) or “blooming, buzzingconfusion” (Francken, Slors, & Craver 2022: 14). The task ofbuilding mechanistic explanations is, on his view, an effort to tamethat confusion, to make sense of that world in ways that areintellectually and pragmatically fruitful.

5.2 Dividing Mechanisms into Parts

The question of how the component entities and activities of amechanism are to be distinguished from one another is itself animmensely complicated issue that blends perspectival and objectiveelements. How do you chunk a mechanism into components, paragraphs ofactivity, non-overlapping enough with one another to constitutedistinct existences? Just as we can ask whether the world comespre-chunked into mechanisms, we can ask whether a mechanism comespre-chunked into parts.

To settle this matter, we need a principle to tell us when we have onepart and when we have two. And surely this chunking procedure that weperform will be guided by things that are relevant to us: whichchunkings render the working of the mechanism transparent to us, whichchunkings give us relatively simple generalizations we can deploy,which chunking best highlights the intended target of ourintervention. But again, the process is not without objectiveconstraint.

One source of constraint comes from patterns in the relevant portionof the causal structure of the world. If we envision that causalstructure as a network of causes, with nodes and edges, Simon argued,we can understand components as sub-networks in that structure made ofnodes that interact with one another more or more strongly than theydo with items outside that node (Simon 1969). Components are, in otherwords, to be identified as modules, in a sense that there now existalgorithms to detect (e.g., Fortunato 2010). Of course, one can decideto ignore the advice of the clustering algorithm, lumping things ittells you to split or splitting things it tells you to lump, but thepoint Simon emphasizes is that, in many systems, especially evolvedsystems, we should expect them to display nested hierarchies of nearlydecomposable components within nearly decomposable components.

There are theoretical and empirical reasons to believe that largeswathes of the world are multiply nearly decomposable, that the‘buzzing confusion’ can be chunked up in different usefulways. Simon (1969) gave us the parable of the watchmakers, Tempus andHora (seesection 2.1.5). Tempus builds nearly decomposable watches, and Hora builds holisticwatches with no neatly separable parts. When they are interrupted, asthey frequently are, Tempus loses all his work on that one part, butHora loses whatever progress she’s made on the watch as a wholeand must start from scratch. The analogy to evolution is indirect butpowerful (Steel 2008): systems with nearly decomposable organizationcan suffer damage to a part without global collapse; systems thatdevelop through the operation of nearly decomposable mechanisms cansustain interruptions to one part of that process while maintainingdevelopment around that failure; systems with nearly decomposablestructure afford opportunities for evolutionary tinkering thatholistic systems do not. Modular organization—in the sense ofnear decomposability—is precisely what we should expect inevolved systems or, more broadly, in systems that display robustnessin the face of changing environmental conditions. Moreover, that seemsto be what the sciences are telling us about the parts of the worldthey study (see, for example, Melo & Marroig 2015; Kashtan &Alon 2005; Kreimer et al. 2008; Wagner 1996). If one does attempt to“cut nature” at joints that fail to respect lines ofnear-decomposability, one is likely to have difficulty findingeconomical generalizations to express the composing relations. This isbecause chunking will crosscut thickets of interactions that rendertheir behavior intelligible to us most at their interfaces, where thevariables are relatively few in number. Are we forced to pay attentionto nature about these decisions? No. But we are well advised to do soas best we can. So while our perspectives are important, the causalstructure of the world plays a large part.

5.3 Functions

Mechanisms are often described in terms of functions. Sometimes, thephenomenon to be explained is some function performed by a mechanism.Sometimes the operations of the parts are described as functions, asin the “localization of function” in neuroscience. Yetwhat precisely this means varies considerably from one mechanist tothe next.

Sometimes the idea of function is understood in a more objectivesense. One way is to conceive of function as a distinctive kind ofetiology (à la Wimsatt 1974; L. Wright 1973; Neander1991). Garson (2013), for example, requires that a mechanism, in orderto be a mechanism for a phenomenon, must have been selected in somesense for having performed that phenomenon. Other mechanists seefunctional ascription, much like mechanistic description, as anorganizing principle for foregrounding some causal relations andbackgrounding others in a world of busy buzzing causal confusion.Following Cummins’ (1975) view of analytic explanations, Craver(2013) for example describes the attribution of functions as an“upward looking” effort to situate something in the causalstructure of the world, such as the intake valve in the behavior of anengine or the kidney in the context of the osmoregulatory system. Todescribe its role-function in this sense is simply to see it as partof a higher-level mechanism, to foreground the causal relationsrelevant to what that mechanism does and to background everythingelse. This is what Craver means when he says that,

Theoretical terms such as vesicle, neurotransmitter, receptor,channel, and ocular dominance column are conspicuously functional,describing entities not in terms of size, shape, and motion but interms of their job or role in the behavior of a system. (Craver 2013:134)

The idea of a role-function applies not only to biological and socialsystems but even to systems for which talk of natural selection isstrained at best; see Illari and Williamson (2012), who treatfunctions as “characteristic activities” in astrophysics.This growing use of Cummins’ account of role-functions isrecognized even by critical recent papers (e.g., Dewhurst & Isaac2023).

Both Craver and Cummins are committed to the idea that such functionaldescriptions are perspectival. One might see the kidney as a componentin blood pressure regulation and the valve as part of a noiseproduction mechanism, and this will lead one to foreground differentaspects and background those we previously found so interesting. Butnotice that, as with the case of phenomena, once we have decided whatwe find interesting, the facts pick up from there. For arole-functional description to be true, it has to capture how the itemis situated in that higher-level causal mechanism; this is anobjective matter, independent of whether anyone ever notices. And itimplies, straightforwardly, strong constraints on the content offunctional ascription and, so, on how such ascriptions can be and aretested in practice.

5.4 Kinds of Mechanisms and Mechanism Boundaries

A theme emerges from this philosophical work: the world has a causalstructure that places, however messily at times, objective constraintson the phenomenon, the decomposition of parts, and the descriptions ofthe roles things play, although we might choose to attend to differentcausal facts depending on our interests. Nothing in this, so far,tarnishes the objectivity of mechanisms. On these views, theboundaries of mechanisms, what is in and what is out, are determinedfundamentally by relevance to the phenomenon of the mechanism,although which phenomena we are interested in depends on pragmaticinterests. In the process of discovery, too, findings about parts andboundaries may lead us to reconstitute or recharacterize thephenomena. Yet even this view is consistent with the view that thereare phenomena and that, given those phenomena, there is an objectivefact about what things are and are not causally or constitutivelyrelevant to them.

Craver (2009) critically examines the idea that in the specialsciences one can identify kinds with the mechanisms underlyinghomeostatic property clusters, the HPC view of natural kinds (Boyd1991, 1999, 2000; Kornblith 1993; see also Tobin 2017, and the entryonnatural kinds). According to one, narrow understanding of that view, the similarityof property clusters is underwritten by the existence of a mechanismthat maintains their co-occurrence. Caver argues that there is nouniquely correct grain of abstraction saying when the mechanism is thesame or different enough for inclusion in the kind.

Craver develops earlier work into an explicitly perspectival view:

The pluralist will insist that the boundaries of kinds are notcompletely arbitrary—as radical constructivists might hold. Thelegitimate causal kinds have to respect the causal structure ofthings; but that causal structure can be described in many ways(abstracting more or less, and here rather than there) each yielding apossibly legitimate way of carving the taxonomy of kinds, depending onone’s needs. (Francken, Slors, & Craver 2022: 12)

In that paper, they describe a “cycle” of kinds,containing two hermeneutically circular loops that feed back on oneanother in the effort to define psychological kinds.

Glennan and Illari (2017a) take on the principles by which mechanismsare and can be sorted into kinds of mechanism. They argue there is notone single way, but rather multiple useful (and equally objectivelygrounded) ways of building principled taxonomies of mechanisms,including varieties of phenomena, varieties of entities, activitiesand interactions, varieties of organization and etiology. Thesedifferent ways of abstracting to types of mechanism, even for the samephenomenon, strongly indicate that whether two items are of the sametype will depend on what type we care about. But once we settle that,there would appear to be a further fact of the matter. Again,mechanisms show a curious blend of the perspectival and the objective,recognized in the mechanisms literature.

5.5 Grades of Perspectival Realism

Let us call this kind of blending of the perspectival and theobjective or realperspectival realism (following Giere 2006)to acknowledge the contributions of both to our understanding ofmechanisms. Different metaphysical presumptions, epistemologicalproclivities, and downright intuitions divide mechanists into how muchto weight these two aspects of the position.

One interesting possible difference among mechanists is how theyinterpret Glennan’s law. Strong versions of Glennan’s law,for example, seem to suggest that the determination of the phenomenon,by itself, determines what will or will not be in the mechanism.Craver (2009) seems to embrace this kind of strong view. And Dewhurstand Isaac (2023) seem to interpret Craver and other mechanists asembracing something like that direction of fit.

In other places, however, Craver (2007a) seems to acknowledge thehistorical fact that the characterization of the phenomenon and theunderstanding of the mechanism often co-evolve, as recognized alreadyby Bechtel and Richardson (1993 [2010]). That is, the conceptualentailment relationship between the phenomenon and the set of all andonly relevant components should not be taken to suggest anything aboutthe historical process by which one comes to understand a mechanism,where that understanding develops at two or more levels at once. AsChurchland (1993) argues, our understandings of what is going on atdifferent levels tend to “co-evolve” under mutualadjustment and calibration; how we think of parts and wholes remainsfluid over the course of a research program. Francken, Slors, andCraver (2022) add to this co-evolutionary process a consideration ofthe experimental methods for testing capacities and activities atdifferent levels of organization. We face a large hermeneutic circleprecisely because, in the final analysis, the mechanism must containall and only the relevant components in the phenomenon.

Some disagreement here appears to be verbal, about whether theabove-discussed intrusions of perspectivism into one’s accountof mechanisms is sufficient to defile any claim to realism. Others mayinsist that the degree of objective constraint on kinds of mechanism,their boundaries, and their components, is sufficient to warrant, atleast in many cases, a stronger commitment to a world that comes, insome sense, packaged into mechanisms. This brings us to the secondinteresting possible difference among mechanists: just how messy theythink the “blooming, buzzing confusion” of the causalstructure of the world is.

In early work, Glennan makes claims like:

These mechanisms are systems consisting of stable arrangements ofparts. In virtue of these arrangements, the systems as a whole havestable dispositions—the behaviors of these mechanisms. (Glennan2002: S345)

This might suggest that Glennan (2002) has stronger faith in a certaincausal stability in the world than Francken, Slors, and Craver (2022).Compare more recent work by Glennan and Illari (2017a), though.Following their acknowledgment that mechanism tokens don’t sortneatly into kinds, they continue,

However, the fact that there are many ways to carve up the world doesnot mean that there isn’t a world out there that constrains andmakes sense of our carvings. We think our account is consistent withwhat Mitchell calls a “pluralist realist approach to ontology,which suggests not that there are multiple worlds, but that there aremultiple correct ways to parse our world”. (Glennan & Illari2017a: 99; quoting Mitchell 2009: 13)

The idea of multiple correct ways might accord a slightly lesser roleto scientists’ interests than Craver does, but this does seem tobe a form of perspectival realism in the tradition of Giere(2006).

Bechtel contrasts with Glennan as he considers scientists’interests, and their cognitive and other epistemic capacities, asparamount, making claims such as, “Explanation is fundamentallyan epistemic activity performed by scientists” (Bechtel 2008:18), and, with Cory Wright, “explaining refers to aratiocinative practice governed by certain norms” (C. Wright& Bechtel 2007: 51).

Craver, Glennan, and Illari are keen to emphasize that the choicesthat scientists make are not arbitrary, but instead are stronglyconstrained by empirical work, emphasizing the engagement with theworld that goes into mechanism discovery. Bechtel of course does notthink scientists’ choices are arbitrary, and he is veryinterested in empirical work. However, he is particularly interestedin the constraints that scientists’ cognitive and otherepistemic capacities impose on mechanism discovery, and this naturallymakes him cautious about mechanism realism. So it seems that forBechtel, scientists’ (and human cognitive) capacities dominatein the story of characterizing and recharacterizing the phenomenon,and deciding on parts, boundaries and kinds of mechanisms. But notethat this is perfectly consistent with a broad perspectival realismthat simply takes the causal structure of the world to besignificantly more messy, and therefore less able to determine allthese choices, than Glennan (2002), and possibly even Francken, Slors,and Craver (2022), seem to think.

Ultimately, then, a perspectival realism broadly captures a range ofkey views in the mechanisms literature, that form at least a kind ofdefault view, albeit one that can be adopted, or rejected (see Buzzoni(2016) for critical discussion). Key mechanists who disagree stillshare quite a lot of substantive agreement, and their disagreementsare not entirely clear, but are quite nuanced, and take significantinterpretation of their work.

6. How Do Different Scientific Disciplines Relate to One Another?

Historically, the term “mechanism” has been associatedwith explanatory reductionism, in which the behavior of the whole isexplained in terms of the organization and activities of the parts. Incontemporary philosophy, however, the idea of mechanism has tendedrather to be associated with forms of explanatory and integrativeanti-reductionism and, for some, metaphysical antireductionism as well(seeSection 3). These anti-reductionist sentiments have at the core the idea thatmechanisms and mechanistic explanations typically span multiple levelsof organization, as introduced inSection 2.1.5.^[1]

6.1 Explanatory Reductionism

According to the CL model of explanation (e.g., Hempel 1965),reductive explanations involve explaining a derivative law in terms ofmore fundamental laws. In schematic form, such explanations involveidentifying the kind-terms of the higher-level laws with kind termsfrom the lower-level laws (using “bridge laws”) andshowing the higher-level laws to be a deductive consequence of thelower-level laws (see Schaffner 1993 for its fullest development; seethe SEP entry onScientific Reduction).

Mechanists argued that this model is too demanding in some respectsand too lax in others. It is too demanding, first, in its reliance onthe idea of “laws of nature”, which many have argued arescarce in the special sciences, perhaps because such language is inaptfor describing a world with variation, equifinality, and frailty(Beatty 1995; Mitchell 1997, 2000; Woodward 2003). It is toodemanding, second, in requiring that the explanation be an argument.Few if any actual interlevel explanations can be spelled out asarguments, and, in actual science, considerable refinement andrestriction are required to bring the two sets of laws into sufficientalignment to allow anything like a derivation to go through. Evenadvocates of the CL model of reduction (e.g., Schaffner 1993;Churchland 1986) argued for this reason that reduction is“peripheral” to explanations in scientific practice, atbest a regulative ideal or, worse, a mopping-up exercise to beperformed after the difficult work of building the lower-levelexplanation is complete.

Others argued that the CL model of downward-looking explanations istoo lax to capture the norms scientists seem to enforce in evaluatingexplanations. In failing to mark adequately the difference betweenaccidental regularities and causes, the constraints on the CL model ofreduction admit explanations involving mere correlations and causallyirrelevant generalizations. They also admit explanations that involveonly non-explanatory re-descriptions.

The mechanistic alternative is that downward-looking explanationsprovide information about the parts and activities underlying theexplanandum phenomenon. This view of downward-looking explanationeschews the law requirement and the argument requirement in the CLmodel. It is designed instead to reflect the goals of explanation asrevealed in the investigative practices of scientists (see Craver2007a).

Finally, new mechanists almost universally defend talk of causes andexplanations at higher levels of organization as scientificallylegitimate and necessary (see, e.g., Craver 2007a; Glennan 2010; 2017;Krickel 2018 a,b). Specifically, they argue that there are truedifference-making relations at higher levels that are not true of therelations among component parts. Glennan argues that these truths holdin virtue of lower-level mechanisms.

6.2 Ontological Reductionism

It is not universally agreed that one can separate issues ofexplanatory reduction from issues of ontological reduction. Salmon(1989) advocated for an ontic view of explanation, and it has beenendorsed by many champions of causal (e.g., Strevens 2008, 2013),mechanistic (e.g., Craver 2007a; Hochstein 2016), and counterfactual(Povich 2017) explanation. On that view, the aim is to characterizethe ontological referent of explanatory texts and models. Others (suchas Bechtel & Abrahamsen 2005), think that norms of explanatortexts, such as intelligibility, should drive one’s ontologicalcommitments.

One tradition of broadly mechanistic thought (including thinkers suchas Simon 1969; Cummins 1983; Lycan 1990) has argued that theorganization of parts gives rise to higher-level phenomena that arequite literally more than simple sums of their parts. Wimsatt, forexample, distinguishes aggregates, simple sums of the properties oftype identical components (as the mass of a pile of sand is a sum ofthe masses of the individual grains), from systems in which theorganization of the components matters. In aggregates, as opposed tosystems, the parts can be intersubstituted for one another withoutchanging the property of the whole; they can be disaggregated andreaggregated, restoring the property of the whole; the parts do notinteract with one another in a way that is relevant to the property ofthe whole; and the spatial and temporal organization of the parts isirrelevant to the property of the whole. These things are all falsefor systems (non-aggregates), explaining why they have properties thatare, literally, more than a simple sum of the parts (see by Bechtel2013; Craver 2007a). (Note that Kim 1998, a prominent critic ofnon-reductive physicalism, describes this point as both obvious andimportant for understanding the structure of the world.)

If higher-level capacities and causings are (token- or type-)identified with their lower-level realizers, the mechanistic defenseof higher-level causings is arguably a form of reductive functionalism(Polger 2004, 2010). In a series of articles, Gillett (2002a,b,c),Melnyk (2003), and Piccinini and Craver (2011) argue that mechanisticreduction is compatible with realism about higher-level causes andindeed an important part of their legitimacy as higher level causes(see, for examples, the contributors to the metaphysical sections ofSchouten and Looren de Jong’s (2007; See Piccinini (2020) for amore recent exploration of these issues.

Some have offered mechanistic accounts of the nature of theconstitution relation. Harbecke (2015) defends a regularity theory ofconstitution, for example, and shows that many of the norms forevaluating mechanistic explanation make sense in light of that notion.Carl Gillett (2002a,b) distinguishes dimensioned and flat views of therealization relation, noting that the former involves the realizationof properties of wholes by properties of the parts, of whichmechanistic realization is a species (though see Polger 2010 for aresponse and Couch 2023 for further development).

Discussions of the legitimacy of higher-level causes (includingdebates about the causal closure of the physical and causaloverdetermination) often turn on metaphysical commitments that varyconsiderably among not just philosophers but mechanists specifically.Some mechanists emphasize the explanatory power of only cellular andmolecular phenomena (e.g., Bickle 2003, 2020) and defend a world oflowest-level causes. Others, such as Craver, Bechtel (2008), Glennan(2010), and Gillett (2002c), defend a world of higher-levelcauses.

6.2.1 Mechanism, Reduction, and Emergence

The relationship between mechanism and emergence is hard to specify,given varying definitions of emergence (see the entry onemergence) and varying metaphysical commitments among mechanists. Yet a fewremarks are in order. (See Gillett 2002b for a useful review.)

First, for many people, emergence is defined by the whole being morethan the sum of its parts. Because mechanisms are non-aggregative, allof them exhibit emergence in this weak sense.Mechanistic (ororganizational) emergence thus understood is ubiquitous andextremely important for understanding both how scientists explainthings and how the domains of the special sciences are related to oneanother.

Also familiar and uncontroversial isepistemic emergence, theinability to predict the properties or behaviors of wholes fromproperties and behaviors of the parts. Epistemic emergence can ariseas a result of ignorance, such as failing to recognize a relevantvariable, or from failing to know how different variables interact incomplex networks. It might also result from limitations on humancognitive abilities or in current-generation representational tools(Bedau 1997; Boogerd, Bruggeman, Richardson, et al. 2005; Richardson& Stephan 2007). The practical necessity of studying mechanisms bydecomposing them into component parts raises the epistemic challengeof putting the parts back together again in a way that actually works(Bechtel 2013); epistemic emergence might stand in the way.

Mechanistic emergence and epistemic emergence contrast with spookyemergence (Richardson & Stephan 2007), cases in which newproperties exist with no sufficient basis in constitutive mechanisms.It is not clear that emergent properties in this sense are properlysaid to be propertiesof the mechanisms at all; and it is notclear in what sense the emergent property is “emergent”rather than simply an effect of a cause. Spooky emergence is oftenconjoined with thoughts of top-down and bottom-up causation, ideasthat are hard to square with mechanistic notions of levels (seesection 3.4.2) and that are, in fact, mundane and unexciting whenapplied to, e.g., levels of size or levels of scientific fields. Inshort, such forms of spooky emergence are altogether distinct from,and so gain no plausibility from, verbal association with,organizational/mechanistic and epistemic emergence.

6.2.2 Mechanism and Realization

Many philosophers now believe that the requirement of type-identitybuilt into the classical model of reduction does not accommodateordinary cases in which higher-level capacities and properties aremultiply realized in their lower-level realizers. What is the idea ofmechanistic realization?

Some scholars have found the notion of mechanism helpful as a way offleshing out the ontological relationship of (a kind of) realization.One important distinction is between flat and dimensioned views ofrealization: According to the “flat view” (Gillett 2002aand 2002b), realization is a relationship between different propertiesof one and the same thing (Kim 1998). The subset view, which holdsthat a property P1 (e.g., mean kinetic energy of the gas) realizesproperty P2 (e.g., temperature of the gas) when the causal powersdistinctive of P2 (temperature) are a subset of the causal powersdistinctive of P2 (mean kinetic energy), is an example of the flatview. P1 and P2 are both attributed to the same thing, the gas(Gillett 2002a,b). The dimensioned view describes realization as arelationship holding between the properties of wholes and theproperties of the parts and their organization. This view ofrealization comports with the explanatory aims of the special sciencesand fits nicely with the evidential base on which interlevel claimsare grounded (see Aizawa & Gillett 2009. Gillett has sinceexpanded this notion to handle the realization of objects, properties,and processes (Gillett 2013); for criticism and alternatives, seePolger 2010; Melnyk 2003, 2010).

6.3 Interfield integration and Integrative Pluralism

What does mechanism add to discussions about the unity of science thatfueled discussions of reductionism in the philosophy of science? Ifone rejects a tidy correspondence between levels, theories, and fieldsof science (as mechanists are apt to do; see Darden 2006; Craver2007a; Craver & Darden 2013), and replaces it with only a set oflocal, multilevel structures framed by reference to a top-mostexplanandum phenomenon, an alternative picture of scientificintegration emerges.

On the mechanistic account, integration is not a relationship betweentheories but a relationship within single mechanistic theories (Darden& Maull 1977). And these theories are not merely abstract, formalstructures composed of logical relations; instead they are furtherstructured by material relations such as causation, part-and-whole,spatial containment, temporal succession, and geometric arrangement.These material commitments allow one to provide an account of levelsand to highlight the kinds of evidence by which interfieldintegrations are evaluated. Furthermore, while classical reduction isexclusively focused on downward-looking explanations, the effort tobuild multilevel mechanistic explanations requires one to look outwardand upward as well to see how phenomena at many different levels arerelated to one another (Bechtel 2009; Craver 2007a).

The abstract structure of a multilevel mechanism introduced inSection 2.1.5 becomes a scaffold onto and around which the findings of diversefields can converge in the service of building a mechanisticexplanation (see Bechtel 1988; Glennan 2017) or, perhaps moreappropriately, a cluster of overlapping and related mechanisticexplanations (e.g., wild type and mutant; healthy and diseased) (seeDarden 2006; Spring & Illari 2019). Case-studies in suchmechanistic integration include Darden’s (2005) extendeddiscussion of the relation between Mendelian and molecular genetics,Bechtel’s (2006) studies of cell biology and fermentation,Bechtel and Richardson’s (1993) exploration of localization offunction in neuroscience and cell biology, Craver’s studies ofthe action potential and the neurobiology of learning and memory(2005; 2007), and Ylikoski’s studies with colleagues ofmechanistic explanations in the social sciences (Hedstrom &Ylikoski 2011; Ylikoski 2017).

The mechanistic perspective exhibits the kind of integrative pluralismMitchell (2003, 2009) advocates for the special sciences. Theconstruction of mechanistic explanations is collaborative andpiecemeal, adding incremental constraints to an developing picture ofhow a mechanism works at a level and across levels, as well as at atime and across times (Craver & Darden 2013). The many scientificdisciplines that investigate a phenomenon co-exist and co-inform oneanother by integratively contributing constraints upon theetiological, constitutive, and contextual mechanistic explanations ofthat phenomenon (Bechtel 2009; Tabery 2014).

Although some mechanists have tended to emphasize the locality of suchintegrations as occurring within the search for a particularexplanation, mechanistic integration across a cluster of relatedmechanisms (Darden 2006; Illari 2019) might offer a kind of wide-scopeunity, expanding beyond the confines of any particular mechanism toprovide quite general explanations types of phenomena.

7. Conclusion

The past two decades have seen truly explosive growth in therecognition of the centrality of mechanisms to our contemporaryconception of science. The term, however, continues to function as anorganizational concept onto which our greatest hopes and fears ofscience can be projected. It is the object of our collective quest,the wellspring of translational knowledge and understanding. At thesame time, it embodies our deepest assumptions about the nature ofexplanation, the aims of science, the constraints on acceptablesolutions to scientific problems. it is therefore a lightning rod fordispute over what science ought to be, what it ought to aim toachieve, and how it most efficiently can achieve that. Philosophicalreflection on mechanism arose in response to the need to engage thatdiscussion in a language appropriate to the practice of science. Andnow that it has been said, it is difficult indeed to think aboutscience without thinking about mechanisms.

Bibliography

• Aizawa, Kenneth, and Carl Gillett, 2009, “The (Multiple)Realization of Psychological and Other Properties in theSciences”,Mind & Language, 24(2): 181–208.doi:10.1111/j.1468-0017.2008.01359.x
• Allen, Garland E., 2005, “Mechanism, Vitalism and Organicismin Late Nineteenth and Twentieth-Century Biology: The Importance ofHistorical Context”,Studies in History and Philosophy ofScience Part C: Studies in History and Philosophy of Biological andBiomedical Sciences, 36(2): 261–283.doi:10.1016/j.shpsc.2005.03.003
• Alon, Uri, 2007,An Introduction to Systems Biology: DesignPrinciples of Biological Circuits (Mathematical and ComputationalBiology Series 10), Boca Raton, FL: Chapman & Hall/CRC.
• Andersen, Holly K., 2011, “Mechanisms, Laws, andRegularities”,Philosophy of Science, 78(2):325–331. doi:10.1086/659229
• –––, 2012, “The Case for Regularity inMechanistic Causal Explanation”,Synthese, 189(3):415–432. doi:10.1007/s11229-011-9965-x
• –––, 2014a, “A Field Guide to Mechanisms:Part I”,Philosophy Compass, 9(4): 274–283.doi:10.1111/phc3.12119
• –––, 2014b, “A Field Guide to Mechanisms:Part II”,Philosophy Compass, 9(4): 284–293.doi:10.1111/phc3.12118
• Anscombe, G. E. M., 1971 [1993],Causality and Determination:An Inaugural Lecture, Cambridge: Cambridge University Press.Reprinted as “Causality and Determination”, inCausation, Ernest Sosa and Michael Tooley (eds), Oxford/NewYork: Oxford University Press, 1993, 88–104.
• Auker‐Howlett, Daniel and Michael Wilde, 2020,“Reinforced Reasoning in Medicine”,Journal ofEvaluation in Clinical Practice, 26(2): 458–464.doi:10.1111/jep.13269
• Ayer, Alfred J., 1936,Language, Truth, and Logic,London: Gollancz, 2nd Edition, 1946.
• Batterman, Robert W. and Collin C. Rice, 2014, “MinimalModel Explanations”,Philosophy of Science, 81(3):349–376. doi:10.1086/676677
• Baumgartner, Michael, 2010, “Interventionism andEpiphenomenalism”,Canadian Journal of Philosophy,40(3): 359–383. doi:10.1080/00455091.2010.10716727
• –––, 2013, “Rendering Interventionism andNon-Reductive Physicalism Compatible: Rendering Interventionism andNon-Reductive Physicalism Compatible”,Dialectica,67(1): 1–27. doi:10.1111/1746-8361.12008
• Baumgartner, Michael and Lorenzo Casini, 2017, “An AbductiveTheory of Constitution”,Philosophy of Science, 84(2):214–233. doi:10.1086/690716
• Baumgartner, Michael and Alexander Gebharter, 2016,“Constitutive Relevance, Mutual Manipulability, andFat-Handedness”,The British Journal for the Philosophy ofScience, 67(3): 731–756. doi:10.1093/bjps/axv003
• Beatty, John, 1995, “The Evolutionary ContingencyThesis”, inConcepts, Theories, and Rationality in theBiological Sciences: The Second Pittsburgh-Konstanz Colloquium in thePhilosophy of Science, University of Pittsburgh, October 1–4,1993 (Pittsburgh-Konstanz Series in the Philosophy and History ofScience), Gereon Wolters, James G. Lennox, and Peter McLaughlin (eds),Konstanz : Pittsburgh, Pa: UVK, UniversitätsverlagKonstanz; University of Pittsburgh Press, 45–81 (ch. 2).
• Bechtel, William, 1988,Philosophy of Science: An Overview forCognitive Science (Tutorial Essays in Cognitive Science),Hillsdale, NJ: L. Erlbaum.
• –––, 2006,Discovering Cell Mechanisms: TheCreation of Modern Cell Biology (Cambridge Studies in Philosophyand Biology), New York: Cambridge University Press.doi:10.1017/CBO9781139164962
• –––, 2008,Mental Mechanisms: PhilosophicalPerspectives on Cognitive Neuroscience, New York: Routledge.doi:10.4324/9780203810095
• –––, 2009, “Looking Down, Around, and Up:Mechanistic Explanation in Psychology”,PhilosophicalPsychology, 22(5): 543–564.doi:10.1080/09515080903238948
• –––, 2011, “Mechanism and BiologicalExplanation”,Philosophy of Science, 78(4):533–557. doi:10.1086/661513
• –––, 2013, “Addressing theVitalist’s Challenge to Mechanistic Science: Dynamic MechanisticExplanation”, inVitalism and the Scientific Image inPost-Enlightenment Life Science, 1800–2010 (History,Philosophy and Theory of the Life Sciences 2), Sebastian Normandin andCharles T. Wolfe (eds), Dordrecht/New York: Springer, 345–370.doi:10.1007/978-94-007-2445-7_14
• –––, 2019, “Analysing Network Models toMake Discoveries about Biological Mechanisms”,The BritishJournal for the Philosophy of Science, 70(2): 459–484.doi:10.1093/bjps/axx051
• –––, 2022, “Levels in BiologicalOrganisms: Hierarchy of Production Mechanisms, Heterarchy of ControlMechanisms”,The Monist, 105(2): 156–174.doi:10.1093/monist/onab029
• Bechtel, William and Adele A. Abrahamsen, 2005,“Explanation: A Mechanist Alternative”,Studies inHistory and Philosophy of Science Part C: Studies in History andPhilosophy of Biological and Biomedical Sciences, 36(2):421–441. doi:10.1016/j.shpsc.2005.03.010
• –––, 2013, “Thinking Dynamically AboutBiological Mechanisms: Networks of Coupled Oscillators”,Foundations of Science, 18(4): 707–723.doi:10.1007/s10699-012-9301-z
• Bechtel, William and Robert C. Richardson, 1993 [2010],Discovering Complexity: Decomposition and Localization asStrategies in Scientific Research, Princeton, NJ: PrincetonUniversity Press. Reprinted Cambridge, MA: The MIT Press, 2010.doi:10.7551/mitpress/8328.001.0001
• Bedau, Mark A., 1997, “Weak emergence”,Philosophical Perspectives, 11: 375–399.
• Bertolaso, Marta and Marco Buzzoni, 2017, “Causality andLevels of Explanation in Biology”, inPhilosophical andScientific Perspectives on Downward Causation, Michele PaoliniPaoletti and Francesco Orilia (eds), New York: Routledge,164–179.
• Bich, Leonardo and William Bechtel, 2022, “OrganizationNeeds Organization: Understanding Integrated Control in LivingOrganisms”,Studies in History and Philosophy ofScience, 93: 96–106. doi:10.1016/j.shpsa.2022.03.005
• Bickle, John, 2003,Philosophy and Neuroscience: A RuthlesslyReductive Account (Studies in Brain and Mind 2), Dordrecht:Springer Netherlands. doi:10.1007/978-94-010-0237-0
• –––, 2020, “Laser Lights and DesignerDrugs: New Techniques for Descending Levels of Mechanisms ‘in aSingle Bound’?”,Topics in Cognitive Science,12(4): 1241–1256. doi:10.1111/tops.12452
• Boas, Marie, 1952, “The Establishment of the MechanicalPhilosophy”,Osiris, 10: 412–541.
• Bogen, Jim, 2005, “Regularities and Causality;Generalizations and Causal Explanations”,Studies in Historyand Philosophy of Science Part C: Studies in History and Philosophy ofBiological and Biomedical Sciences, 36(2): 397–420.doi:10.1016/j.shpsc.2005.03.009
• –––, 2008, “The Hodgkin-Huxley Equationsand the Concrete Model: Comments on Craver, Schaffner, andWeber”,Philosophy of Science, 75(5): 1034–1046.doi:10.1086/594544
• Boogerd, Fred C., Frank J. Bruggeman, and Robert C. Richardson,2013, “Mechanistic Explanations and Models in Molecular SystemsBiology”,Foundations of Science, 18(4): 725–744.doi:10.1007/s10699-012-9302-y
• Boogerd, Fred C., Frank J. Bruggeman, Robert C. Richardson, AchimStephan, and Hans V. Westerhoff, 2005, “Emergence and Its Placein Nature: A Case Study of Biochemical Networks”,Synthese, 145(1): 131–164.doi:10.1007/s11229-004-4421-9
• Boyd, Richard, 1991, “Realism, Anti-Foundationalism and theEnthusiasm for Natural Kinds”,Philosophical Studies,61(1–2): 127–148. doi:10.1007/BF00385837
• –––, 1999, “Homeostasis, Species, andHigher Taxa”, inSpecies: New Interdisciplinary Essays,Robert A. Wilson (ed.), Cambridge, MA: The MIT Press, 141–186.doi:10.7551/mitpress/6396.003.0012
• –––, 2000, “Kinds as the‘Workmanship of Men’: Realism, Constructivism, and NaturalKinds”, inRationalität, Realismus, Revision /Rationality, Realism, Revision: Proceedings of the 3rd InternationalCongress of the Society for Analytical Philosophy September15–18, 1997 in Munich, Julian Nida-Rümelin (ed.),Berlin/New York: De Gruyter, 52–89.doi:10.1515/9783110805703.52
• Braillard, Pierre-Alain, 2015, “Prospect and Limits ofExplaining Biological Systems in Engineering Terms”, inBraillard and Malaterre 2015: 319–344. doi:10.1007/978-94-017-9822-8_14
• Braillard, Pierre-Alain and Christophe Malaterre(eds), 2015,Explanation in Biology: An Enquiry into the Diversityof Explanatory Patterns in the Life Sciences (History, Philosophyand Theory of the Life Sciences 11), Dordrecht: Springer.doi:10.1007/978-94-017-9822-8
• Brainard, Lindsay, 2020, “How to ExplainHow-Possibly”,Philosophers’ Imprint, 20(13):1–23.
• Brandon, Robert N., 1984, “Grene on Mechanism andReductionism: More Than Just a Side Issue”,PSA: Proceedingsof the Biennial Meeting of the Philosophy of Science Association,1984(2): 344–353.doi:10.1086/psaprocbienmeetp.1984.2.192513
• Bromberger, Sylvain, 1966, “Why-questions”, in RobertGarland Colodny (ed.),Mind and Cosmos: Essays in ContemporaryScience and Philosophy, Pittsburgh: University of PittsburghPress, pp. 86–11.
• Brooks, Daniel S., 2017, “In Defense of Levels: Layer Cakesand Guilt by Association”,Biological Theory, 12(3):142–156. doi:10.1007/s13752-017-0272-8
• Bunge, Mario, 1963, “A General Black Box Theory”,Philosophy of Science, 30(4): 346–358.
• –––, 1997, “Mechanism andExplanation”,Philosophy of the Social Sciences, 27(4):410–465.
• Buzzoni, Marco, 2016, “Mechanisms, Experiments, andTheory-Ladenness: A Realist–Perspectivalist View”,Axiomathes, 26(4): 411–427.doi:10.1007/s10516-016-9301-7
• Campaner, Raffaella, 2011, “Understanding Mechanisms in theHealth Sciences”,Theoretical Medicine and Bioethics,32(1): 5–17. doi:10.1007/s11017-010-9166-5
• Cartwright, Nancy, 1989,Nature’s Capacities and TheirMeasurement, Oxford: Clarendon.doi:10.1093/0198235070.001.0001
• Chao, Hsiang-Ke, Szu-Ting Chen, and Roberta L.Millstein (eds), 2013,Mechanism and Causality in Biology andEconomics (History, Philosophy and Theory of the Life Sciences3), Dordrecht/London: Springer. doi:10.1007/978-94-007-2454-9
• Chemero, Anthony and Michael Silberstein, 2008, “After thePhilosophy of Mind: Replacing Scholasticism with Science”,Philosophy of Science, 75(1): 1–27.doi:10.1086/587820
• Chirimuuta, Mazviit, 2014, “Minimal Models and CanonicalNeural Computations: The Distinctness of Computational Explanation inNeuroscience”,Synthese, 191(2): 127–153.doi:10.1007/s11229-013-0369-y
• Churchland, Patricia Smith, 1986,Neurophilosophy: Toward aUnified Science of the Mind-Brain (Computational Models ofCognition and Perception), Cambridge, MA: MIT Press.doi:10.7551/mitpress/4952.001.0001
• –––, 1993, “The Co-Evolutionary ResearchIdeology”, inReadings in Philosophy and CognitiveScience, Alvin I. Goldman (ed.), Cambridge, MA: The MIT Press,745–768 (ch. 34). doi:10.7551/mitpress/5782.003.0043
• Clarke, Brendan, Donald Gillies, Phyllis Illari, Federica Russo,and Jon Williamson, 2013, “The Evidence That Evidence-BasedMedicine Omits”,Preventive Medicine, 57(6):745–747. doi:10.1016/j.ypmed.2012.10.020
• –––, 2014, “Mechanisms and the EvidenceHierarchy”,Topoi, 33(2): 339–360.doi:10.1007/s11245-013-9220-9
• Coffa, J. A., 1974, “Hempel’s ambiguity”,Synthese, 28(2): 141–163.
• Colaço, David, 2020, “Recharacterizing ScientificPhenomena”,European Journal for Philosophy of Science,10(2): article 14. doi:10.1007/s13194-020-0279-z
• Comte, Auguste, 1830 [1970],Cours de PhilosophiePositive, 6 vols., Paris: Bachelier. Selections translated asIntroduction to Positive Philosophy, Frederick Ferré(trans.), Indianapolis, IN: Bobbs-Merrill, 1970.
• Couch, Mark B., 2011, “Mechanisms and ConstitutiveRelevance”,Synthese, 183(3): 375–388.doi:10.1007/s11229-011-9882-z
• –––, 2023, “Clarifying the RelationBetween Mechanistic Explanations and Reductionism”,Frontiers in Psychology, 14: 984949.doi:10.3389/fpsyg.2023.984949.
• Craver, Carl F., 2001, “Role Functions, Mechanisms, andHierarchy”,Philosophy of Science, 68(1): 53–74.doi:10.1086/392866
• –––, 2005, “Beyond Reduction: Mechanisms,Multifield Integration and the Unity of Neuroscience”,Studies in History and Philosophy of Science Part C: Studies inHistory and Philosophy of Biological and Biomedical Sciences,36(2): 373–395. doi:10.1016/j.shpsc.2005.03.008
• –––, 2007a,Explaining the Brain: Mechanismsand the Mosaic Unity of Neuroscience, Oxford: Clarendon Press.doi:10.1093/acprof:oso/9780199299317.001.0001
• –––, 2007b, “Constitutive ExplanatoryRelevance”,Journal of Philosophical Research, 32:3–20. doi:10.5840/jpr20073241
• –––, 2008, “Physical Law and MechanisticExplanation in the Hodgkin and Huxley Model of the ActionPotential”,Philosophy of Science, 75(5):1022–1033. doi:10.1086/594543
• –––, 2009, “Mechanisms and NaturalKinds”,Philosophical Psychology, 22(5): 575–594.doi:10.1080/09515080903238930
• –––, 2010, “Prosthetic Models”,Philosophy of Science, 77(5): 840–851.doi:10.1086/656822
• –––, 2013, “Functions and Mechanisms: APerspectivalist View”, inFunctions: Selection andMechanisms (Synthese Library 363), Philippe Huneman (ed.),Dordrecht: Springer, 133–158.doi:10.1007/978-94-007-5304-4_8
• –––, 2014, “The Ontic Account ofScientific Explanation”, inKaiser, Scholz, Plenge, and Hüttemann 2014: 27–52. doi:10.1007/978-94-007-7563-3_2
• –––, 2016, “Levels”, inOpenMIND: Philosophy and the Mind Sciences in the 21st Century,Thomas Metzinger and Jennifer M. Windt (eds), Cambridge, MA: The MITPress, chapter 23. doi:10.7551/mitpress/10603.003.0025
• Craver, Carl F. and William Bechtel, 2007, “Top-downCausation Without Top-down Causes”,Biology &Philosophy, 22(4): 547–563.doi:10.1007/s10539-006-9028-8
• Craver, Carl F. and Lindley Darden, 2013,In Search ofMechanisms: Discoveries across the Life Sciences, Chicago/London:University of Chicago Press.
• Craver, Carl F., Michael Dozmorov, Mark Reimers, & Kenneth S.Kendler, 2020, “Gloomy Prospects and Roller Coasters: FindingCoherence in Genome-Wide Association Studies”,Philosophy ofScience, 87(5): 1084–1095.
• Craver, Carl F., Stuart Glennan, and Mark Povich, 2021,“Constitutive Relevance & Mutual ManipulabilityRevisited”,Synthese, 199(3–4): 8807–8828.doi:10.1007/s11229-021-03183-8
• Craver, Carl F. and Marie I. Kaiser, 2013, “Mechanisms andLaws: Clarifying the Debate”, inChao, Chen, and Millstein 2013: 125–145 (ch. 7). doi:10.1007/978-94-007-2454-9_7
• Craver, Carl F. and David M. Kaplan, 2020, “Are More DetailsBetter? On the Norms of Completeness for MechanisticExplanations”,The British Journal for the Philosophy ofScience, 71(1): 287–319. doi:10.1093/bjps/axy015
• Cummins, Robert, 1975, “Functional Analysis”,TheJournal of Philosophy, 72(20): 741–765.doi:10.2307/2024640
• –––, 1983,The Nature of PsychologicalExplanation, Cambridge, MA: MIT Press.
• Darden, Lindley, 1986, “Reasoning in Theory Construction:Analogies, Interfield Connections, and Levels of Organization”,inFoundations of Biology, Paul Weingartner and Georg Dorn(eds), Vienna: Holder-Pichler-Tempsky, pp. 99–107.
• –––, 1991,Theory Change in Science:Strategies from Mendelian Genetics (Monographs on the History andPhilosophy of Biology), Oxford/New York: Oxford University Press.doi:10.1093/oso/9780195067972.001.0001
• –––, 2005, “Relations among Fields:Mendelian, Cytological and Molecular Mechanisms”,Studies inHistory and Philosophy of Science Part C: Studies in History andPhilosophy of Biological and Biomedical Sciences, 36(2):349–371. doi:10.1016/j.shpsc.2005.03.007
• –––, 2006,Reasoning in BiologicalDiscoveries: Essays on Mechanisms, Interfield Relations, and AnomalyResolution (Cambridge Studies in Philosophy and Biology), NewYork: Cambridge University Press. doi:10.1017/CBO9780511498442
• –––, 2009, “Discovering Mechanisms inMolecular Biology: Finding and Fixing Incompleteness andIncorrectness”, inModels of Discovery and Creativity,Joke Meheus and Thomas Nickles (eds), Dordrecht: Springer,43–55. doi:10.1007/978-90-481-3421-2_3
• –––, 2013, “Mechanisms Versus Causes inBiology and Medicine”, inChao, Chen, and Millstein 2013: 19–34 (ch. 1). doi:10.1007/978-94-007-2454-9_2
• –––, 2017, “Strategies for DiscoveringMechanisms”, inGlennan and Illari 2017b: 255–266 (ch. 19).
• Darden, Lindley and Joseph A. Cain, 1989, “Selection TypeTheories”,Philosophy of Science, 56(1): 106–129.doi:10.1086/289475
• Darden, Lindley and Carl Craver, 2002, “Strategies in theInterfield Discovery of the Mechanism of Protein Synthesis”,Studies in History and Philosophy of Science Part C: Studies inHistory and Philosophy of Biological and Biomedical Sciences,33(1): 1–28. doi:10.1016/S1369-8486(01)00021-8
• Darden, Lindley, Lipika R. Pal, Kunal Kundu, and John Moult, 2018,“The Product Guides the Process: Discovering DiseaseMechanisms”, inBuilding Theories: Heuristics and Hypothesesin Sciences (Studies in Applied Philosophy, Epistemology andRational Ethics 41), David Danks and Emiliano Ippoliti (eds), Cham:Springer International Publishing, 101–117.doi:10.1007/978-3-319-72787-5_6
• Darden, Lindley and Nancy Maull, 1977, “InterfieldTheories”,Philosophy of Science, 44(1): 43–64.doi:10.1086/288723
• Datteri, Edoardo, 2009, “Simulation Experiments in Bionics:A Regulative Methodological Perspective”,Biology &Philosophy, 24(3): 301–324.doi:10.1007/s10539-008-9133-y
• Datteri, Edoardo and Guglielmo Tamburrini, 2007, “BioroboticExperiments for the Discovery of Biological Mechanisms”,Philosophy of Science, 74(3): 409–430.doi:10.1086/522095
• DesAutels, Lane, 2011, “Against Regular and IrregularCharacterizations of Mechanisms”,Philosophy ofScience, 78(5): 914–925. doi:10.1086/662558
• Dewhurst, Joe and Alistair M. C. Isaac, 2023, “The Ups andDowns of Mechanism Realism: Functions, Levels, and CrosscuttingHierarchies”,Erkenntnis, 88(3): 1035–1057.doi:10.1007/s10670-021-00392-y
• DiFrisco, James, 2017, “Time Scales and Levels ofOrganization”,Erkenntnis, 82(4): 795–818.doi:10.1007/s10670-016-9844-4
• DiFrisco, James, Alan C. Love, and Günter P. Wagner, 2020,“Character Identity Mechanisms: A Conceptual Model forComparative-Mechanistic Biology”,Biology &Philosophy, 35(4): article 44.doi:10.1007/s10539-020-09762-2
• Dijksterhuis, Eduard Jan, 1950 [1961],De mechanisering vanhet wereldbeeld, Amsterdam: Meulenhoff. Translated asTheMechanization of the World Picture, C. Dikshoorn (trans.),Oxford: Clarendon Press, 1961.
• Dowe, Phil, 2011, “The Causal‐process‐modelTheory of Mechanisms”, inCausality in the Sciences,Phyllis McKay Illari, Federica Russo, and Jon Williamson (eds),Oxford/New York: Oxford University Press, 865–879 (ch. 40).doi:10.1093/acprof:oso/9780199574131.003.0040
• Dray, William H., 1968, “On Explaining How-Possibly”:,The Monist, 52(3): 390–407.doi:10.5840/monist196852339
• Dretske, Fred, 1994, “If You Can’t Make One, YouDon’t Know How It Works”,Midwest Studies inPhilosophy, 19: 468–482.doi:10.1111/j.1475-4975.1994.tb00299.x
• Dupré, John, 2013, “Living Causes”,Aristotelian Society Supplementary Volume, 87: 19–37.doi:10.1111/j.1467-8349.2013.00218.x
• Dupré, John and Daniel J. Nicholson, 2018, “AManifesto for a Processual Philosophy of Biology”, inNicholson and Dupré 2018: 3–46. doi:10.1093/oso/9780198779636.003.0001
• Eberhardt, Frederick, & Richard Scheines, 2007,“Interventions and Causal Inference”,Philosophy ofScience, 74(5): 981–995.
• Eronen, Markus I., 2013, “No Levels, No Problems: DownwardCausation in Neuroscience”,Philosophy of Science,80(5): 1042–1052. doi:10.1086/673898
• –––, 2015, “Levels of Organization: ADeflationary Account”,Biology & Philosophy, 30(1):39–58. doi:10.1007/s10539-014-9461-z
• Fagan, Melinda Bonnie, 2012, “The Joint Account ofMechanistic Explanation”,Philosophy of Science, 79(4):448–472. doi:10.1086/668006
• Fazekas, Peter and Gergely Kertész, 2011, “Causationat Different Levels: Tracking the Commitments of MechanisticExplanations”,Biology & Philosophy, 26(3):365–383. doi:10.1007/s10539-011-9247-5
• Fiorentino, Alexander R. and Olaf Dammann, 2015, “Evidence,Illness, and Causation: An Epidemiological Perspective on theRusso–Williamson Thesis”,Studies in History andPhilosophy of Science Part C: Studies in History and Philosophy ofBiological and Biomedical Sciences, 54: 1–9.doi:10.1016/j.shpsc.2015.09.010
• Fodor, Jerry A., 1968,Psychological Explanation: AnIntroduction to the Philosophy of Psychology (A Random HouseStudy in Problems of Philosophy), New York: Random House.
• Fortunato, Santo, 2010, “Community Detection inGraphs”,Physics Reports, 486(3–5): 75–174.doi:10.1016/j.physrep.2009.11.002
• Francken, Jolien C., Marc Slors, and Carl F. Craver, 2022,“Cognitive Ontology and the Search for Neural Mechanisms: ThreeFoundational Problems”,Synthese, 200(5): 378.doi:10.1007/s11229-022-03701-2
• Gabbey, Alan, 2004, “What Was ‘Mechanical’ about‘The Mechanical Philosophy’?”, inThe Receptionof the Galilean Science of Motion in Seventeenth-Century Europe(Boston Studies in the Philosophy and History of Science 239), CarlaRita Palmerino and J. M. M. H. Thijssen (eds), Dordrecht: SpringerNetherlands, 11–23. doi:10.1007/978-1-4020-2455-9_2
• Garber, Daniel, 1992,Descartes’ MetaphysicalPhysics (Science and Its Conceptual Foundations), Chicago:University of Chicago Press.
• Garson, Justin, 2013, “The Functional Sense ofMechanism”,Philosophy of Science, 80(3):317–333. doi:10.1086/671173
• Gebharter, Alexander, 2014, “A Formal Framework forRepresenting Mechanisms?”,Philosophy of Science,81(1): 138–153. doi:10.1086/674206
• –––, 2017,Causal Nets, Interventionism, andMechanisms: Philosophical Foundations and Applications (SyntheseLibrary 381), Cham: Springer. doi:10.1007/978-3-319-49908-6
• –––, 2022, “A Causal Bayes Net Analysis ofGlennan’s Mechanistic Account of Higher-Level Causation (andSome Consequences)”,The British Journal for the Philosophyof Science, 73(1): 185–210. doi:10.1093/bjps/axz034
• Gebharter, Alexander and Marie I. Kaiser, 2014, “CausalGraphs and Biological Mechanisms”, inKaiser, Scholz, Plenge, and Hüttemann 2014: 55–85. doi:10.1007/978-94-007-7563-3_3
• Gervais, Raoul and Erik Weber, 2015, “The Role ofOrientation Experiments in Discovering Mechanisms”,Studiesin History and Philosophy of Science Part A, 54: 46–55.doi:10.1016/j.shpsa.2015.08.015
• Giere, Ronald N., 2006,Scientific Perspectivism,Chicago: University of Chicago Press.
• Gillett, Carl, 2002a, “The Dimensions of Realization: ACritique of the Standard View”,Analysis, 62(4):316–323. doi:10.1093/analys/62.4.316
• –––, 2002b, “The Metaphysics ofRealization, Multiple Realizability, and the Special Sciences”,The Journal of Philosophy, 100(11): 591–603.
• –––, 2002c, “The Varieties of Emergence:Their Purposes, Obligations and Importance”,GrazerPhilosophische Studien, 65: 95–121.doi:10.1163/18756735-90000795
• –––, 2013, “Constitution, and MultipleConstitution, in the Sciences: Using the Neuron to Construct aStarting Framework”,Minds and Machines, 23(3):309–337. doi:10.1007/s11023-013-9311-9
• Glennan, Stuart S., 1996, “Mechanisms and the Nature ofCausation”,Erkenntnis, 44(1): 49–71.doi:10.1007/BF00172853
• –––, 2002, “Rethinking MechanisticExplanation”,Philosophy of Science, 69(S3):S342–S353. doi:10.1086/341857
• –––, 2005, “Modeling Mechanisms”,Studies in History and Philosophy of Science Part C: Studies inHistory and Philosophy of Biological and Biomedical Sciences,36(2): 443–464. doi:10.1016/j.shpsc.2005.03.011
• –––, 2009, “Productivity, Relevance andNatural Selection”,Biology & Philosophy, 24(3):325–339. doi:10.1007/s10539-008-9137-7
• –––, 2010, “Mechanisms, Causes, and theLayered Model of the World”,Philosophy and PhenomenologicalResearch, 81(2): 362–381.doi:10.1111/j.1933-1592.2010.00375.x
• –––, 2017,The New MechanicalPhilosophy, Oxford: Oxford University Press.doi:10.1093/oso/9780198779711.001.0001
• Glennan, Stuart and Phyllis Illari, 2017a, “Varieties ofMechanisms”, inGlennan and Illari 2017b: 91–103 (ch. 7).
• ––– (eds), 2017b,The RoutledgeHandbook of Mechanisms and Mechanical Philosophy, London/NewYork: Routledge. doi:10.4324/9781315731544
• Glennan, Stuart, Phyllis Illari, and Erik Weber, 2022, “SixTheses on Mechanisms and Mechanistic Science”,Journal forGeneral Philosophy of Science, 53(2): 143–161.doi:10.1007/s10838-021-09587-x
• Hanson, Norwood Russell, 1958,Patterns of Discovery: AnInquiry into the Conceptual Foundations of Science, Cambridge:Cambridge University Press.
• Harbecke, Jens, 2010, “Mechanistic Constitution inNeurobiological Explanations”,International Studies in thePhilosophy of Science, 24(3): 267–285.doi:10.1080/02698595.2010.522409
• –––, 2015, “Regularity Constitution andthe Location of Mechanistic Levels”,Foundations ofScience, 20(3): 323–338. doi:10.1007/s10699-014-9371-1
• Harinen, Totte, 2018, “Mutual Manipulability and CausalInbetweenness”,Synthese, 195(1): 35–54.doi:10.1007/s11229-014-0564-5
• Haugeland, John, 1998,Having Thought: Essays in theMetaphysics of Mind, Cambridge, MA: Harvard UniversityPress.
• Hedström, Peter, & Petri Ylikoski, 2010, “CausalMechanisms in the Social Sciences”,Annual Review ofSociology, 36: 49–67.
• Hempel, Carl G., 1965,Aspects of Scientific Explanation: AndOther Essays in the Philosophy of Science, New York: FreePress.
• Hochstein, Eric, 2016, “One Mechanism, Many Models: ADistributed Theory of Mechanistic Explanation”,Synthese, 193(5): 1387–1407.doi:10.1007/s11229-015-0844-8
• –––, 2017, “Why One Model Is Never Enough:A Defense of Explanatory Holism”,Biology &Philosophy, 32(6): 1105–1125.doi:10.1007/s10539-017-9595-x
• Hodgkin, Alan L. and Andrew F. Huxley, 1952, “A QuantitativeDescription of Membrane Current and Its Application to Conduction andExcitation in Nerve”,The Journal of Physiology,117(4): 500–544. doi:10.1113/jphysiol.1952.sp004764
• Huneman, Philippe, 2010, “Topological Explanations andRobustness in Biological Sciences”,Synthese, 177(2):213–245. doi:10.1007/s11229-010-9842-z
• Illari, Phyllis McKay, 2011, “Mechanistic Evidence:Disambiguating the Russo–Williamson Thesis”,International Studies in the Philosophy of Science, 25(2):139–157. doi:10.1080/02698595.2011.574856
• –––, 2013, “Mechanistic Explanation:Integrating the Ontic and Epistemic”,Erkenntnis,78(S2): 237–255. doi:10.1007/s10670-013-9511-y
• –––, 2019, “Mechanisms, Models and Laws inUnderstanding Supernovae”,Journal for General Philosophy ofScience, 50(1): 63–84. doi:10.1007/s10838-018-9435-y
• Illari, Phyllis McKay and Federica Russo, 2014,Causality:Philosophical Theory Meets Scientific Practice, Oxford: OxfordUniversity Press.
• Illari, Phyllis McKay and Jon Williamson, 2010, “Functionand Organization: Comparing the Mechanisms of Protein Synthesis andNatural Selection”,Studies in History and Philosophy ofScience Part C: Studies in History and Philosophy of Biological andBiomedical Sciences, 41(3): 279–291.doi:10.1016/j.shpsc.2010.07.001
• –––, 2012, “What Is a Mechanism? Thinkingabout Mechanismsacross the Sciences”,EuropeanJournal for Philosophy of Science, 2(1): 119–135.doi:10.1007/s13194-011-0038-2
• –––, 2013, “In Defence ofActivities”,Journal for General Philosophy of Science,44(1): 69–83. doi:10.1007/s10838-013-9217-5
• Ioannidis, Stavros and Stathis Psillos, 2018, “Mechanisms inPractice: A Methodological Approach”,Journal of Evaluationin Clinical Practice, 24(5): 1177–1183.doi:10.1111/jep.12971
• –––, 2022,Mechanisms in Science: Method orMetaphysics?, Cambridge/New York: Cambridge University Press.doi:10.1017/9781009019668
• Kaiser, Marie I., 2015,Reductive Explanation in theBiological Sciences, Cham: Springer InternationalPublishing.
• Kaiser, Marie I. and Beate Krickel, 2017, “The Metaphysicsof Constitutive Mechanistic Phenomena”,The British Journalfor the Philosophy of Science, 68(3): 745–779.doi:10.1093/bjps/axv058
• Kaiser, Marie I., Oliver R. Scholz, Daniel Plenge,and Andreas Hüttemann (eds), 2014,Explanation in the SpecialSciences: The Case of Biology and History (Synthese Library 367),Dordrecht/New York: Springer. doi:10.1007/978-94-007-7563-3
• Kaplan, David Michael, 2011, “Explanation and Description inComputational Neuroscience”,Synthese, 183(3):339–373. doi:10.1007/s11229-011-9970-0
• –––, 2012, “How to Demarcate theBoundaries of Cognition”,Biology & Philosophy,27(4): 545–570. doi:10.1007/s10539-012-9308-4
• Kaplan, David Michael, & William Bechtel, 2011,“Dynamical Models: An Alternative or Complement to MechanisticExplanations?”Topics in Cognitive Science, 3(2):438–444.
• Kaplan, David Michael, and Carl F. Craver, 2011, “TheExplanatory Force of Dynamical and Mathematical Models inNeuroscience: A Mechanistic Perspective”,Philosophy ofScience 78(4): 601–27. doi:10.1086/661755
• Kaplan, David M. and Christopher L. Hewitson, 2021,“Modelling Bayesian Computation in the Brain: Unification,Explanation, and Constraints”, inNeural Mechanisms: NewChallenges in the Philosophy of Neuroscience (Studies in Brainand Mind 17), Fabrizio Calzavarini and Marco Viola (eds), Cham:Springer International Publishing, 11–33.doi:10.1007/978-3-030-54092-0_2
• Kashtan, Nadav and Uri Alon, 2005, “Spontaneous Evolution ofModularity and Network Motifs”,Proceedings of the NationalAcademy of Sciences, 102(39): 13773–13778.doi:10.1073/pnas.0503610102
• Kästner, Lena, 2015, “Learning About ConstitutiveRelations”, inRecent Developments in the Philosophy ofScience: EPSA13 Helsinki (European Studies in Philosophy ofScience), Uskali Mäki, Ioannis Votsis, Stéphanie Ruphy,and Gerhard Schurz (eds), Cham: Springer International Publishing,155–167. doi:10.1007/978-3-319-23015-3_12
• –––, 2018, “Integrating MechanisticExplanations through Epistemic Perspectives”,Studies inHistory and Philosophy of Science Part A, 68: 68–79.doi:10.1016/j.shpsa.2018.01.011
• –––, 2021, “Integration and theMechanistic Triad: Producing, Underlying and Maintaining MechanisticExplanations”, inNeural Mechanisms: New Challenges in thePhilosophy of Neuroscience (Studies in Brain and Mind 17),Fabrizio Calzavarini and Marco Viola (eds), Cham: Springer,337–361 (ch. 15). doi:10.1007/978-3-030-54092-0_15
• Kästner, Lena and Lise Marie Andersen, 2018,“Intervening into Mechanisms: Prospects and Challenges”,Philosophy Compass, 13(11): e12546.doi:10.1111/phc3.12546
• Kästner, Lena and Philipp Haueis, 2021, “DiscoveringPatterns: On the Norms of Mechanistic Inquiry”,Erkenntnis, 86(6): 1635–1660.doi:10.1007/s10670-019-00174-7
• Kauffman, Stuart A., 1971 [1976], “Articulation of PartsExplanation in Biology and the Rational Search for Them”, inPSA 1970: Proceedings of the 1970 Biennial Meeting of thePhilosophy of Science Association (Boston Studies in thePhilosophy of Science 8), Roger C. Buck and Robert S. Cohen (eds.),Dordrecht: D. Reidel, 257–272. Reprinted inTopics in thePhilosophy of Biology (Boston Studies in the Philosophy ofScience 27), Marjorie Grene and Everett Mendelsohn (eds),Dordrecht/Boston: D. Reidel, 245–263.doi:10.1086/psaprocbienmeetp.1970.495768doi:10.1007/978-94-010-1829-6_11
• Kay, Kendrick N., 2018, “Principles for Models of NeuralInformation Processing”,NeuroImage, New advances inencoding and decoding of brain signals, 180: 101–9.doi:10.1016/j.neuroimage.2017.08.016
• Kim, Jaegwon, 1992, “‘Downward Causation’ inEmergentism and Nonreductive Physicalism”, inEmergence orReduction? Essays on the Prospects of Nonreductive Physicalism(Foundations of Communication and Cognition = Grundlagen derKommunikation und Kognition), Ansgar Beckermann, Hans Flohr, andJaegwon Kim (eds), Berlin/New York: W. de Gruyter, 119–138.
• –––, 1998,Mind in a Physical World: AnEssay on the Mind-Body Problem and Mental Causation(Representation and Mind), Cambridge, MA: MIT Press.doi:10.7551/mitpress/4629.001.0001
• Kornblith, Hilary, 1993,Inductive Inference and Its NaturalGround: An Essay in Naturalistic Epistemology, Cambridge, MA: MITPress. doi:10.7551/mitpress/3730.001.0001
• Kreimer, Anat, Elhanan Borenstein, Uri Gophna, and Eytan Ruppin,2008, “The Evolution of Modularity in Bacterial MetabolicNetworks”,Proceedings of the National Academy ofSciences, 105(19): 6976–6981.doi:10.1073/pnas.0712149105
• Krickel, Beate, 2014,The Metaphysics of Mechanism, PhDDissertation, Humboldt-Universität zu Berlin.
• –––, 2018a, “Causation andConstitution”, in herThe Mechanical World: The MetaphysicalCommitments of the New Mechanistic Approach (Studies in Brain andMind 13), Cham: Springer International Publishing, 129–153.doi:10.1007/978-3-030-03629-4_7
• –––, 2018b, “Saving the MutualManipulability Account of Constitutive Relevance”,Studiesin History and Philosophy of Science Part A, 68: 58–67.doi:10.1016/j.shpsa.2018.01.003
• Kuhn, Thomas S., 1962 [1970],The Structure of ScientificRevolutions, Chicago: University of Chicago Press. Secondedition, enlarged, 1970.
• Laudan, Larry, 1977,Progress and Its Problems: Toward aTheory of Scientific Growth, Berkeley, CA: University ofCalifornia Press.
• –––, 1980, “Why Was the Logic of DiscoveryAbandoned?”, inScientific Discovery, Logic, andRationality (Boston Studies in the Philosophy of Science 56),Thomas Nickles (ed.), Dordrecht/Boston: D. Reidel, 173–183.doi:10.1007/978-94-009-8986-3_6
• Leuridan, Bert, 2010, “Can Mechanisms Really Replace Laws ofNature?”,Philosophy of Science, 77(3): 317–340.doi:10.1086/652959
• –––, 2012, “Three Problems for the MutualManipulability Account of Constitutive Relevance in Mechanisms”,The British Journal for the Philosophy of Science, 63(2):399–427. doi:10.1093/bjps/axr036
• Levine, Joseph, 1987, “The Nature of PsychologicalExplanation by Robert Cummins: A Critical Notice”,ThePhilosophical Review, 96(2): 249–274.doi:10.2307/2185159
• Levy, Arnon, 2013, “Three Kinds of New Mechanism”,Biology & Philosophy, 28(1): 99–114.doi:10.1007/s10539-012-9337-z
• –––, 2014, “What Was Hodgkin andHuxley’s Achievement?”,The British Journal for thePhilosophy of Science, 65(3): 469–492.doi:10.1093/bjps/axs043
• –––, 2021, “Idealization and Abstraction:Refining the Distinction”,Synthese, 198(S24):5855–5872. doi:10.1007/s11229-018-1721-z
• Levy, Arnon and William Bechtel, 2013, “Abstraction and theOrganization of Mechanisms”,Philosophy of Science,80(2): 241–261. doi:10.1086/670300
• Lewis, David, 1973, “Causation”,The Journal ofPhilosophy, 70(17): 556–567. doi:10.2307/2025310
• –––, 1986, “Causal Explanation”, inhistPhilosophical Papers, Volume II, New York: OxfordUniversity Press, 214–240. doi:10.1093/0195036468.003.0007
• Loeb, Jacques, 1912,The Mechanistic Conception of Life:Biological Essays, Chicago, IL: The University of ChicagoPress.
• Lycan, William G., 1990, “The Continuity of Levels ofNature”, inMind and Cognition: A Reader, William G.Lycan (ed.), Oxford/New York: Basil Blackwell, 77–96.
• Oleksowicz, Michał, 2021, “New Mechanism and Causality:The Case of Interactive Causal Pluralism”,RevistaPortuguesa de Filosofia, 77(4): 1175–1208.doi:10.17990/RPF/2021_77_4_1175
• Machamer, Peter, 2004, “Activities and Causation: TheMetaphysics and Epistemology of Mechanisms”,InternationalStudies in the Philosophy of Science, 18(1): 27–39.doi:10.1080/02698590412331289242
• Machamer, Peter, Lindley Darden, and Carl F. Craver, 2000,“Thinking about Mechanisms”,Philosophy ofScience, 67(1): 1–25. doi:10.1086/392759
• Mackie, John Leslie, 1980,The Cement of the Universe: A Studyof Causation (Clarendon Library of Logic and Philosophy),paperback edition, Oxford: Clarendon.doi:10.1093/0198246420.001.0001
• Matthews, Lucas J., 2019, “Isolability as the UnifyingFeature of Modularity”,Biology & Philosophy,34(2): article 20. doi:10.1007/s10539-019-9672-4
• Matthews, Lucas J. and James Tabery, 2017, “Mechanisms andthe Metaphysics of Causation”, inGlennan and Illari 2017b: 131–143 (ch. 10).
• Mauk, Michael D., 2000, “The Potential Effectiveness ofSimulations versus Phenomenological Models”,NatureNeuroscience, 3(7): 649–651. doi:10.1038/76606
• McCauley, Robert N. and William Bechtel, 2001, “ExplanatoryPluralism and Heuristic Identity Theory”,Theory &Psychology, 11(6): 736–760.doi:10.1177/0959354301116002
• Melnyk, Andrew, 2003,A Physicalist Manifesto: ThoroughlyModern Materialism (Cambridge Studies in Philosophy), New York:Cambridge University Press. doi:10.1017/CBO9780511498817
• –––, 2010, “Comments on SydneyShoemaker’s Physical Realization”,PhilosophicalStudies, 148(1): 113–123.doi:10.1007/s11098-010-9500-9
• Menzies, Peter, 2012, “The Causal Structure ofMechanisms”,Studies in History and Philosophy of SciencePart C: Studies in History and Philosophy of Biological and BiomedicalSciences, 43(4): 796–805.doi:10.1016/j.shpsc.2012.05.008
• Melo, Diogo and Gabriel Marroig, 2015, “DirectionalSelection Can Drive the Evolution of Modularity in ComplexTraits”,Proceedings of the National Academy ofSciences, 112(2): 470–475. doi:10.1073/pnas.1322632112
• Mitchell, Sandra D., 1997, “Pragmatic Laws”,Philosophy of Science, 64(S4): S468–S479.doi:10.1086/392623
• –––, 2000, “Dimensions of ScientificLaw”,Philosophy of Science, 67(2): 242–265.doi:10.1086/392774
• –––, 2003,Biological Complexity andIntegrative Pluralism (Cambridge Studies in Philosophy andBiology), Cambridge/New York: Cambridge University Press.doi:10.1017/CBO9780511802683
• –––, 2009,Unsimple Truths: Science,Complexity, and Policy, Chicago: University of ChicagoPress.
• Neander, Karen, 1991, “Functions as Selected Effects: TheConceptual Analyst’s Defense”,Philosophy ofScience, 58(2): 168–184. doi:10.1086/289610
• Nicholson, Daniel J., 2013, “Organisms≠Machines”,Studies in History and Philosophy of Science Part C: Studies inHistory and Philosophy of Biological and Biomedical Sciences,44(4): 669–678. doi:10.1016/j.shpsc.2013.05.014
• –––, 2014, “The Return of the Organism asa Fundamental Explanatory Concept in Biology”,PhilosophyCompass, 9(5): 347–359. doi:10.1111/phc3.12128
• Nicholson, Daniel J. and John Dupré (eds),2018,Everything Flows: Towards a Processual Philosophy ofBiology, Oxford: Oxford University Press.doi:10.1093/oso/9780198779636.001.0001
• Nickles, Thomas, 1985, “Beyond Divorce: Current Status ofthe Discovery Debate”,Philosophy of Science, 52(2):177–206. doi:10.1086/289239
• Norton, John D., 2003, “A Material Theory ofInduction”,Philosophy of Science, 70(4):647–670. doi:10.1086/378858
• Oppenheim, Paul and Hilary Putnam, 1958, “Unity of Scienceas a Working Hypothesis”, inConcepts, Theories, and theMind-Body Problem (Minnesota Studies in the Philosophy of Science2), Herbert Feigl, Michael Scriven, and Grover Maxwell (eds),Minneapolis, MN: University of Minnesota Press, 3–36.
• Paolini Paoletti, Michele, 2021, “Mechanisms andRelations”,Erkenntnis, 86(1): 95–111.doi:10.1007/s10670-018-0095-4
• Parkkinen, Veli-Pekka, Christian Wallmann, Michael Wilde, BrendanClarke, Phyllis Illari, Michael P Kelly, Charles Norell, FedericaRusso, Beth Shaw, and Jon Williamson, 2018,Evaluating Evidence ofMechanisms in Medicine: Principles and Procedures (SpringerBriefsin Philosophy), Cham: Springer International Publishing.doi:10.1007/978-3-319-94610-8
• Pauly, Philip J., 1987,Controlling Life: Jacques Loeb and theEngineering Ideal in Biology (Monographs on the History andPhilosophy of Biology), New York: Oxford University Press.
• Pearl, Judea, 2009,Causality: Models, Reasoning, andInference, second edition, Cambridge/New York: CambridgeUniversity Press. doi:10.1017/CBO9780511803161
• –––, 2014, “Interpretation andIdentification of Causal Mediation.”,PsychologicalMethods, 19(4): 459–481. doi:10.1037/a0036434
• Persson, Johannes, 2010, “Activity-Based Accounts ofMechanism and the Threat of Polygenic Effects”,Erkenntnis, 72(1): 135–149.doi:10.1007/s10670-009-9195-5
• Piccinini, Gualtiero, 2020,Neurocognitive Mechanisms:Explaining Biological Cognition, Oxford: Oxford University Press.doi:10.1093/oso/9780198866282.001.0001
• Piccinini, Gualtiero and Carl Craver, 2011, “IntegratingPsychology and Neuroscience: Functional Analyses as MechanismSketches”,Synthese, 183(3): 283–311.doi:10.1007/s11229-011-9898-4
• Piekarski, Michal, 2022, “Motivation, CounterfactualPredictions and Constraints: Normativity of PredictiveMechanisms”,Synthese, 200: 352.doi:10.1007/s11229-022-03837-1
• Polger, Thomas W., 2004,Natural Minds, Cambridge, MA:MIT Press. doi:10.7551/mitpress/4863.001.0001
• –––, 2007, “Realization and theMetaphysics of Mind1”,Australasian Journal ofPhilosophy, 85(2): 233–259.doi:10.1080/00048400701343085
• –––, 2010, “Mechanisms and ExplanatoryRealization Relations”,Synthese, 177(2):193–212. doi:10.1007/s11229-010-9841-0
• Popper, Karl Raimund, 1959,The Logic of ScientificDiscovery, London: Hutchinson.
• Potochnik, Angela and Brian McGill, 2012, “The Limitationsof Hierarchical Organization”,Philosophy of Science,79(1): 120–140. doi:10.1086/663237
• Povich, Mark Adam, 2015, “Mechanisms and Model-BasedFunctional Magnetic Resonance Imaging”,Philosophy ofScience, 82(5): 1035–1046.
• –––, 2017, “Model and World: Generalizingthe Ontic Conception of Scientific Explanation”, PhD, WashingtonUniversity in St. Louis. doi:10.7936/K7125R3K
• –––, 2018, “Minimal Models and theGeneralized Ontic Conception of Scientific Explanation”,TheBritish Journal for the Philosophy of Science, 69(1):117–137. doi:10.1093/bjps/axw019
• –––, 2021, “Information and Explanation:An Inconsistent Triad and Solution”,European Journal forPhilosophy of Science, 11(2): article 43.doi:10.1007/s13194-021-00368-3
• Povich, Mark, and Carl F. Craver, 2017, “Mechanistic Levels,Reduction, and Emergence”, inGlennan and Illari 2017b: 185–197 (ch. 14).
• Prychitko, Emily, 2021, “The Causal Situationist Account ofConstitutive Relevance”,Synthese, 198(2):1829–1843. doi:10.1007/s11229-019-02170-4
• Psillos, Stathis, 2004, “A Glimpse of the Secret Connexion:Harmonizing Mechanisms with Counterfactuals”,Perspectiveson Science, 12(3): 288–319.doi:10.1162/1063614042795426
• Reichenbach, Hans, 1938,Experience and Prediction: AnAnalysis of the Foundations and the Structure of Knowledge,Chicago, IL: The University of Chicago Press.
• Rescorla, Michael, 2018, “An Interventionist Approach toPsychological Explanation”,Synthese, 195(5):1909–1940. doi:10.1007/s11229-017-1553-2
• Richardson, Robert C. and Achim Stephan, 2007, “Mechanismand Mechanical Explanation in Systems Biology”, inSystemsBiology: Philosophical Foundations, Fred C. Boogerd, Frank J.Bruggeman, Jan-Hendrik S. Hofmeyr, and Hans V. Westerhoff (eds),Amsterdam/Boston: Elsevier, 123–144.doi:10.1016/B978-044452085-2/50008-5
• Romero, Felipe, 2015, “Why There Isn’t Inter-LevelCausation in Mechanisms”,Synthese, 192(11):3731–3755. doi:10.1007/s11229-015-0718-0
• Ross, Lauren N., 2015, “Dynamical Models and Explanation inNeuroscience”,Philosophy of Science, 82(1):32–54. doi:10.1086/679038
• –––, 2021, “Causal Concepts in Biology:How Pathways Differ from Mechanisms and Why It Matters”,TheBritish Journal for the Philosophy of Science, 72(1):131–158. doi:10.1093/bjps/axy078
• Roux, Sophie, 2017, “From the Mechanical Philosophy to EarlyModern Mechanisms”, InGlennan and Illari 2017b: 26–45 (ch. 3).
• Russo, Federica and Jon Williamson, 2007, “InterpretingCausality in the Health Sciences”,International Studies inthe Philosophy of Science, 21(2): 157–170.doi:10.1080/02698590701498084
• Salmon, Wesley C., 1975, “Theoretical Explanation”, inExplanation, Oxford: Basil Blackwell, 118–145.
• –––, 1977, “An ‘At-At’ Theoryof Causal Influence”,Philosophy of Science, 44(2):215–224. doi:10.1086/288739
• –––, 1984,Scientific Explanation and theCausal Structure of the World, Princeton, NJ: PrincetonUniversity Press.
• –––, 1989, “Four Decades of ScientificExplanation”, inScientific Explanation (MinnesotaStudies in the Philosophy of Science, 13), P. Kitcher and W.C. Salmon(eds), 3–219.
• –––, 1997, “Causality and Explanation: AReply to Two Critiques”,Philosophy of Science, 64(3):461–477. doi:10.1086/392561
• –––, 1998,Causality and Explanation,New York: Oxford University Press.doi:10.1093/0195108647.001.0001
• Sanford, David H., 1993, “The Problem of the Many, ManyComposition Questions, and Naive Mereology”,Noûs, 27(2): 219–228. doi:10.2307/2215757
• Schaffner, Kenneth F., 1974, “The Peripherality ofReductionism in the Development of Molecular Biology”,Journal of the History of Biology, 7(1): 111–139.doi:10.1007/BF00179296
• –––, 1993,Discovery and Explanation inBiology and Medicine (Science and Its Conceptual Foundations),Chicago, IL: University of Chicago Press.
• Scholl, Raphael, 2013, “Causal Inference, Mechanisms, andthe Semmelweis Case”,Studies in History and Philosophy ofScience Part A, 44(1): 66–76.doi:10.1016/j.shpsa.2012.04.002
• Scholl, Raphael and Kärin Nickelsen, 2015, “Discoveryof Causal Mechanisms: Oxidative Phosphorylation and theCalvin–Benson Cycle”,History and Philosophy of theLife Sciences, 37(2): 180–209.doi:10.1007/s40656-015-0061-2
• Schouten, Maurice Kenneth Davy and Huibert Looren de Jong (eds),2007,The Matter of the Mind: Philosophical Essays on Psychology,Neuroscience, and Reduction, Malden, MA: Blackwell.
• Scriven, Michael, 1959, “Explanation and Prediction inEvolutionary Theory: Satisfactory Explanation of the Past Is PossibleEven When Prediction of the Future Is Impossible”,Science, 130(3374): 477–482.doi:10.1126/science.130.3374.477
• –––, 1975, “Causation asExplanation”,Noûs, 9(1): 3–16.doi:10.2307/2214338
• Shapiro, Lawrence A., 2017, “Mechanism or Bust? Explanationin Psychology”,The British Journal for the Philosophy ofScience, 68(4): 1037–1059. doi:10.1093/bjps/axv062
• Siegel, Gabriel, forthcoming, “Counterfactual Dependence IsNot Intelligibility”,Philosophical Studies.
• Siegel, Gabriel and Carl F. Craver, 2024, “PhenomenologicalLaws and Mechanistic Explanations”,Philosophy ofScience, 91(1): 132–150. doi:10.1017/psa.2023.65
• Simon, Herbert A., 1969,The Sciences of the Artificial(Karl Taylor Compton Lectures 1968), Cambridge, MA: MIT Press.
• Skipper, Robert A. and Roberta L. Millstein, 2005, “Thinkingabout Evolutionary Mechanisms: Natural Selection”,Studiesin History and Philosophy of Science Part C: Studies in History andPhilosophy of Biological and Biomedical Sciences, 36(2):327–347. doi:10.1016/j.shpsc.2005.03.006
• Spirtes, Peter, Clark N. Glymour, and Richard Scheines, 1993[2000],Causation, Prediction, and Search (Lecture Notes inStatistics 81), New York: Springer-Verlag. Second edition, Cambridge,MA: MIT Press, 2000. doi:10.7551/mitpress/1754.001.0001
• Spring, Jonathan M. and Phyllis Illari, 2019, “BuildingGeneral Knowledge of Mechanisms in Information Security”,Philosophy & Technology, 32(4): 627–659.doi:10.1007/s13347-018-0329-z
• Steel, Daniel, 2008,Across the Boundaries: Extrapolation inBiology and Social Science (Environmental Ethics and SciencePolicy Series), Oxford/New York: Oxford University Press.doi:10.1093/acprof:oso/9780195331448.001.0001
• Strevens, Michael, 2008,Depth: An Account of ScientificExplanation, Cambridge, MA: Harvard University Press.doi:10.2307/j.ctv1dv0tnw
• –––, 2013, “No Understanding withoutExplanation”,Studies in History and Philosophy of SciencePart A, 44(3): 510–515.doi:10.1016/j.shpsa.2012.12.005
• Sullivan, Jacqueline A., 2009, “The Multiplicity ofExperimental Protocols: A Challenge to Reductionist andNon-Reductionist Models of the Unity of Neuroscience”,Synthese, 167(3): 511–539.doi:10.1007/s11229-008-9389-4
• Sulloway, Frank J., 1979,Freud, Biologist of the Mind: Beyondthe Psychoanalytic Legend, New York: Basic Books.
• Tabery, James G., 2004, “Synthesizing Activities andInteractions in the Concept of a Mechanism”,Philosophy ofScience, 71(1): 1–15. doi:10.1086/381409
• –––, 2009, “Difference Mechanisms:Explaining Variation with Mechanisms”,Biology &Philosophy, 24(5): 645–664.doi:10.1007/s10539-009-9161-2
• –––, 2014,Beyond Versus: The Struggle toUnderstand the Interaction of Nature and Nurture (Life and Mind:Philosophical Issues in Biology and Psychology), Cambridge, MA: MITPress. doi:10.7551/mitpress/9780262027373.001.0001
• Thagard, Paul, 1998, “Explaining Disease: Causes,Correlations, and Mechanisms”,Minds and Machines, 8:61–78. doi:10.1023/A:1008286314688
• –––, 2000,How Scientists ExplainDisease, Princeton, NJ: Princeton University Press.
• Tobin, Emma, 2017, “Mechanisms and Natural Kinds” inGlennan and Illari 2017b: 198–210 (ch. 15).
• Van Eck, Dingmar and Julie Mennes, 2018, “MechanismDiscovery and Design Explanation: Where Role Function Meets BiologicalAdvantage Function”,Journal for General Philosophy ofScience, 49(3): 413–434. doi:10.1007/s10838-017-9383-y
• Vineis, Paolo, Phyllis Illari, and Federica Russo, 2017,“Causality in Cancer Research: A Journey through Models inMolecular Epidemiology and Their Philosophical Interpretation”,Emerging Themes in Epidemiology, 14(1): article 7.doi:10.1186/s12982-017-0061-7
• Wagner, Günter P., 1996, “Homologues, Natural Kinds andthe Evolution of Modularity”,American Zoologist,36(1): 36–43. doi:10.1093/icb/36.1.36
• Weinberger, Naftali, 2019, “Mechanisms without MechanisticExplanation”,Synthese, 196(6): 2323–2340.doi:10.1007/s11229-017-1538-1
• Weisberg, Michael, 2013,Simulation and Similarity: UsingModels to Understand the World, Oxford/New York: OxfordUniversity Press. doi:10.1093/acprof:oso/9780199933662.001.0001
• Weiskopf, Daniel A., 2011, “Models and Mechanisms inPsychological Explanation”,Synthese, 183(3):313–338. doi:10.1007/s11229-011-9958-9
• Williamson, Jon, 2011, “Mechanistic Theories of CausalityPart I”,Philosophy Compass, 6(6): 421–432.doi:10.1111/j.1747-9991.2011.00400.x
• Wimsatt, William C., 1974, “Complexity andOrganization”, inPSA 1972: Proceedings of the 1972 BiennialMeeting of the Philosophy of Science Association (Boston Studiesin the Philosophy of Science 20), Kenneth F. Schaffner and Robert S.Cohen (eds), Dordrecht: Springer Netherlands, 67–86.doi:10.1007/978-94-010-2140-1_5
• –––, 1976a, “Reductionism, Levels ofOrganization, and the Mind-Body Problem”, inConsciousnessand the Brain: A Scientific and Philosophical Inquiry, Gordon G.Globus, Maxwell Grover, and Irwin Savodnik (eds), New York: PlenumPress, 199–267.
• –––, 1976b, “Reductive Explanation: AFunctional Account”, inPSA 1974: Proceedings of the 1974Biennial Meeting of the Philosophy of Science Association (BostonStudies in the Philosophy of Science 32), R. S. Cohen, C. A. Hooker,A. C. Michalos, and J. W. Van Evra (eds), Dordrecht: SpringerNetherlands, 671–710. doi:10.1007/978-94-010-1449-6_38
• Woodward, Jim [James], 2001, “Law and Explanation inBiology: Invariance Is the Kind of Stability That Matters”,Philosophy of Science, 68(1): 1–20.doi:10.1086/392863
• –––, 2002, “What Is a Mechanism? ACounterfactual Account”,Philosophy of Science, 69(S3):S366–S377. doi:10.1086/341859
• –––, 2003,Making Things Happen: A Theory ofCausal Explanation (Oxford Studies in Philosophy of Science),Oxford/New York: Oxford University Press.doi:10.1093/0195155270.001.0001
• –––, 2014, “A Functional Account ofCausation; or, A Defense of the Legitimacy of Causal Thinking byReference to the Only Standard That Matters—Usefulness (asOpposed to Metaphysics or Agreement with Intuitive Judgment)”,Philosophy of Science, 81(5): 691–713.doi:10.1086/678313
• –––, 2015, “Interventionism and CausalExclusion”,Philosophy and Phenomenological Research,91(2): 303–347. doi:10.1111/phpr.12095
• –––, 2017, “Explanation in Neurobiology:An Interventionist Perspective”, inExplanation andIntegration in Mind and Brain Science, David M. Kaplan (ed.),Oxford: Oxford University Press, 70–100 (ch. 4).
• Woody, Andrea I., 2013, “How Is the Ideal Gas LawExplanatory?”,Science & Education, 22(7):1563–1580. doi:10.1007/s11191-011-9424-6
• Wright, Cory and William Bechtel, 2007, “Mechanisms andPsychological Explanation”, inPhilosophy of Psychology andCognitive Science, Paul Thagard (ed.), Amsterdam/Boston:North-Holland, 31–79. doi:10.1016/B978-044451540-7/50019-0
• Wright, Larry, 1973, “Functions”,ThePhilosophical Review, 82(2): 139–168.doi:10.2307/2183766
• Ylikoski, Petri, 2011, “Social Mechanisms and ExplanatoryRelevance”, in Pierre Demeulenaere (ed.),AnalyticalSociology and Social Mechanisms, Cambridge: Cambridge UniversityPress, pp. 154–172.
• –––, 2013, “Causal and ConstitutiveExplanation Compared”,Erkenntnis, 78(S2):277–297. doi:10.1007/s10670-013-9513-9
• –––, 2017, “Social Mechanisms”, inStuart Glennan and Phyllis Illari (eds.),The Routledge Handbookof Mechanisms and Mechanical Philosophy, London: Routledge, pp.401–412.
• Zednik, Carlos, 2015, “Heuristics, Descriptions, and theScope of Mechanistic Explanation”, inBraillard and Malaterre 2015: 295–318. doi:10.1007/978-94-017-9822-8_13

Academic Tools

	How to cite this entry.
	Preview the PDF version of this entry at theFriends of the SEP Society.
	Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO).
	Enhanced bibliography for this entryatPhilPapers, with links to its database.

Other Internet Resources

[Please contact the authors with suggestions.]

Related Entries

Aristotle, Special Topics: causality |biology: experiment in |biomedicine, philosophy of |Boyle, Robert |causation: and manipulability |causation: counterfactual theories of |causation: in physics |causation: probabilistic |causation: the metaphysics of |cell biology, philosophy of |Descartes, René: physics |emergent properties |empiricism: logical |Gassendi, Pierre |genetics |Helmholtz, Hermann von |laws of nature |laws of nature:ceteris paribus |levels of organization in biology |Lewis, David |Lewis, David: metaphysics |life |medicine, philosophy of |mereology |models in science |molecular biology |natural kinds |Popper, Karl |reduction, scientific |reduction, scientific: in biology |Reichenbach, Hans |Salmon, Wesley |scientific discovery |scientific explanation |scientific method |scientific realism