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Social Mechanism
Social Mechanism Petri Ylikoski, University of Helsinki, Helsinki, Finland Ó 2015 Elsevier Ltd. All rights reserved.
Abstract The basic idea behind mechanism-based explanation is simple at its core, it implies that proper explanations should detail the ‘cogs and wheels’ of the causal process through which the outcome to be explained was brought about. This idea has become increasingly popular in both the social sciences and philosophy of science over the last two decades. At the core of the mechanistic approach is a criticism of widely held views about social scientific explanation, causation, and the nature of social scientific theories. However, it also has interesting methodological implications for the social sciences.
The Rise of Social Mechanisms The idea of mechanism-based explanation in the social sciences does not have a single origin. The idea has become popular in recent philosophy of science (Craver, 2007), and has begun to have an influence on many social scientists. However, the rise of mechanisms in the social sciences predates the wide adoption of the idea by philosophers of science. In fact, a number of authors seem to have adopted the idea independently of each other and have utilized it to criticize explanatory practices in the social sciences. For these authors, the idea of mechanism-based explanation has provided a useful platform for criticizing existing research practices and views about the nature of the social scientific enterprise. Due to these diverse origins, there are many different definitions of social mechanisms (Mahoney, 2001). This diversity is understandable, as the idea of a mechanism has been used to support quite different methodological and theoretical positions (Hedström and Ylikoski, 2010). In a short review such as this, it is only possible to mention some of the more prominent advocates of mechanistic ideas. Jon Elster (1989, 2007) is probably the most influential advocate of mechanisms in the social sciences and his many books are full of examples of mechanism-based thinking in action. For him, the core of this thinking is the idea that mechanistic explanations open up black boxes and show the cogs and wheels of the internal machinery underlying the phenomenon. While much of Elster’s work has focused more on psychological rather than social mechanisms, his work has had a huge impact on how social scientists think about explanation. The critical realism movement (e.g., Sayer, 1992; Archer et al., 1998) has been highly influential with respect to thinking in terms of mechanisms. The original source of its ideas about causation and scientific explanation is Rom Hárre’s (1970) pioneering work, but the principal inspiration for critical realists has been Roy Bhaskar (1978, 1979). His critical realism is a tight philosophical package of ontological and epistemological views in which the idea of mechanistic explanation is the most intuitive part. Within philosophy of the social sciences, both Daniel Little and Mario Bunge have been important spokesmen for mechanistic thinking. Little’s textbook (Little, 1991) and articles (Little, 1998) show in an accessible way how thinking in terms of causal mechanisms helps to solve various methodological
International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Volume 22
problems which characterize other approaches to the social sciences. Little also strongly emphasizes the important role of microfoundations in mechanistic explanations. Bunge’s (1997, 2004) account of mechanistic explanation is part of his more general systemistic philosophy of science. Like Bhaskar, Bunge’s ideas about mechanistic explanations are part of a much larger philosophical system. However, his ideas about mechanisms are quite compatible with what others have said about social mechanisms. In political science, causal mechanisms have played an important role in debates about research methodology and causal inference (e.g., Mahoney, 2001; George and Bennett, 2005; Gerring, 2008). Many authors have presented mechanistic thinking as alternative to statistical methodologies and as a way to provide foundations for case studies and other smallN studies. As Alexander George and Andrew Bennett (2005) suggest, the idea of process tracing is useful for both development and testing of mechanistic explanations, and it is not the case that only statistical evidence is relevant when assessing causal claims. In general, the debate in political science has been quite similar to that in sociology: the advocates of mechanisms have criticized the simplistic empiricist uses of statistical methodology for ignoring the importance of causal process assumptions in causal inference. The volume Social Mechanisms (1998) edited by Peter Hedström and Richard Swedberg is comprised of works by many social scientists who have been important in the development of the mechanistic perspective in social theory. The chapters by Raymond Boudon, Jon Elster, Diego Gambetta, Peter Hedström, Gudmund Hernes, Thomas Schelling, and Aage Sørensen represent what has become known as the analytical approach to social mechanisms. This approach has subsequently developed into analytical sociology movement (Hedström, 2005; Hedström and Bearman, 2009; Hedström and Ylikoski, 2010). While it is still developing and important differences exists between its advocates, the approach currently represents probably the most developed version of the mechanistic approach to social theory.
Mechanism-Based Explanation The idea of mechanism-based explanation has been developed independently among social scientists and philosophers of
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biology (Hedström and Ylikoski, 2010). In the social sciences, the idea of causal mechanism has been used mainly as a tool for exposing the unsatisfactory nature of existing theories and explanatory practices, while in the philosophy of biology the aim has been to find a descriptively adequate account of biological explanation. Despite these separate origins and motivations, both traditions are clearly building on similar ideas about scientific explanation. For example, they share the same dissatisfaction with the covering-law account of explanation (Hedström, 2005; Craver, 2007). In contrast to the covering-law account of explanation (Hempel, 1965), the mechanism-based accounts are explicitly causal. Because of this, they can avoid many problems that have plagued the covering-law account. For example, unlike the covering-law account, the causal theories of explanation do not allow the length of the flagpole to be explained by the length of its shadow. In other words, the causal theories respect the asymmetry of the explanatory relation. The causal requirement also helps with another set of important counterexamples to covering-law account that is based on explanatorily irrelevant information. Thus mechanical approach has a better chance to deal with cases like men eating contraceptive pills and hexed salt that has proved to be embarrassing for the covering-law account (Salmon, 1989; Woodward, 2003). Similarly, the scarcity of real covering laws in sciences like biology, psychology, and the social sciences is not a problem for mechanism-based accounts of explanation. Observable regularities (often called effects) are things to be explained (Cummins, 2000), not the things that are responsible for explanatory understanding. Finally, mechanism-based accounts clearly distinguish between diagnostic and explanatory reasoning. While supporters of the covering-law account propose that the principal difference between explanation and prediction is that in the case of explanation the conclusion of the argument is already know to be true, the mechanism-based account severs the connection: diagnostic effects might allow for prediction, but they are not explanatory, and the sciences provide plentiful examples of explanatory theories that do not allow for precise predictions (e.g., evolutionary theory). No consensus exists on the right definition of a causal mechanism. Although some theorists find such a situation frustrating, it is not clear that this is a real problem. The entities and processes studied by different sciences are quite heterogeneous, and it might be impossible to propose a definition of mechanisms that would be both informative and cover all the prominent examples of mechanisms. Some disciplines, such as cell biology (Bechtel, 2006) and the neurosciences (Craver, 2007), study highly integrated systems, whereas others, such as evolutionary biology and the social sciences, study more dispersed phenomena (Kuorikoski, 2009), so it is more plausible to think that informative characterizations of mechanisms are field specific. However, it is possible to give some general characteristics of mechanisms. First, a mechanism is always a mechanism for something; it is identified by the kind of effect or phenomenon it produces. For this reason the characterization of the effect often requires some care. A standard roulette wheel, for example, does not have different mechanisms for distributing the ball to pockets 16 or 17; rather, the same mechanism produces all 37 possible
outcomes. However, the effect does not uniquely identify the mechanism: often there are multiple alternative mechanisms for producing similar effects. Second, a mechanism is an irreducibly causal notion. It refers to the entities of a causal process that produce the effect of interest. Some definitions of a mechanism make unnecessarily strong assumptions, however. For example, it is sometimes suggested the mechanism is sufficient for the effect. This is an all too strong requirement: a mechanism can involve irreducibly stochastic elements and thus affect only the probability of a given effect. Similarly, the requirement that a mechanism should be unobservable (Mahoney, 2001) is a hasty generalization. There is nothing in the notion of a mechanism that would imply that it is by definition unobservable. Most of the mechanisms constituting an automobile’s engine, for example, are quite visible when one opens up the hood (Mayntz, 2004). Of course, when one appeals to mechanisms to make sense of statistical associations, one is referring to things that are not visible in the data, but this is different from them being unobservable in principle. Third, a mechanism has a structure. When a mechanismbased explanation opens a black box, it makes visible how the participating entities and their properties, activities, and relations produce the effect of interest. More metaphorically, it makes the black box transparent. For this reason, the common suggestion that a mechanism is just an intervening variable misses an important point. The focus on mechanisms breaks up the original explanation-seeking why-question into a series of smaller questions about the causal process: Which entities are participating, and what are their relevant properties; how are the interactions of these entities organized (both spatially and temporally); and what changes in the system or in its environment would prevent or modify the outcome? Fourth, there is a hierarchy of mechanisms. While a mechanism at one level presupposes or takes for granted the existence of certain entities with characteristic properties and activities, it is expected that lower-level mechanisms will explain them. In other words, the explanations employed by one field always bottom out somewhere (Craver, 2007). However, this fundamental status of certain entities, properties, and activities of a given mechanism is only relative, as these are legitimate targets of mechanistic explanation in another field. It is an inherent feature of the mechanistic view that the entities and mechanisms of various sciences are ultimately related to each other. The entities and activities that one theory postulates should be mechanistically explainable by another theory. Although it is sometimes (Kincaid, 1996) suggested that the idea of mechanistic explanation leads to an infinite regress, this is not the case. For a mechanism to be explanatory, it is not required that the entities, properties, and activities that it appeals to are themselves explained. The only requirement is that such entities, properties, and activities really exist; their explanation is a separate question (Ylikoski, 2012). Finally, mechanisms seem to be combinable. Two or more mechanisms can be combined to form a more complicated one. For this reason, we can distinguish between atomic and molecular mechanisms (Elster, 1999, 2007): atomic mechanisms are the simplest mechanisms analyzed within a scientific field, whereas molecular mechanisms combine two or more
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atomic mechanisms. Another way to conceive of the combinatorial nature of mechanisms is to distinguish between causal mechanism schemes and causal scenarios (Ylikoski and Aydinonat, 2014). This distinction captures the fact that there are two different ways to talk about mechanisms. Causal mechanism schemes are theoretically motivated accounts of how certain kind of effect could be produced. These schemes are often rather abstract and they are not assumed to be faithful descriptions of any particular causal process. They basically provide a how-possible explanation; it tells us how an effect of a certain kind could in principle be produced. In contrast, the causal scenarios are meant to be faithful representations of the processes that produce the effect in a particular case. They are often modified adaptations and combinations of more general mechanism schemes. While the fact that both mechanism schemes and causal scenarios are called mechanisms might cause some confusion, their different roles guarantee that neither way of talking about mechanisms is inherently incoherent. While the notion of mechanism is important in understanding explanatory reasoning in science, one should not assume that it is the ultimate solution to all problems in the theory of explanation. Quite the contrary, the mechanistic theory presupposes accounts of explanatory relevance, causation, and the nature of generalizations that provide the basis for mechanisms. The notion of mechanism should not be treated like a black box. For example, a mechanism-based explanation describes the causal process selectively. It does not aim at an exhaustive account of all details but seeks to capture the crucial elements of the process by abstracting away details that are irrelevant. The idea of counterfactual difference making provides a handle on this process. The relevance of entities, their properties, and their interactions are determined by their ability to make a relevant difference to the outcome of interest. If the presence of an entity or of changes in its properties or activities truly makes no difference to the effect to be explained, it can be ignored. A natural way to understand these causal counterfactuals is to understand them as claims about the consequences of ideal causal interventions (Woodward, 2003, 2008).
Mechanisms and Causation The idea of mechanism is also often associated with the analysis of causation. Some authors (e.g., Glennan, 1996) have even attempted to define causation in terms of mechanisms. However, it is prudent to keep these two things apart because most characterizations of mechanisms employ causal notions. A mechanistic definition of causation would be circular, and it would face a thorny question about the causal powers of fundamental (physical) entities. If causal relations at the fundamental level are not mechanical, the definition is false, and if they are mechanical, we end up with an infinite regress, which is regarded by many as an unsatisfactory consequence; with the infinite regress there would be no real causation. The metaphysics of causation is still hotly debated among philosophers, so it is a strength that the mechanistic account of explanation is compatible with a number of different accounts of causation (Hedström and Ylikoski, 2010). However, the
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mechanistic perspective sets some important constraints. For example, the idea of productive causal activities associated with it implies a commitment to the locality of causal processes; whether A is a cause of B depends on facts about the spatiotemporally restricted causal process, not on what would happen in other similar situations. This means that theories which attempt to define causality in terms of regularities (like Hume’s constant conjunction theory, and many probabilistic theories of causation) are not compatible with mechanistic theories. This obviously does not mean that supporters of the mechanistic perspective would have to disregard regularities as an important source of evidence about causal relations. The relevance of mechanism-based reasoning is not restricted to explanation. Mechanisms also play an important role in the justification of causal claims. Especially in nonexperimental contexts, mechanisms have often play a crucial role in distinguishing true causal relations from spurious correlations. Mechanisms are useful in causal inference in two ways. The knowledge that there is a mechanism through which X influences Y supports the inference that X is a cause of Y. In addition, the absence of a plausible mechanism linking X to Y gives us good reason to be suspicious of the relation being a causal one (Hedström, 2005). Understanding mechanisms is also important in the extrapolation of causal findings. Here the assumption about the similarity of causal mechanisms is crucial for making reliable inferences from one setting or population to another (Steel, 2008). While it would be too strong to say that mechanisms are necessary for causal inference, a fully satisfactory social scientific explanation requires that the causal mechanisms be specified. In this context, it makes sense to distinguish between simple causal claims and deeper explanatory claims that are based on an understanding of mechanisms. A simple causal claim tells us about counterfactual dependence: it tells us what would have happened if the cause had been different. In contrast, the mechanism tells us why the counterfactual dependence holds by showing what kinds of entities and processes underlie it. One could say that an account of causal mechanism integrates an isolated piece of causal knowledge into a much larger body of knowledge, and helps us to answer many natural follow-up questions about the conditions under which the causal dependence holds. It is important, however, to emphasize that mechanisms are not a magic bullet for causal inference. In the social sciences, often the problem is not the absence of possible mechanisms, but how to discriminate between a number of potential mechanisms. To avoid lazy mechanistic storytelling, the mechanism scheme must be made both explicit and detailed, and its assumptions must be supported by relevant empirical evidence.
Mechanism-Based Theorizing The idea of causal mechanisms is also related to broader ideas about the growth and organization of scientific knowledge. According to an old but still influential empiricist view, general scientific knowledge consists of empirical generalizations and more abstract theoretical principles from which these generalizations can (ideally) be deduced. This vision is challenged by
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the mechanistic account of knowledge with its emphasis on scientific knowledge as embedded in mechanism schemes and not in empirical generalizations. According to this mechanism-based view, scientific knowledge expands by adding to, or improving upon, items already present in the toolbox of possible causal mechanisms. Understanding accumulates when the knowledge of mechanisms becomes more detailed and the number of known mechanisms increases. The unification of scientific knowledge, then, consists of developing a comprehensive and consistent understanding of the causal structure of the world. Further, the world studied by the social sciences is complex, so the idea of a unified foundational theory should be treated with great suspicion. It is better to focus on theories of a more limited scope that are compatible with knowledge produced by other fields. Rather than dreaming about grand unified theories, social scientists should develop theories about social mechanisms that are mutually compatible and which form an integrated web where mechanisms at lower levels of organization explain the mechanisms that higher-level disciplines take for granted. For example, psychology explains (and corrects) the assumptions that social scientists make about human cognitive processes. The above mechanistic vision of knowledge has much in common with Robert K. Merton’s idea of sociological theories of the middle range (Merton, 1968). These theories are not just low-level empirical working hypotheses, or all-inclusive attempts to fashion comprehensive accounts of society. Rather, they are focused theories about certain aspects of the social world that can also be applied to other cases. They do not provide a comprehensive description of the empirical phenomenon, but instead capture something essential about the factors that explain it. While Merton’s own discussion of theories of the middle range can be ambiguous, the idea of a mechanism-based explanation can be used to make it more precise (Hedström and Udehn, 2009). In the present account, the middle-range theories consist of the causal mechanism schemes discussed above. These schemes are more or less general in the sense that they can be employed and adapted to particular situations and explanatory tasks. The idea of mechanism-based theories of the middle-range is important in the highly specialized and fragmented social sciences. Although empirical data, research methods, and substantial theories differ from one subfield to another, the general ideas concerning possible causal mechanisms are something these fields could share and thereby mutually benefit from. In this scenario, social theory provides a set of explanatory tools that can be employed in and adapted to particular situations and explanatory tasks. The mechanisms are general in the sense that most are not limited to a particular application. For example, the same type of mechanisms can be used for explaining cumulative advantage in scientific careers, success in cultural markets, and other contexts that are characterized by surprising inequalities in outcomes.
Individualism and Social Mechanisms A controversial issue concerning social mechanisms has been its relation to the doctrine of methodological individualism. This
is no surprise, as some of the most visible advocates of mechanism-based explanations (e.g., Elster, Boudon) have also advocated methodological individualism. Furthermore, as mechanism-based explanations open the black boxes underlying macro variables, they are bound to end up with smaller-scale entities like individuals and their activities. However, as Renate Mayntz (2004) has pointed out, there is no reason to assume that mechanistic thinking automatically leads to explanatory factors that are about the intentional actions of individual agents. Mechanisms that cite supraindividual entities or properties are certainly possible. For example, various filtering mechanisms that are analogical to natural selection are difficult to understand other than as population-wide processes, and when the selected units are organizations (for example, firms), it is natural to conceive of the mechanism as supra-individual. Similarly, it is hard to see how explanations that employ the structure of a social network as a crucial explanatory variable are compatible with strictures of methodological individualism. It, therefore, seems that mechanism-based explanations can refer to a variety of macro facts and processes that are not about individuals and their attributes. More generally, once the idea of there being a fixed and unique ‘micro level’ in the social sciences is given up, the association between mechanism-based explanation and methodological individualism vanishes. While a satisfactory mechanism-based explanation in the social sciences might refer to individuals, their relationships, and their actions, it is not guaranteed that they would only refer to the individualistically acceptable properties. Furthermore, the arguments for mechanism-based explanation do not support the assumption that individual intentional explanations have some kind of explanatory privilege. Thus, while Raymond Boudon is expressing a widely felt intuition among methodological individualists when he writes: “When a sociological phenomenon is made the outcome of individual reasons, one does not need to ask further questions” (Boudon, 1998: 177), he is not spelling out an implication of the mechanistic account but rather articulating a doctrine that is in conflict with it. This “intentional fundamentalism” (Ylikoski, 2012) can take various forms, but it is often related to rational choice theory. However, the assumption that human deliberation is a black box that should not be opened is more in line with nineteenth-century hermeneutic romanticism than with explanatorily oriented social science. A mechanistic explanation appeals to micro-level processes, but nothing in the notion of mechanistic explanation implies that these micro things would always be facts about the intentional actions of individuals. Apart from being supraindividual, as argued above, some explanatory mechanisms might also be sub-individual. For example, various biases and heuristics of human cognition (Kahneman, 2011) might have many effects of sociological interest. It would therefore be foolish to limit the explanatory factors available to social scientists to those that can be reconstructed using simple beliefdesire psychology. This argument should not be regarded as challenging the claim that there should exist a division of labor between the social sciences and the sciences of cognition. Rather, it points to the boundaries of this division of labor actually being adjustable and not fixed.
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The above argument does not imply a denial of the fact that intentional explanations are, and will be, an indispensable part of the social scientific explanatory repertoire. From the mechanistic point of view, intentional explanations are legitimate causal explanations. Furthermore, the intentional attitudes of individuals play an important role in most mechanism-based explanations of social phenomena. The idea of social mechanism in itself, however, does not tell us how to conceptualize human action. Rather than relying on some preconceived ideas about human motivation or cognitive processing, the mechanistic perspective suggests that social scientific accounts of human agency should be based on findings and theories from the psychological and cognitive sciences. So while the idea of social mechanisms is quite often associated with rational choice theory (Gross, 2009), from a philosophical and general sociological point of view the connection between the two is rather weak. There is nothing in the idea of a mechanistic explanation that would require the explanation to be articulated in terms of rational choice theory. In fact, the requirement that mechanistic explanations cite the actual causes of the phenomenon to be explained often makes rational choice explanations unacceptable as they are built upon implausible psychological and sociological assumptions (Hedström and Ylikoski, 2010). The key idea of mechanistic explanation is that explanation should represent the essential features of the actual causal structure that produces the observed phenomena. Thus it is not enough that the model ‘saves the phenomena.’ For unrealistic psychological and social assumptions to be acceptable, they must be simplifying idealizations that help the modeling but do not affect the central explanatory relationships in a significant way. This is not often the case in the applications of rational choice theory. However, as gaining an understanding of complex phenomena is only possible in a piecemeal manner, social scientific explanations must abstract away from many details of human mental life. Therefore, only those aspects of cognition that are relevant to the explanatory task at hand should be included in the explanation. In this sense, the explanatory task determines how rich the psychological assumptions must be. So although the mechanistic approach emphasizes the importance of intentional action in the explanation of social phenomena, it cannot subscribe to an axiomatic vision according to which a specific action theory should be used for all purposes. For many social scientific purposes, a relatively simple desire-belief-opportunity model would be sufficient (Hedström, 2005). For other purposes, some version of the pragmatist theory of action (Gross, 2009) could be fruitful.
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causation have received much attention. A consequence of this is that the mechanists have been much clearer about what they oppose than what they advocate. Second, and more importantly, the idea of mechanism-based explanation has been used to advocate improvement in the explanatory practices of the social sciences. For example, analytical sociologists have emphasized the intellectual virtues of clarity and explicitness (Hedström, 2005). Humans are susceptible to illusion of depth of understanding; we tend to overestimate the detail, coherence, and depth of our understanding (Keil, 2003). With its implicit and vague explanatory standards, largely verbal mode of theorizing, and highly complex object of study, social theory is probably a showcase of this failure of metacognition. The extensive debate about social mechanisms shows that while the idea of mechanistic explanation has been helping social scientists to avoid certain philosophical pitfalls, the mere adoption of loose mechanistic parlance will not fulfill its more important promise of improving social scientific explanatory practices. It has turned out that many things people call mechanisms are basically effects; they describe a certain kind of outcome but do not provide much insight into the process that produces it. For example, when Charles Tilly (2001) talks about democratization as a mechanism, it is quite clear that he is describing something he is interested in rather than giving an account of the cogs and wheels the drive that process. However, the problem is much more general: cumulative advantage, invisible hand, segregation, and self-fulfilling prophesy are all processes that are identified by their outcomes rather than their internal workings. The future advancement of the mechanistic agenda in the social sciences requires that social scientists develop an increasingly sophisticated theoretical discussion about causal mechanism schemes. In this endeavor, an abstract definition of social mechanisms will be of little help. What is needed is a cumulative work on the exemplars of social mechanisms. Another challenge is development of a methodology for evaluating mechanism-based causal scenarios. Abstract causal mechanism schemes provide the material for alluring explanatory narratives that might be compelling but lack proper empirical support. The basic problem is that in the social sciences, developing sketchy mechanistic scenarios is rather easy; much more difficult is to articulate these sketches into full-blown explanations and finding evidence that discriminates between competing scenarios. Therefore, much depends on the way in which mechanistic ideas are put to use, otherwise social scientists will end up with mere mechanistic storytelling that lacks both theoretical rigor and empirical relevance.
Future Challenges The recent work on social mechanisms has been motivated by two broad considerations. First, social scientists and philosophers have attempted to replace outdated philosophical ideas about social scientific explanation with more plausible ones. Thus the criticism of the covering-law account of explanation and associated ideas about
See also: Analytical Sociology; Explanation: Conceptions in the Social Sciences; Functional Explanation: Philosophical Aspects; Mechanism-Based Causal Analysis; Methodological Individualism in Sociology; Rational Choice Explanation: Philosophical Aspects; Rational Choice Theory in Sociology; Scientific Explanation; Sociological Theory.
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Abstract The basic idea behind mechanism-based explanation is simple at its core, it implies that proper explanations should detail the ‘cogs and wheels’ of the causal process through which the outcome to be explained was brought about. This idea has become increasingly popular in both the social sciences and philosophy of science over the last two decades. At the core of the mechanistic approach is a criticism of widely held views about social scientific explanation, causation, and the nature of social scientific theories. However, it also has interesting methodological implications for the social sciences.
The Rise of Social Mechanisms The idea of mechanism-based explanation in the social sciences does not have a single origin. The idea has become popular in recent philosophy of science (Craver, 2007), and has begun to have an influence on many social scientists. However, the rise of mechanisms in the social sciences predates the wide adoption of the idea by philosophers of science. In fact, a number of authors seem to have adopted the idea independently of each other and have utilized it to criticize explanatory practices in the social sciences. For these authors, the idea of mechanism-based explanation has provided a useful platform for criticizing existing research practices and views about the nature of the social scientific enterprise. Due to these diverse origins, there are many different definitions of social mechanisms (Mahoney, 2001). This diversity is understandable, as the idea of a mechanism has been used to support quite different methodological and theoretical positions (Hedström and Ylikoski, 2010). In a short review such as this, it is only possible to mention some of the more prominent advocates of mechanistic ideas. Jon Elster (1989, 2007) is probably the most influential advocate of mechanisms in the social sciences and his many books are full of examples of mechanism-based thinking in action. For him, the core of this thinking is the idea that mechanistic explanations open up black boxes and show the cogs and wheels of the internal machinery underlying the phenomenon. While much of Elster’s work has focused more on psychological rather than social mechanisms, his work has had a huge impact on how social scientists think about explanation. The critical realism movement (e.g., Sayer, 1992; Archer et al., 1998) has been highly influential with respect to thinking in terms of mechanisms. The original source of its ideas about causation and scientific explanation is Rom Hárre’s (1970) pioneering work, but the principal inspiration for critical realists has been Roy Bhaskar (1978, 1979). His critical realism is a tight philosophical package of ontological and epistemological views in which the idea of mechanistic explanation is the most intuitive part. Within philosophy of the social sciences, both Daniel Little and Mario Bunge have been important spokesmen for mechanistic thinking. Little’s textbook (Little, 1991) and articles (Little, 1998) show in an accessible way how thinking in terms of causal mechanisms helps to solve various methodological
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problems which characterize other approaches to the social sciences. Little also strongly emphasizes the important role of microfoundations in mechanistic explanations. Bunge’s (1997, 2004) account of mechanistic explanation is part of his more general systemistic philosophy of science. Like Bhaskar, Bunge’s ideas about mechanistic explanations are part of a much larger philosophical system. However, his ideas about mechanisms are quite compatible with what others have said about social mechanisms. In political science, causal mechanisms have played an important role in debates about research methodology and causal inference (e.g., Mahoney, 2001; George and Bennett, 2005; Gerring, 2008). Many authors have presented mechanistic thinking as alternative to statistical methodologies and as a way to provide foundations for case studies and other smallN studies. As Alexander George and Andrew Bennett (2005) suggest, the idea of process tracing is useful for both development and testing of mechanistic explanations, and it is not the case that only statistical evidence is relevant when assessing causal claims. In general, the debate in political science has been quite similar to that in sociology: the advocates of mechanisms have criticized the simplistic empiricist uses of statistical methodology for ignoring the importance of causal process assumptions in causal inference. The volume Social Mechanisms (1998) edited by Peter Hedström and Richard Swedberg is comprised of works by many social scientists who have been important in the development of the mechanistic perspective in social theory. The chapters by Raymond Boudon, Jon Elster, Diego Gambetta, Peter Hedström, Gudmund Hernes, Thomas Schelling, and Aage Sørensen represent what has become known as the analytical approach to social mechanisms. This approach has subsequently developed into analytical sociology movement (Hedström, 2005; Hedström and Bearman, 2009; Hedström and Ylikoski, 2010). While it is still developing and important differences exists between its advocates, the approach currently represents probably the most developed version of the mechanistic approach to social theory.
Mechanism-Based Explanation The idea of mechanism-based explanation has been developed independently among social scientists and philosophers of
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biology (Hedström and Ylikoski, 2010). In the social sciences, the idea of causal mechanism has been used mainly as a tool for exposing the unsatisfactory nature of existing theories and explanatory practices, while in the philosophy of biology the aim has been to find a descriptively adequate account of biological explanation. Despite these separate origins and motivations, both traditions are clearly building on similar ideas about scientific explanation. For example, they share the same dissatisfaction with the covering-law account of explanation (Hedström, 2005; Craver, 2007). In contrast to the covering-law account of explanation (Hempel, 1965), the mechanism-based accounts are explicitly causal. Because of this, they can avoid many problems that have plagued the covering-law account. For example, unlike the covering-law account, the causal theories of explanation do not allow the length of the flagpole to be explained by the length of its shadow. In other words, the causal theories respect the asymmetry of the explanatory relation. The causal requirement also helps with another set of important counterexamples to covering-law account that is based on explanatorily irrelevant information. Thus mechanical approach has a better chance to deal with cases like men eating contraceptive pills and hexed salt that has proved to be embarrassing for the covering-law account (Salmon, 1989; Woodward, 2003). Similarly, the scarcity of real covering laws in sciences like biology, psychology, and the social sciences is not a problem for mechanism-based accounts of explanation. Observable regularities (often called effects) are things to be explained (Cummins, 2000), not the things that are responsible for explanatory understanding. Finally, mechanism-based accounts clearly distinguish between diagnostic and explanatory reasoning. While supporters of the covering-law account propose that the principal difference between explanation and prediction is that in the case of explanation the conclusion of the argument is already know to be true, the mechanism-based account severs the connection: diagnostic effects might allow for prediction, but they are not explanatory, and the sciences provide plentiful examples of explanatory theories that do not allow for precise predictions (e.g., evolutionary theory). No consensus exists on the right definition of a causal mechanism. Although some theorists find such a situation frustrating, it is not clear that this is a real problem. The entities and processes studied by different sciences are quite heterogeneous, and it might be impossible to propose a definition of mechanisms that would be both informative and cover all the prominent examples of mechanisms. Some disciplines, such as cell biology (Bechtel, 2006) and the neurosciences (Craver, 2007), study highly integrated systems, whereas others, such as evolutionary biology and the social sciences, study more dispersed phenomena (Kuorikoski, 2009), so it is more plausible to think that informative characterizations of mechanisms are field specific. However, it is possible to give some general characteristics of mechanisms. First, a mechanism is always a mechanism for something; it is identified by the kind of effect or phenomenon it produces. For this reason the characterization of the effect often requires some care. A standard roulette wheel, for example, does not have different mechanisms for distributing the ball to pockets 16 or 17; rather, the same mechanism produces all 37 possible
outcomes. However, the effect does not uniquely identify the mechanism: often there are multiple alternative mechanisms for producing similar effects. Second, a mechanism is an irreducibly causal notion. It refers to the entities of a causal process that produce the effect of interest. Some definitions of a mechanism make unnecessarily strong assumptions, however. For example, it is sometimes suggested the mechanism is sufficient for the effect. This is an all too strong requirement: a mechanism can involve irreducibly stochastic elements and thus affect only the probability of a given effect. Similarly, the requirement that a mechanism should be unobservable (Mahoney, 2001) is a hasty generalization. There is nothing in the notion of a mechanism that would imply that it is by definition unobservable. Most of the mechanisms constituting an automobile’s engine, for example, are quite visible when one opens up the hood (Mayntz, 2004). Of course, when one appeals to mechanisms to make sense of statistical associations, one is referring to things that are not visible in the data, but this is different from them being unobservable in principle. Third, a mechanism has a structure. When a mechanismbased explanation opens a black box, it makes visible how the participating entities and their properties, activities, and relations produce the effect of interest. More metaphorically, it makes the black box transparent. For this reason, the common suggestion that a mechanism is just an intervening variable misses an important point. The focus on mechanisms breaks up the original explanation-seeking why-question into a series of smaller questions about the causal process: Which entities are participating, and what are their relevant properties; how are the interactions of these entities organized (both spatially and temporally); and what changes in the system or in its environment would prevent or modify the outcome? Fourth, there is a hierarchy of mechanisms. While a mechanism at one level presupposes or takes for granted the existence of certain entities with characteristic properties and activities, it is expected that lower-level mechanisms will explain them. In other words, the explanations employed by one field always bottom out somewhere (Craver, 2007). However, this fundamental status of certain entities, properties, and activities of a given mechanism is only relative, as these are legitimate targets of mechanistic explanation in another field. It is an inherent feature of the mechanistic view that the entities and mechanisms of various sciences are ultimately related to each other. The entities and activities that one theory postulates should be mechanistically explainable by another theory. Although it is sometimes (Kincaid, 1996) suggested that the idea of mechanistic explanation leads to an infinite regress, this is not the case. For a mechanism to be explanatory, it is not required that the entities, properties, and activities that it appeals to are themselves explained. The only requirement is that such entities, properties, and activities really exist; their explanation is a separate question (Ylikoski, 2012). Finally, mechanisms seem to be combinable. Two or more mechanisms can be combined to form a more complicated one. For this reason, we can distinguish between atomic and molecular mechanisms (Elster, 1999, 2007): atomic mechanisms are the simplest mechanisms analyzed within a scientific field, whereas molecular mechanisms combine two or more
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atomic mechanisms. Another way to conceive of the combinatorial nature of mechanisms is to distinguish between causal mechanism schemes and causal scenarios (Ylikoski and Aydinonat, 2014). This distinction captures the fact that there are two different ways to talk about mechanisms. Causal mechanism schemes are theoretically motivated accounts of how certain kind of effect could be produced. These schemes are often rather abstract and they are not assumed to be faithful descriptions of any particular causal process. They basically provide a how-possible explanation; it tells us how an effect of a certain kind could in principle be produced. In contrast, the causal scenarios are meant to be faithful representations of the processes that produce the effect in a particular case. They are often modified adaptations and combinations of more general mechanism schemes. While the fact that both mechanism schemes and causal scenarios are called mechanisms might cause some confusion, their different roles guarantee that neither way of talking about mechanisms is inherently incoherent. While the notion of mechanism is important in understanding explanatory reasoning in science, one should not assume that it is the ultimate solution to all problems in the theory of explanation. Quite the contrary, the mechanistic theory presupposes accounts of explanatory relevance, causation, and the nature of generalizations that provide the basis for mechanisms. The notion of mechanism should not be treated like a black box. For example, a mechanism-based explanation describes the causal process selectively. It does not aim at an exhaustive account of all details but seeks to capture the crucial elements of the process by abstracting away details that are irrelevant. The idea of counterfactual difference making provides a handle on this process. The relevance of entities, their properties, and their interactions are determined by their ability to make a relevant difference to the outcome of interest. If the presence of an entity or of changes in its properties or activities truly makes no difference to the effect to be explained, it can be ignored. A natural way to understand these causal counterfactuals is to understand them as claims about the consequences of ideal causal interventions (Woodward, 2003, 2008).
Mechanisms and Causation The idea of mechanism is also often associated with the analysis of causation. Some authors (e.g., Glennan, 1996) have even attempted to define causation in terms of mechanisms. However, it is prudent to keep these two things apart because most characterizations of mechanisms employ causal notions. A mechanistic definition of causation would be circular, and it would face a thorny question about the causal powers of fundamental (physical) entities. If causal relations at the fundamental level are not mechanical, the definition is false, and if they are mechanical, we end up with an infinite regress, which is regarded by many as an unsatisfactory consequence; with the infinite regress there would be no real causation. The metaphysics of causation is still hotly debated among philosophers, so it is a strength that the mechanistic account of explanation is compatible with a number of different accounts of causation (Hedström and Ylikoski, 2010). However, the
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mechanistic perspective sets some important constraints. For example, the idea of productive causal activities associated with it implies a commitment to the locality of causal processes; whether A is a cause of B depends on facts about the spatiotemporally restricted causal process, not on what would happen in other similar situations. This means that theories which attempt to define causality in terms of regularities (like Hume’s constant conjunction theory, and many probabilistic theories of causation) are not compatible with mechanistic theories. This obviously does not mean that supporters of the mechanistic perspective would have to disregard regularities as an important source of evidence about causal relations. The relevance of mechanism-based reasoning is not restricted to explanation. Mechanisms also play an important role in the justification of causal claims. Especially in nonexperimental contexts, mechanisms have often play a crucial role in distinguishing true causal relations from spurious correlations. Mechanisms are useful in causal inference in two ways. The knowledge that there is a mechanism through which X influences Y supports the inference that X is a cause of Y. In addition, the absence of a plausible mechanism linking X to Y gives us good reason to be suspicious of the relation being a causal one (Hedström, 2005). Understanding mechanisms is also important in the extrapolation of causal findings. Here the assumption about the similarity of causal mechanisms is crucial for making reliable inferences from one setting or population to another (Steel, 2008). While it would be too strong to say that mechanisms are necessary for causal inference, a fully satisfactory social scientific explanation requires that the causal mechanisms be specified. In this context, it makes sense to distinguish between simple causal claims and deeper explanatory claims that are based on an understanding of mechanisms. A simple causal claim tells us about counterfactual dependence: it tells us what would have happened if the cause had been different. In contrast, the mechanism tells us why the counterfactual dependence holds by showing what kinds of entities and processes underlie it. One could say that an account of causal mechanism integrates an isolated piece of causal knowledge into a much larger body of knowledge, and helps us to answer many natural follow-up questions about the conditions under which the causal dependence holds. It is important, however, to emphasize that mechanisms are not a magic bullet for causal inference. In the social sciences, often the problem is not the absence of possible mechanisms, but how to discriminate between a number of potential mechanisms. To avoid lazy mechanistic storytelling, the mechanism scheme must be made both explicit and detailed, and its assumptions must be supported by relevant empirical evidence.
Mechanism-Based Theorizing The idea of causal mechanisms is also related to broader ideas about the growth and organization of scientific knowledge. According to an old but still influential empiricist view, general scientific knowledge consists of empirical generalizations and more abstract theoretical principles from which these generalizations can (ideally) be deduced. This vision is challenged by
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the mechanistic account of knowledge with its emphasis on scientific knowledge as embedded in mechanism schemes and not in empirical generalizations. According to this mechanism-based view, scientific knowledge expands by adding to, or improving upon, items already present in the toolbox of possible causal mechanisms. Understanding accumulates when the knowledge of mechanisms becomes more detailed and the number of known mechanisms increases. The unification of scientific knowledge, then, consists of developing a comprehensive and consistent understanding of the causal structure of the world. Further, the world studied by the social sciences is complex, so the idea of a unified foundational theory should be treated with great suspicion. It is better to focus on theories of a more limited scope that are compatible with knowledge produced by other fields. Rather than dreaming about grand unified theories, social scientists should develop theories about social mechanisms that are mutually compatible and which form an integrated web where mechanisms at lower levels of organization explain the mechanisms that higher-level disciplines take for granted. For example, psychology explains (and corrects) the assumptions that social scientists make about human cognitive processes. The above mechanistic vision of knowledge has much in common with Robert K. Merton’s idea of sociological theories of the middle range (Merton, 1968). These theories are not just low-level empirical working hypotheses, or all-inclusive attempts to fashion comprehensive accounts of society. Rather, they are focused theories about certain aspects of the social world that can also be applied to other cases. They do not provide a comprehensive description of the empirical phenomenon, but instead capture something essential about the factors that explain it. While Merton’s own discussion of theories of the middle range can be ambiguous, the idea of a mechanism-based explanation can be used to make it more precise (Hedström and Udehn, 2009). In the present account, the middle-range theories consist of the causal mechanism schemes discussed above. These schemes are more or less general in the sense that they can be employed and adapted to particular situations and explanatory tasks. The idea of mechanism-based theories of the middle-range is important in the highly specialized and fragmented social sciences. Although empirical data, research methods, and substantial theories differ from one subfield to another, the general ideas concerning possible causal mechanisms are something these fields could share and thereby mutually benefit from. In this scenario, social theory provides a set of explanatory tools that can be employed in and adapted to particular situations and explanatory tasks. The mechanisms are general in the sense that most are not limited to a particular application. For example, the same type of mechanisms can be used for explaining cumulative advantage in scientific careers, success in cultural markets, and other contexts that are characterized by surprising inequalities in outcomes.
Individualism and Social Mechanisms A controversial issue concerning social mechanisms has been its relation to the doctrine of methodological individualism. This
is no surprise, as some of the most visible advocates of mechanism-based explanations (e.g., Elster, Boudon) have also advocated methodological individualism. Furthermore, as mechanism-based explanations open the black boxes underlying macro variables, they are bound to end up with smaller-scale entities like individuals and their activities. However, as Renate Mayntz (2004) has pointed out, there is no reason to assume that mechanistic thinking automatically leads to explanatory factors that are about the intentional actions of individual agents. Mechanisms that cite supraindividual entities or properties are certainly possible. For example, various filtering mechanisms that are analogical to natural selection are difficult to understand other than as population-wide processes, and when the selected units are organizations (for example, firms), it is natural to conceive of the mechanism as supra-individual. Similarly, it is hard to see how explanations that employ the structure of a social network as a crucial explanatory variable are compatible with strictures of methodological individualism. It, therefore, seems that mechanism-based explanations can refer to a variety of macro facts and processes that are not about individuals and their attributes. More generally, once the idea of there being a fixed and unique ‘micro level’ in the social sciences is given up, the association between mechanism-based explanation and methodological individualism vanishes. While a satisfactory mechanism-based explanation in the social sciences might refer to individuals, their relationships, and their actions, it is not guaranteed that they would only refer to the individualistically acceptable properties. Furthermore, the arguments for mechanism-based explanation do not support the assumption that individual intentional explanations have some kind of explanatory privilege. Thus, while Raymond Boudon is expressing a widely felt intuition among methodological individualists when he writes: “When a sociological phenomenon is made the outcome of individual reasons, one does not need to ask further questions” (Boudon, 1998: 177), he is not spelling out an implication of the mechanistic account but rather articulating a doctrine that is in conflict with it. This “intentional fundamentalism” (Ylikoski, 2012) can take various forms, but it is often related to rational choice theory. However, the assumption that human deliberation is a black box that should not be opened is more in line with nineteenth-century hermeneutic romanticism than with explanatorily oriented social science. A mechanistic explanation appeals to micro-level processes, but nothing in the notion of mechanistic explanation implies that these micro things would always be facts about the intentional actions of individuals. Apart from being supraindividual, as argued above, some explanatory mechanisms might also be sub-individual. For example, various biases and heuristics of human cognition (Kahneman, 2011) might have many effects of sociological interest. It would therefore be foolish to limit the explanatory factors available to social scientists to those that can be reconstructed using simple beliefdesire psychology. This argument should not be regarded as challenging the claim that there should exist a division of labor between the social sciences and the sciences of cognition. Rather, it points to the boundaries of this division of labor actually being adjustable and not fixed.
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The above argument does not imply a denial of the fact that intentional explanations are, and will be, an indispensable part of the social scientific explanatory repertoire. From the mechanistic point of view, intentional explanations are legitimate causal explanations. Furthermore, the intentional attitudes of individuals play an important role in most mechanism-based explanations of social phenomena. The idea of social mechanism in itself, however, does not tell us how to conceptualize human action. Rather than relying on some preconceived ideas about human motivation or cognitive processing, the mechanistic perspective suggests that social scientific accounts of human agency should be based on findings and theories from the psychological and cognitive sciences. So while the idea of social mechanisms is quite often associated with rational choice theory (Gross, 2009), from a philosophical and general sociological point of view the connection between the two is rather weak. There is nothing in the idea of a mechanistic explanation that would require the explanation to be articulated in terms of rational choice theory. In fact, the requirement that mechanistic explanations cite the actual causes of the phenomenon to be explained often makes rational choice explanations unacceptable as they are built upon implausible psychological and sociological assumptions (Hedström and Ylikoski, 2010). The key idea of mechanistic explanation is that explanation should represent the essential features of the actual causal structure that produces the observed phenomena. Thus it is not enough that the model ‘saves the phenomena.’ For unrealistic psychological and social assumptions to be acceptable, they must be simplifying idealizations that help the modeling but do not affect the central explanatory relationships in a significant way. This is not often the case in the applications of rational choice theory. However, as gaining an understanding of complex phenomena is only possible in a piecemeal manner, social scientific explanations must abstract away from many details of human mental life. Therefore, only those aspects of cognition that are relevant to the explanatory task at hand should be included in the explanation. In this sense, the explanatory task determines how rich the psychological assumptions must be. So although the mechanistic approach emphasizes the importance of intentional action in the explanation of social phenomena, it cannot subscribe to an axiomatic vision according to which a specific action theory should be used for all purposes. For many social scientific purposes, a relatively simple desire-belief-opportunity model would be sufficient (Hedström, 2005). For other purposes, some version of the pragmatist theory of action (Gross, 2009) could be fruitful.
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causation have received much attention. A consequence of this is that the mechanists have been much clearer about what they oppose than what they advocate. Second, and more importantly, the idea of mechanism-based explanation has been used to advocate improvement in the explanatory practices of the social sciences. For example, analytical sociologists have emphasized the intellectual virtues of clarity and explicitness (Hedström, 2005). Humans are susceptible to illusion of depth of understanding; we tend to overestimate the detail, coherence, and depth of our understanding (Keil, 2003). With its implicit and vague explanatory standards, largely verbal mode of theorizing, and highly complex object of study, social theory is probably a showcase of this failure of metacognition. The extensive debate about social mechanisms shows that while the idea of mechanistic explanation has been helping social scientists to avoid certain philosophical pitfalls, the mere adoption of loose mechanistic parlance will not fulfill its more important promise of improving social scientific explanatory practices. It has turned out that many things people call mechanisms are basically effects; they describe a certain kind of outcome but do not provide much insight into the process that produces it. For example, when Charles Tilly (2001) talks about democratization as a mechanism, it is quite clear that he is describing something he is interested in rather than giving an account of the cogs and wheels the drive that process. However, the problem is much more general: cumulative advantage, invisible hand, segregation, and self-fulfilling prophesy are all processes that are identified by their outcomes rather than their internal workings. The future advancement of the mechanistic agenda in the social sciences requires that social scientists develop an increasingly sophisticated theoretical discussion about causal mechanism schemes. In this endeavor, an abstract definition of social mechanisms will be of little help. What is needed is a cumulative work on the exemplars of social mechanisms. Another challenge is development of a methodology for evaluating mechanism-based causal scenarios. Abstract causal mechanism schemes provide the material for alluring explanatory narratives that might be compelling but lack proper empirical support. The basic problem is that in the social sciences, developing sketchy mechanistic scenarios is rather easy; much more difficult is to articulate these sketches into full-blown explanations and finding evidence that discriminates between competing scenarios. Therefore, much depends on the way in which mechanistic ideas are put to use, otherwise social scientists will end up with mere mechanistic storytelling that lacks both theoretical rigor and empirical relevance.
Future Challenges The recent work on social mechanisms has been motivated by two broad considerations. First, social scientists and philosophers have attempted to replace outdated philosophical ideas about social scientific explanation with more plausible ones. Thus the criticism of the covering-law account of explanation and associated ideas about
See also: Analytical Sociology; Explanation: Conceptions in the Social Sciences; Functional Explanation: Philosophical Aspects; Mechanism-Based Causal Analysis; Methodological Individualism in Sociology; Rational Choice Explanation: Philosophical Aspects; Rational Choice Theory in Sociology; Scientific Explanation; Sociological Theory.
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