Recent thinking about scientific explanation

What do we want from a scientific explanation?  Is there a single answer to this question, or is the field of explanation fundamentally heterogeneous, perhaps by discipline or by research community? Do biologists explain outcomes differently from physicists or sociologists? Is a good explanation within the Anglo-American traditions of science also a good explanation in the German or Chinese research communities? Is the idea of a scientific explanation paradigm-dependent?

For several decades in the twentieth century there was a dominant answer to this question, that was an outgrowth of the tradition of logical positivism and examples from the natural sciences. This theory of explanation focused on the idea of subsumption of an event or regularity under a higher-level set of laws. The deductive-nomological theory of explanation specified that an outcome is explained when we have produced a deductively valid argument with premises that include at least one general law and that lead to a description of the event as conclusion. Carl Hempel was the most prominent advocate for this theory (Aspects of Scientific Explanation), but it was widely accepted throughout the philosophy of science in the 1950s and 1960s.  The "covering law" model was a core dogma for the philosophy of science for several decades.

The D-N theory was subject to many kinds of criticisms, including the obvious point that much explanation involves phenomena that are probabilistic rather than deterministic.  Hempel introduced the inductive version of the D-N model to cover probabilistic-statistical explanation, along these lines. An argument provides a scientific explanation of E if it provides at least one probabilistic law and a set of background conditions such that, given the law and conditions, E is highly probable.  This model was described as the "Inductive-Statistical" model (I-S model).  Wesley Salmon's Scientific Explanation and the Causal Structure of the World falls within this tradition but offers important refinements, including his formal definition of causal relevance.

In each case the motivation for the theory of explanation is a plausible one: we explain an event when we show how it was necessary [or highly probable] in the circumstances, given existing conditions and relevant laws of nature. On the logical positivist approach, an explanation is an answer to a "why necessary" question: why did this event occur? In this conception of explanation the idea of necessity or probability is replaced with the idea of deductive or inductive derivability -- a syntactic relationship among sets of sentences.

A different approach to explanation turns to the idea of causation.  We provide an explanation of an event or pattern when we succeed in identifying the causal conditions and events that brought it about.  This approach can be tied to the D-N approach, if we believe that all causal relations are the manifestation of strict or probabilistic causal regularities.  But not all D-N explanations are causal, and not all causal explanations invoke regularities.  Derivability is no longer the criterion of explanatory success, and explanation is no longer primarily a syntactic relation between sets of sentences.  Instead, substantive theories of causal powers and properties are the foundation of scientific explanation.  A leading exponent of this view is Rom Harré in Harré and Madden, Causal Powers: Theory of Natural Necessity. Nancy Cartwright's Nature's Capacities and Their Measurements is also an important contribution to this view.  And J. L. Mackie's The Cement of the Universe: A Study of Causation is an important contribution as well.  The causal approach retains the idea that explanation involves showing why an event is necessary or probable, but it turns from derivability from statements of laws of nature, to theories of causal powers and properties.

The causal mechanisms approach to explanation continues the insight that explanations involve demonstrating why an event occurred; but this approach moves even farther away from the idea of a causal law, replacing it with the idea of a discrete causal mechanism.  On this approach, we explain an event when we identify a series of causal interactions that lead from some antecedent condition to the outcome of interest.  Hedstrom and Swedborg's Social Mechanisms: An Analytical Approach to Social Theory presents aspects of this theory of explanation in application to the social sciences.  One benefit of the social mechanisms approach is that it also provides a basis for answering "how possible" questions: if our puzzlement is that an outcome has occurred that seems inherently unlikely, we can provide an account of a set of causal mechanisms that transpired to bring it about.

The chief line of dispute in the traditions mentioned so far is between the "general laws" camp and the "causal powers" camp.  Both are committed to the idea that explanation involves showing how an outcome fits into the ways the world works; but the general laws approach presumes that law-like regularities are fundamental, whereas the causal approach presumes that causal powers and mechanisms are fundamental.

So what has developed in the theory of explanation in the past twenty years? Quite a bit. A recent collection of essays coming largely from the Scandinavian tradition of the philosophy of science is quite helpful in orienting readers to recent developments. This is Johannes Persson and Petri Ylikoski's 2007  Rethinking Explanation. Quite a number of the contributions are worth reading carefully.  But Jan Faye's "Pragmatic-Rhetorical Theory of Explanation" is a good place to start.  Faye distinguishes among three basic approaches to the theory of explanation: formal-logical, ontological, and pragmatic.  The formal-logical approach is essentially the H-D and I-S approaches described above.  The ontological approach is the causal-powers approach described above.  The pragmatic approach is in a sense the most important recent contribution to the theory of explanation, and represents a significant re-focusing of the debates in post-empiricist philosophy of science. Here is how Faye describes the pragmatic approach to explanation-theory:

The pragmatic view sees scientific explanations to be basically similar to explanations in everyday life. It regards every explanation as an appropriate answer to an explanation-seeking question, emphasising that the context of the discourse, including the explainer’s interest and background knowledge, determines the appropriate answer. (44)

And why should we consider a pragmatic approach?  Faye offers eight reasons:

First, we have to recognise that even within the natural sciences there exist many different types of accounts, which scientists regard as explanatory. (46)

Second, if one is looking for a prescriptive treatment of explanation, I see no reason why the social sciences and the humanities should be excluded from such a prescription. If they are included, the prescriptive account must include intentional and interpretive explanations, i.e., accounts providing information about either motives or meanings. (47)

Third, the meaning of a why-question alone does not determine whether the answer is relevant or not. (47)

Fourth, John Searle has correctly argued that the meaning of every indicative sentence is context-dependent. He does not deny that many sentences have literal meaning, which is traditionally seen as the semantic content a sentence has independently of any context. (49)

Fifth, many explanations take the form of stories. Arthur Danto has argued that what we want to explain is always a change of some sort. When a change occurs, we have one situation before and another situation after, and the explanation is what connects these two situations. This is the story. (50)

Sixth, a change always takes place in a complex causal field of circumstances each of which is necessary for its occurrence. Writers like P.W. Bridgman, Norwood Russell Hanson, John Mackie, and Bas van Fraassen have all correctly argued that events are enmeshed in a causal network and that it is the salient factors mentioned in an explanation that constitute the causes of that events. (50)

Seventh, the level of explanation depends also on our interest of communication. In science an appropriate nomic or causal account can be given on the basis of different explanatory levels, and which of these levels one selects as informative depends very much on the rhetorical purposes. (51)

Eight, scientific theories are empirically underdetermined by data. It is always possible to develop competing theories that explain things differently and, therefore, it is impossible to set up a crucial experiment that shows which of these theories that yields the correct account of the data available. (52)

Faye then goes on to analyze scientific explanation as a speech act. We need to understand the presuppositions and purposes that the explainer and the listener have, before we can say much about how the explanation works.

Petri Ylikoski's contribution to the volume, "The Idea of Contrastive Explanandum," picks up on one particular but pervasively important feature of the rhetorical situation of explanation, the idea of contrast.  When we ask for an explanation of an outcome, often we are not asking simply why it occurred, but rather why it occurred instead of something else.  And the contrastive condition is crucial.  If we ask "why did the Prussian army win the Franco-Prussian War?", the answer we give will be very different depending on whether we understand the question as:

"Why did the Prussian army [rather than the French army] win the Franco-Prussian War?"

or:

"Why did the Prussian army win [rather than fighting to stalemate] the Franco-Prussian War?"

So scientific explanation is context-dependent in at least this important respect: we need to understand what the question-asker has in mind before we can provide an adequate explanation from his/her point of view. As Henrik Hallsten puts it in his contribution, "What to Ask of an Explanation-Theory",

To summarize: Any explanation-theory must [do] justice to the distinction between objective explanatory relevance and context dependent explanatory relevance or provide good arguments as to why this distinction should not be upheld. (16)

So perhaps the most important recent developments in the theory of scientific explanation fall in a few categories.  First, there has been substantial work on refining the idea of causal explanation (link).  Second, philosophers have reinforced the idea that explanation has pragmatic and rhetorical aspects that cannot be put aside in favor of syntactic and substantive features of explanation. And third, there is more recognition and acceptance of the idea that explanatory models and standards may reasonably differ across disciplines and research areas.  In particular, the social and historical sciences are entitled to offer explanatory frameworks that are well adapted to the particular kinds of why and how questions that are posed in these fields.   In each case the philosophy of science has made a very great deal of progress since the state of the debates about explanation that transpired in the 1960s.

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Posted in: CAT_explanation,causal mechanism

Current issues in causation research

This week's conference on Causality and Explanation in the Sciences in Ghent was an unusually good academic meeting (link). Participants gathered from all over Europe, as well as a few from North America, Australia, and South Africa, to debate the logic and substance of causal interpretations of the world. Among other things, it provided all participants with a very good sense of the ideas about causation that are generating the most discussion today.

A general perception that emerges from the gestalt of papers at the conference is that there are three large focus areas in current research on scientific causation. First, there is interest in specifying what causal assertions and concepts mean in scientific explanations. What are the logical, conceptual, and pragmatic issues associated with causal assertions and explanations?

Second, there is a large body of work focusing on the methods we can use to support causal inference in the sciences. Every field of science produces volumes of data about variables and events over time. What methods exist to permit inferences about causal relationships among the observed variables and entities? This includes causal modeling statistical methods, but also comparative methods deriving from Mill's methods of difference and similarity.

Third, there is a group of philosophers and scientists who are primarily interested in the ontology of causation in various parts of the sciences. How do various factors exercise causal powers in ecology, the social sciences, or complex systems? Researchers in these areas need provisional answers to questions raised by the first two groups, but their focus is on substantive causal processes rather than the logic of causal statements.

It is useful to inventory half a dozen approaches that were repeatedly cited. This survey is impressionistic but gives an idea of the current landscape.

The mechanisms approach. The idea that we can explicate causation through the idea of a mechanism has been rising in importance over the past twenty years. The idea here is that the fundamental causal concept is that of a mechanism through which X brings about or produces Y. This is argued to be key to causation from single-case studies to large statistical studies suggesting a causal relationship between two or more variables. Peter Hedstrom and other exponents of analytical sociology are recent voices for this approach for the social sciences, though expositions of this approach don't usually go into the level of detail expected by philosophers like Woodward and Cartwright. An important paper by Peter Machamer, Lindley Darden and Carl Craver, "Thinking about Mechanisms", sets the terms of current technical discussions; their view is referred to as the MDC theory. A common concern is that the approach hasn't been as clear as it should be about what precisely a mechanism is. James Mahoney made this criticism in 2001 in "Beyond Correlational Analysis" reviewing Charles Ragin, Fuzzy-Set Social Science and Peter Hedstrom and Richard Swedberg, Social Mechanisms: An Analytical Approach to Social Theory (link), and we still need a more generally recognized specification of the idea. (See an earlier post on this approach; link.)

The manipulability account. Jim Woodward is perhaps the leading exponent of the manipulability (or interventionist) account. He develops his views in detail in his recent book, Making Things Happen: A Theory of Causal Explanation. The view is an intuitively plausible one: causal claims have to do with judgments about how the world would be if we altered certain circumstances. If we observe that the concentration of sulphuric acid is increasing in the atmosphere, we might consider the increasing volume of H2SO4 released by coal power plants from 1960 to 1990. And we might speculate that there is a causal connection between these facts. A counterfactual causal statement holds that: If X (increasing emissions) had not occurred, then Y (increasing acid rain) would not have occurred. The manipulability theory adds this point: if we could remove X from the sequence, then we would alter the value of Y. And this in turn makes good sense of the ways in which we design controlled experiments.

Difference-making. Another strand of thinking about causation focuses on the explanations we are looking for when we ask about the cause of some outcome. Here philosophers note that there are vastly many conditions that are causally necessary for an event but do not count as being explanatory. Lee Harvey Oswald was alive when he fired his rifle in Dallas; but this doesn't play an explanatory role in the assassination of Kennedy. Crudely speaking, we want to know which causal factors were salient; which factors made a difference in the outcome. Michael Strevens provides a detailed and innovative explication of this set of intuitions in his recent book Depth: An Account of Scientific Explanation, where he introduces his theory of "Kairetic" explanation.

Contrastive analysis as a theory of explanation. When we seek an explanation of something, we generally have something specific in mind: why X rather than X'? And an explanation that keys off the wrong contrast will fail, even though its premises are correct. Bas van Fraassen (1980), The Scientific Image, is often cited in this context. A conference participant, Petri Ylikoski, develops a contrastive counterfactual theory in his dissertation (link). This body of work seeks to clarify pragmatic issues concerning explanation, including understanding and explanatory relevance. If we ask for an explanation for why X occurred, we are usually presupposing a question like this:

Why did X occur [rather than Y]?

• Why is John carrying his umbrella [rather than not]?
• Why is John carrying his umbrella [rather than his raincoat]?
• Why is John carrying his umbrella [rather than his assistant Harry]?

These all demand different answers:

• Because he expects rain;
• Because it is too warm for a raincoat;
• Because Harry is carrying three heavy suitcases.

Here is a much-cited review article by Nancy Cartwright on van Fraasen's work (link), and here is a discussion of contrastive explanation by Jonathan Schaffer (link).

Causal modeling theory. This topic refers to the large body of statistical theory devoted to identifying potential causal relationships among observable variables in a large data set. Hubert Blalock is a founder of this approach (Causal Inferences in Nonexperimental Research; 1964) with his statistical models for causal path analysis. (Here is a short account of the history of path analysis in genetics.) Judea Pearl has contributed a great deal to the method of structural equation modeling (SEM) in Causality: Models, Reasoning and Inference and elsewhere. Here is a handbook article in which he explains the method and its causal relevance (link). Pearl maintains a research blog on causality here. Granger causality is a specific technique for assessing causal relationships within time series data: X Granger-causes Y if variations in X and Y together do a better job of predicting Y than variations in Y by itself.

Prior foundations of philosophical theories of causation. Two older discussions of causality also received some notice in these papers: J. L. Mackie on INUS conditions and causal fields (The Cement of the Universe: A Study of Causation) and Wesley Salmon on the causal structure of the world (Scientific Explanation and the Causal Structure of the World).

Nancy Cartwright's "Causation: One Word, Many Things" provides a very good contemporary review of the varieties of approaches that are currently being taken to the idea of causation (link).

Much of the intellectual vitality of this group of philosophers is captured in the major work recently edited by Phyllis McKay Illari, Federica Russo, and John Williamson, Causality in the Sciences. The book contains a very wide range of disciplines and approaches in its treatment of the topic.

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Posted in: CAT_explanation,causal mechanism,causal reasoning

Woodward on mechanisms

Jim Woodward has extended a lot of his philosophical effort towards the task of understanding causation in the sciences (Making Things Happen: A Theory of Causal Explanation). Woodward is a primary exponent of the "manipulationist" theory of causation. He brings a counterfactual orientation to the problem of defining causal relations. If we assert that X caused Y, there is an implication that, if X had not occurred, Y would not have occurred. This implication isn't universally valid, since some events or outcomes are causally overdetermined. (Both X and X' may be a sufficient cause for Y -- in which case removing X still allows for Y through the X' pathway.) Notwithstanding this problem, the counterfactual nature of causal assertions is widely recognized. And this implies the association between causation and intervention or manipulation: if X causes Y, then we should be able to influence the occurrence of Y by manipulating X. This fact in turn underlies the logic of experimental design.

Woodward's treatment of causation deserves fuller treatment than I'll give it here. In this post I will focus on his application of these ideas to the notion of a causal mechanism. He lays this treatment out in a short but influential article, "What is a Mechanism? A Counterfactual Account" (link).

Here is the core idea. He focuses on the Machamer-Darden-Craver (MDC) definition of a causal mechanism (link):

Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions. (3)

Woodward's contribution is to give greater clarity to the idea of regularity or law by adding the idea of a relationship that is "invariant under intervention". This idea models the notion of experimental testing of a causal hypothesis. We are interested in "X causes Y". We look for interventions that change the state of Y. If we find that the only interventions that change Y, do so through their ability to change X, then the X-Y relation is said to be invariant under intervention, and X is said to cause Y. Here is how he expresses the idea in the article:

I understand this in terms of the notion of invariance under interventions. Suppose that X and Y are variables that can take at least two values. The notion of an intervention attempts to capture, in non-anthropomorphic language that makes no reference to notions like human agency, the conditions that would need to be met in an ideal experimental manipulation of X performed for the purpose of determining whether X causes Y. The intuitive idea is that an intervention on X with respect to Y is a change in the value of X that changes Y, if at all, only via a route that goes through X and not in some other way. This requires, among other things, that the intervention not be correlated with other causes of Y except for those causes of Y (if any) that are causally between X and Y and that the intervention not affect Y independently of X. Thus if A is a common cause of B and S as in the example above, manipulating B by manipulating A will not count as an intervention on B with respect to S since in this case the manipulation affects S via a route (the route that connects A to S ) that does not go through B. (369-70)

Here is how he applies this idea to causal mechanisms. A mechanism consists of separate components that have intervention-invariant relations to separate sets of outcomes. These components are modular: they exercise their influence independently. And, like keys on an accordion, they can be separately activated with discrete results.

So far I have been arguing that components of mechanisms should behave in accord with regularities that are invariant under interventions and support counterfactuals about what would happen in hypothetical experiments. (374)

Here is the proposal all of this leads up to:

(MECH) a necessary condition for a representation to be an acceptable model of a mechanism is that the representation (i) describe an organized or structured set of parts or components, where (ii) the behavior of each component is described by a generalization that is invariant under interventions, and where (iii) the generalizations governing 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 under manipulation of the input to each compo- nent and changes in the components themselves. (375)

Woodward illustrates his theory of mechanisms with simple physical and biological examples. How does this theory work when we consider social mechanisms?

What seems most evident is that social mechanisms are not commonly as complex as Woodward's examples would suggest. The sorts of mechanisms that crop up in sociology seem largely to be "simple" mechanisms: they don't consist of multiple independent components leading to an outcome.

Here is the way that McAdam, Tarrow and Tilly (MTT) characterize mechanisms and processes in Dynamics of Contention:

• Mechanisms are a delimited class of events that alter relations among specified sets of elements in identical or closely similar ways over a variety of situations.
• Processes are regular sequences of such mechanisms that produce similar (generally more complex and contingent) transformations of those elements. (24)

These definitions imply that processes are compound, whereas typical mechanisms are simple.

Here are examples that MTT offer of mechanisms:

• resource depletion or enhancement affects people's capacity to engage in contentious politics (25)
• commitment is a widely recurrent individual mechanism in which persons who individually would prefer not to take the risks of collective action find themselves unable to withdraw without hurting others whose solidarity they value (26)
• Brokerage ... as the linking of two or more previously unconnected social sites by a unit that mediates their relations with one another and/or with yet other sites (26)
• Identity shift ... alteration during contentious claim making of public answers to the question: "Who are you?" (27)

In each case we seem to have a simple relationship between one social or environmental fact and a typical outcome -- not a complex concatenation of "cogs and wheels" of social interaction.

So when we consider typical examples of social mechanisms -- free-riding (Olson), escalation (McAdam-Tarrow-Tilly), identity competition (Horowitz) -- we commonly find that they are all basically one-step mechanisms. So the assumption that a mechanism consists of modular components doesn't fit the social sciences well. There are complex social processes, to be sure, but it seems best to understand these as concatenations of distinct mechanisms rather than as a single complex mechanism. (Why? Because they are all too often unrepeatable.)

This doesn't mean that we can't understand social mechanisms along the lines Woodward suggests, if we are content to acknowledge that it is hard to find complex social mechanisms. But in order for even this to be the case, we would have to confirm that these simple mechanisms produce intervention-invariant regularities.

This requirement runs up against a different problem, however. The regularities that correspond to typical social mechanisms are soft regularities, not hard-and-fast laws. Social causation is probabilistic, not deterministic. The regularities corresponding to social causes derive from features of human agency and behavior, and they are deeply exception-laden. The free-rider mechanism tends to give rise to under-investment in the public good -- except when people self-organize, semi-coercive organizations appear, or altruistic religious attitudes take hold. Social mechanisms are productive, in the sense that they "bring about" the associated outcomes. But they are not invariant across all or most cases.

This implies that there are no intervention-invariant relations to be had in the social world. And therefore we need some other analytical foundation if we are to persist in thinking there are social causal mechanisms.

Woodward addresses something very much like this possibility in conjunction with psychological mechanisms. And he draws a prescriptive conclusion: if the "mechanisms" cited in psychology do not have these characteristics of modularity and invariance, then they aren't really mechanisms:

the standard boxological diagrams allegedly describing the operation of psychological mechanisms drawn by psychologists are rarely accompanied by convincing evidence that the parts corresponding to the boxes satisfy the modularity condition described above. If the argument of this paper is correct, this is a reason for being skeptical that these diagrams describe genuine mechanisms. (377)

It seems likely enough that he would reach a similar conclusion about the kinds of mechanisms offered by MTT.

(Here is an excellent review by Michael Strevens of Woodward, Making Things Happen.)

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Posted in: CAT_mechanisms,causal mechanism

More on meso causation

A recent post considered the question, do organizations have causal powers? There I argued that they do, in a number of ways. Here I'd like to return to these claims and see how they disaggregate onto subvening circumstances, including especially patterns of individual and group activity. The italicized phrases are extracted from the earlier post.

• First, the rules and procedures of the organization may themselves have behavioral consequences that lead consistently to a certain kind of outcome.

How do rules and procedures causally affect the behavior of the actors who participate in them? (a) Through training and inculcation. The new participant is exposed to training processes designed to lead him/her to internalize the procedures and norms governing his/her function. (b) Through formal enforcement. Supervisors are institutionally charged to enforce the rules through direct observation and feedback. (c) Through the normative example of other participants, including informal sanctions by non-supervisors for "wrong" behavior. (d) Through positive incentives administered by supervisors and mid-level functionaries. Each of these avenues for influencing the behavior of an actor within an organization depends on the actions and motivations of other actors within the organization. So we have the recursive question, what factors influence the behavior of those actors? And the answer seems to be: all actors find themselves within a dynamic system of behavior by other actors, frequently maintaining an equilibrium of reproduction of the rules and roles.

• Second, different organizational forms may be more or less efficient at performing their tasks, leading to consequences for the people and higher-level organizations that are depending on them.

Institutions designed to do similar work may differ in their functioning because of specific differences in the implementation of roles and processes within the organization. This is a system characteristic of the particular features and interactions of the rules and processes of the organization, along with the expected behaviors of the participants. It is also a causal characteristic: implementing system A results in greater efficiency at X than implementing B. The underlying causal reality that needs explanation is how it comes to pass that participants carry out their roles as prescribed--which takes us back to the first thesis.

• Third, the discrepancy between what the rules require of participants and what the participants actually do may have consequences for the outputs of the organization.

This causal claim highlights the difference between formal and informal procedures and practices within an organization. Informal practices can be highly regular and reproducible. In order to incorporate their implications into our analysis of the workings of the organization we need to accurately understand them; so we need to do some organizational ethnography to identify the practices of the organization. But in principle, the logic of explanation we provide on the basis of informal practices is exactly the same as those offered on the basis of the formal rules of the organization.

• Fourth, the specific ways in which incentives, sanctions, and supervision are implemented differentiate across organizations.

This is one of the key insights of the "new institutionalism." The specific design of the institution in terms of opportunities and incentives presented to participants makes a large difference in actors' behavior, and consequently a large difference to the system-level performance of the institution. Tweaking the variable of the level in the organization's hierarchy that needs to sign off on expenditures at a given level has significant effects on behavior and system properties. On the one hand, higher-level sign-off may serve to restrain spending. On the other hand, it may make the organization more unwieldy in responding to opportunities and threats.

• Fifth, the organization has causal powers with respect to the behavior of the individuals involved in the organization.

This factor parallels thesis 1 but is meant to refer to longterm effects on behavior and personality. The idea here is that immersion in a particular organization and its culture creates a distinctive social psychology in the people who experience it. They may acquire habits of thought, ways of responding to new circumstances, higher or lower levels of trust of others, and so forth, in ways that influence their behavior in the broader society. The idea of an "organization man" falls in this category of influence. The organization influences the individual's behavior, not just through the immediate system of rewards and punishments, but through its ability to shape his/her more permanent social psychology.

There are only two fundamental causal paths identified here. The causal properties of the organization are embodied in the patterns of coordinated actions undertaken by the actors who are involved; and these orderly patterns create system effects for the organization as a whole that can be analyzed in abstraction from the individuals whose actions constitute the micro-level of the social entity.

The most obvious causal property of an organization is bound up in the function of the organization. An organization is developed in order to bring about certain social effects: reduce pollution or crime, distribute goods throughout a population, provide services to individuals, seize and hold territory, disseminate information. These effects occur as a result of the coordinated activities of people within the organization. When organizations work correctly they bring about one set of effects; when they break down they bring about another set of effects. Here we can think about organizations in analogy with technology components like amplifiers, thermostats, stabilizers, or surge protectors. This analogy suggests we think about the causal powers of an organization at two levels: what they do (their meso-level effects) and how they do it (their micro-level sub-mechanisms).

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Posted in: CAT_institutions,causal mechanism,methodological individualism,microfoundations,organization

Aggregation dynamics of conditional psychological dispositions

We can use computational modeling techniques to aggregate individual behavior into collective patterns. A simple version of this is Thomas Schelling's segregation model (Micromotives and Macrobehavior).

Most commonly these tools have been used to model the results of rational choices by the actors involved, often using the tools of game theory. But the approach is more general; any common behavioral feature at the individual level can be aggregated through similar modeling techniques as well if we can specify our assumptions about conditionality plausibly.

Here is a hypothetical example illustrating the feasibility of modeling non-rational social dynamics. Suppose individuals have a social behavior disposition that is variable depending on the behaviors of other individuals in their acquaintance spaces. Examples might include: cooperative behavior, racist behavior and intolerance, abuse against women, or philanthropy. Assume individuals exist in an extended social graph of "acquaintance", so each has a specific list of immediate acquaintances (for example along the lines of the Framingham Heart Study graph below). And assume a contagion rate: the probability of switching when one acquaintance switches is p, the probability of switching when two acquaintances switch is p', and so forth.


Now we are in a position to do some interesting modeling based on recursive calculation of each individual's state based on the states of individuals within his/her acquaintance space. (This is analogous to Schelling's segregation model.) Calculate each individual's state based on the states of his/her acquaintances in the previous iteration. And run this recalculation through the whole population as many iterations as you like. The series of full iterations will represent moments in time as this dynamic system moves to a new equilibrium. Each represents a frame in an animation of the spread of intolerance through the population. (It should be possible to embody this simulation in a spreadsheet. Models like these are sometimes referred to as cellular automata.)

Now we can do a number of interesting things. We can observe the spread of racist attitudes and behavior. We can introduce disturbances in various parts of the graph and observe the transmission process. We may be able to document path dependency: perhaps it matters where the disturbance occurs.

After performing a large number of iterations, four large possibilities exist: everyone intolerant, everyone tolerant, stable neighborhoods within the graph of tolerance and intolerance, and no equilibrium at all.

(Actually, based on the assumptions outlined so far, it is inevitable that the graph will eventually go 100% "infected" with intolerance, since there is no recovery mechanism at the individual level. So we would probably want to incorporate some influence that turns individuals from intolerant to tolerant once in a while. We could also introduce more complexity into the model by postulating multiple states for the actor -- perhaps high, moderate, low intolerance. Individuals moving up or down the scale could infect their neighbors in the same direction. And we might attribute different infection rates to different individuals, to see how this affects the outcome.)

This example also creates the possibility of strategic intervention by outsiders: knowing how these dynamics work affords both the state and activist organizations to undertake actions designed to alter the outcome by strategically "seeding" the graph with intolerant individuals. Racist anti-immigrant organizations in western Europe appear to be doing exactly this at present.

If actors are wired this way (i.e. their social dispositions are a function of those of the individuals in their acquaintance space), then racism, philanthropy, and violence against women will behave like a communicable disease and the tools of social epidemiology will be applicable. And the consequences are great: some societies will have a stable anti-racist population and others the opposite, depending on contingent events, the nature of the network, and deliberate actions and policies.

This example is framed in terms of behavioral dispositions of social psychology. But it is equally pertinent to any individual characteristic that is variable in response to social contacts: slang, manners, social perception, ... Any psychological, cognitive, or emotional state with behavioral consequences that is responsive to context in this way is amenable to the same kind of modeling.

This is an example of social aggregation dynamics that is not grounded in strategic rationality but rather in features of conditionalized social psychology. The example is fully compatible with the requirement that macro-outcomes need to be explained on the basis of mechanisms with microfoundations. And it does not depend on the assumptions of rational actor theory or game theory.

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Posted in: analytical sociology,CAT_aggregation,causal mechanism

Decision-making in complex systems

source: The Financial Ninja (link)

How should we make intelligent decisions in contexts in which the object of choice involves the actions of other agents whose choices jointly determine the outcome and where the outcome is unpredictable?  Robert Axelrod and Michael Cohen address these issues in Harnessing Complexity: Organizational Implications of a Scientific Frontier.  They define a complex adaptive system in something like these terms: a body of causal processes and agents whose interactions lead to outcomes that are unpredictable. So the interactions among agents often have unpredictable consequences; and the agents themselves adapt their behavior based on past experiences: "They interact in intricate ways that continually reshape their collective future."  Here is how Axelrod and Cohen put their question:

In a world where many players are all adapting to each other and where the emerging future is extremely hard to predict, what actions should you take? (xi)

This book is about designing organizations and strategies in complex settings, where the full consequences of actions may be hard -- even impossible -- to predict. (2)

Complexity and chaos are often used interchangeably; but Axelrod and Cohen distinguish sharply between them in these terms:

Chaos deals with situations such as turbulence that rapidly become highly disordered and unmanageable.  On the other hand, complexity deals with systems composed of many interacting agents.  While complex systems may be hard to predict, they may also have a good deal of structure and permit improvement by thoughtful intervention. (xv)

Here is a simple current example -- an assembly of 1000 Egyptian citizens in January 2011, interested in figuring out what to do in light of their longstanding grievances and the example of Tunisia. Will the group erupt into defiant demonstration or dissolve into private strategies of self-preservation?  The dynamics of the situation are fundamentally undetermined; the outcome depends on things like who speaks first, how later speakers are influenced by earlier speakers, whether the PA system is working adequately, which positions happen to have a critical mass of supporters, the degree to which the government can make credible threats of retaliation, the presence of experienced organizers, and a dozen other factors.  So we cannot predict whether this group will move towards resistance or accommodation, even when we assume that all present have serious grievances against the Egyptian state.  

The fact of path dependence comes into this understanding of complexity, in that the order of actions by the agents can influence the outcome.  So we could run the Egypt scenario forward multiple times and arrive at different outcomes repeatedly.  We might imagine a tool along the lines of a Monte Carlo simulation that models the range of possible outcomes; and in the sorts of systems Axelrod and Cohen are interested in, the range of outcomes is very wide with no "modal" and most probable outcomes at the core.

The difficulty of prediction in the future development of a complex system derives in part from the adaptiveness of the agents who make it up; but it also derives from the fact of non-linearity of causation in complex systems.  Small influences can have large effects; there is often a discontinuity between the magnitude and direction of a cause and its effect.

What makes prediction especially difficult in these settings is that the forces shaping the future do not add up in a simple, systemwide manner.  Instead, their effects include nonlinear interactions among the components of the system.  The conjunction of a few small events can produce a big effect if their impacts multiply rather than add. (14)

Decision theorists distinguish between situations of parametric rationality and strategic rationality.  In the former the decision maker is playing against nature, with a fixed set of probabilities and causal properties; in the latter the decision maker is playing against and with other rational agents, and the outcome for each depends upon the choices made by all. Game theory offers a mathematical framework for analyzing strategic rationality, while expected utility theory is advanced as a framework for analyzing the problem of choice under risk and uncertainty.  The fundamental finding of game theory is that there are equilibria for multi-person games, both zero-sum and non-zero-sum, for any game that can be formulated in the canonical game matrix of agents' strategies and joint outcomes.  Whether those equilibria are discoverable for ordinary strategic reasoners is a separate question, so the behavioral relevance of the availability of an equilibrium set of strategies is limited.  And here is the key point: neither parametric rationality nor equilibrium-based strategic rationality helps much in the problem of decision-making within a complex adaptive system.

The situation that Axelrod and Cohen describe here is an instance of strategic rationality, but it doesn't yield to the framework of mathematical game theory.  This is because we can't attach payoffs to combinations of strategies for the separate agents; this follows from the unpredictability assumption built into the idea of complexity.  And, second, complex adaptive systems are usually in a dynamic process of change, so that the system never attains an equilibrium state.

Axelrod and Cohen are hoping to provide counsel for how decision makers can "harness" complexity -- that is, how they can design policies and strategies that perhaps push a complex situation in a favorable direction, or that insulate an organization from the worst outcomes that the complex system may produce.

Harnessing complexity ... means deliberately changing the structure of a system in order to increase some measure of performance, and to do so by exploiting an understanding that the system itself is complex. (9)

Axelrod and Cohen make use of three high-level concepts to describe the development of complex adaptive systems: variation, interaction, and selection.  Variation is critical here, as it is in evolutionary biology, because it provides a source of potentially successful innovation -- in strategies, in organizations, in rules of action.  The idea of adaptation is central to their analysis -- in this case, adaptation and modification of strategies by agents in light of current and past success.  Interaction occurs when agents and organizations intersect in the application of their strategies -- often producing unforeseen consequences.  (The strategy of open-source software development is one example that they look at, and the interactions that occurred as open-source innovations encountered closed-source innovations.)  An organization or a population is best served, they argue, when there is a regular source of innovations (variations); when these innovations are implemented in the form of variant strategies; and when it is possible to cultivate more successful variations and to damp out less successful (selection).  Here is how they summarize their view:

Agents, of a variety of types, use their strategies, in patterned interaction, with each other and with artifacts.  Performance measures on the resulting events drive the selection of agents and/or strategies through processes of error-prone copying and recombination, thus changing the frequencies of the types within the system.

And they arrive at eight rules of thumb for "harnessing complexity" when it comes to organizations and social policies:

• Arrange organizational routines to generate a good balance between exploration and exploitation.
• Link processes that generate extreme variation to processes that select with few mistakes in the attribution of credit.
• Build networks of reciprocal interaction that foster trust and cooperation. 
• Assess strategies in light of how their consequences can spread.
• Promote effective neighborhoods.
• Do not sow large failures when reaping small efficiencies.
• Use social activity to support the growth and spread of valued criteria.
• Look for shorter-term, finer-grained measures of success that can usefully stand in for longer-run, broader aims. (156-158)

So how should we understand these heuristics as a conclusion to this analysis?  They function as an "operating manual" for leaders and policy makers attempting to bring about good effects within a population of agents demonstrating adaptive complexity.  And perhaps these are plausible meta-strategies for intervening within a complex social system.

What is worrisome, though, is the implicit functionalism that seems to underlie the book: the idea that agents of good will and having the longterm best interests of the population in mind are making the rules.  But what happened to the predators -- the organized crime figures, the drug lords, the conspirators, the predatorial businesses, the anti-democrats?  Won't they too be looking to exploit (harness) the workings of complexity?  Axelrod's earlier work on repeated prisoners' dilemmas explicitly took into account the availability of strategies designed to exploit the cooperators; and his work on cooperation emphatically makes the point that cooperation is often deployed for anti-social and predatory purposes (cartels, extortion rackets, ...)  (The Evolution of Cooperation: Revised Edition).  Shouldn't this counter-social agency be incorporated into this analysis of complex adaptive systems as well?  As Charles Tilly points out, crime and piracy also depend upon "trust networks" and innovative forms of predation (Trust and Rule).

During the 1980s the Reagan administration wanted to create a "Star Wars" anti-missile shield, and some of their policy makers argued that we could solve the technical challenges because the U.S. had succeeded in putting a man on the moon.  But critics of this military space strategy rejoined, "But the moon didn't fight back;" whereas Soviet scientists and engineers were fully capable of adapting their ICBM technologies to evade the defensive characteristics of a missile shield.  There seems to be something of the same blind spot in this analysis of social complexity; predation and the common good are in competition with each other, and neither has a decisive advantage.

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Posted in: CAT_agency,causal mechanism,complexity

Mechanisms of contention reconsidered

Social contention theorists Doug McAdam, Sidney Tarrow, and Charles Tilly created a great deal of interest in the "mechanisms" approach to social explanation with the publication of their Dynamics of Contention in 2001.  The book advocated for several important new angles of approach to the problem of analyzing and explaining social contention: to disaggregate the object of analysis from macro-events like "civil war," "revolution," "rebellion," or "ethnic violence" into the component social processes that recur in various instances of social contention; and to analyze these components as "causal mechanisms."  Here is how they define contentious politics:

By contentious politics we mean: episodic, public, collective interaction among makers of claims and their objects when (a) at least one government is a claimant, an object of claims, or a party to the claims and (b) the claims would, if realized, affect the interests of at least one of the claimants.  Roughly translated, the definition refers to collective political struggle. (5)

Here is the way they characterize the distinctive nature of the analysis offered in their new work:

This book identifies similarities and differences, pathways and trajectories across a wide range of contentious politics -- not only revolutions, but also strike waves, wars, social movements, ethnic mobilizations, democratization, and nationalism. (9)

And here is how they want to make systematic, explanatory sense of the heterogeneous examples of social contention that the world presents: to identify and investigate some common social mechanisms that work in roughly similar ways across numerous different instances of social contention.

Social processes, in our view, consist of sequences and combinations of causal mechanisms.  To explain contentious politics is to identify its recurrent causal mechanisms, the ways they combine, in what sequences they recur, and why different combinations and sequences, starting from different initial conditions, produce varying effects on the large scale....  Instead of seeking to identify necessary and sufficient conditions for mobilization, action, or certain trajectories, we search out recurrent causal mechanisms and regularities in their concatenation. (13)

They offer these definitions of the key analytical terms:

Mechanisms are a delimited class of events that alter relations among specified sets of elements in identical or closely similar ways over a variety of situations.

Processes are regular sequences of such mechanisms that produce similar (generally more complex and contingent) transformations of those elements.

Episodes are continuous streams of contention including collective claims making that bears on other parties' interests. (24)

They distinguish among environmental mechanisms ("externally generated influences on conditions affecting social life"), cognitive mechanisms ("operate through alterations individual and collective perception"), and relational mechanisms ("alter connections among people, groups, and interpersonal networks") (25-26).  And they offer a few examples of mechanisms: mobilization mechanisms, political identity formation mechanisms, and aggregation mechanisms.

The approach can be summarized in these terms:

Seen as wholes, the French Revolution, the American civil rights movement, and Italian contention look quite different from each other. ... Yet when we take apart the three histories, we find a number of common mechanisms that moved the conflicts along and transformed them: creation of new actors and identities through the very process of contention; brokerage by activists who connected previously insulated local clumps of aggrieved people; competition among contenders that led to factional divisions and re-alignments, and much more.  These mechanisms concatenated into more complex processes such as radicalization and polarization of conflict; formation of new balances of power; and re-alignments of the polity along new lines. (32-33)

This is roughly the conception of social ontology and explanation that was put forward in 2001, and it was a powerful challenge to a more positivistic methodology that insisted on looking for general laws of contention and uniform regularities governing things like revolutions and civil wars.

By 2007, however, Tarrow and Tilly found it necessary to reformulate their views to some degree; and this re-thinking resulted in Contentious Politics.  So what changed between the theory offered in 2001 and that restated in 2007?  The answer is, surprisingly little at the level of concept and method.

Tilly and Tarrow refer to three main lines of criticism of Dynamics of Contention to which they felt a need to respond:

Although that book stirred up a lively scholarly discussion, even specialists who were sympathetic to our approach made three justified complaints about it.  First, it pointed to mechanisms and processes by the dozen without defining and documenting them carefully, much less showing exactly how they worked.  Second, it remained unclear about the methods and evidence students and scholars could use to check out its explanations.  Third, instead of making a straightforward presentation of its teachings, it reveled in complications, asides, and illustrations. (xi)

What did not change between the two formulations was the conceptual foundation.  The key concepts of contentious politics, mechanisms, processes, and episodes are essentially the same in the 2007 book as in 2001.

Contentious politics involves interactions in which actors make claims bearing on someone else's interests, leading to coordinated efforts on behalf of shared interests or programs, in which governments are involved as targets, initiators of claims, or third parties.  Contentious politics thus brings together three familiar features of social life: contention, collective action, and politics. (4)

Further, they analyze contention in the same basic terms in 2007 as in 2001:

For explanation, we need additional concepts.  This chapter supplies four of them: the events and episodes of streams of contention and the mechanisms and processes that constitute them.

And their definitions of mechanisms and processes are unchanged:

By mechanisms, we mean a delimited class of events that alter relations among specified sets of elements in identical or closely similar ways over a variety of situations.  Mechanisms compound into processes.  By processes, we mean regular combinations and sequences of mechanisms that produce similar (generally more complex and contingent) transformations of those elements. (29)

One goal of the 2007 book is to simplify the discussion of mechanisms.  The authors highlight three mechanisms as being particularly central to episodes of contention:

• Brokerage: production of a new connection between previously unconnected sites
• Diffusion: spread of a form of contention, an issue, or a way of framing it from one site to another
• Coordinated action: two or more actors' engagement in mutual signaling and parallel making of claims on the same object (31)

Other mechanisms that are discussed include social appropriation, boundary activation, certification, and identity shift (34).  And their key examples of processes are mobilization and de-mobilization -- each of which consists of a series of component mechanisms.

One difference between the two versions of the theory is more substantive.  In 2007 Tarrow and Tilly give greater priority to the performative nature of contentious politics: contentious performances and repertoires have greater prominence in the story offered in 2007 than in the analysis of episodes provided in 2001.  This is not a new element, since Tilly himself made extensive use of the ideas of performance and repertoire in his earlier analyses of French contentious politics; but the theme is given more prominence in 2007 than it was in 2001.

Overall, it seems reasonable to say that Contentious Politics expresses the same conceptual framework for researching and understanding contention as that found in Dynamics of Contention.  There is no fundamental break between the two works.  What has changed is more a matter of pedagogy and presentation.  The authors have sought to provide a more coherent and orderly presentation of the conceptual framework that they are presenting; and they have sought to provide an orderly and systematic analysis of the cases, in order to identify the mechanisms that recur across episodes.

Where additional work is still needed is at the level of conceptualization of causal mechanisms.  There is now a large body of discussion and debate about how to think about social causal mechanisms, and many observers are persuaded that the move to mechanisms is a very good way of getting a better grip on social explanation and analysis.  But how to define a social mechanism is still obscure.  The definition that MTT offer does not really seem satisfactory -- "a delimited class of events that alter relations among specified sets of elements in identical or closely similar ways over a variety of situations."  A mechanism is not an event (or a class of events); rather, it is a nexus between a cause and an effect; it is the pathway through which the cause brings about the effect.  It is a materially embodied set of causal powers and their effects.  But the specific formulation provided by MTT doesn't succeed in capturing any of these root ideas.

Various philosophers have attempted to specify more clearly the notion of a causal mechanism (Jon Elster, Explaining Social Behavior: More Nuts and Bolts for the Social Sciences; Hedstrom and Swedberg, Social Mechanisms: An Analytical Approach to Social Theory, my own Varieties Of Social Explanation: An Introduction To The Philosophy Of Social Science). We can give good examples of what we mean by a causal mechanism.  But to date, it seems that we have not yet been able to come up with a fully satisfactory definition of a causal mechanism.  (Here is a short conference paper by Tilly in 2007 that takes a different approach by linking the concept to Robert Merton's work; link.)

So the disaggregative approach that MTT advocate is a crucially important breakthrough in the study of complex social phenomena, and it seems convincing that it is "mechanisms" that disaggregation should lay bare.  Moreover, the idea of mechanisms aggregating to processes and constituting episodes is an intuitively compelling notion of how complex social phenomena are constituted.  These are genuinely important new ways of conceptualizing the complex social reality of contention and the task of providing descriptions and explanations of complex social episodes.  Contentious Politics is a very good presentation of these fundamental ideas.  What we don't yet have, however, is a fully convincing and fertile conception of the root idea, the notion of a causal mechanism.

(See other postings under the thread of causal mechanism for other discussions of the topic.)

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Posted in: CAT_mechanisms,causal mechanism,concepts,microfoundations,social movements

What do we want from sociology?

Let's say we've absorbed the anti-positivism argued many times here -- sociology should not be modeled on the natural sciences, we shouldn't expect social phenomena to have the homogeneity and consistency characteristic of natural phenomena, and we shouldn't expect to find social laws.  What remains for the intellectual task of post-positivist sociology?  What do we want from sociology?

Here are a handful of topics that are both important and feasible.

• description and theory of social movements / collective action / popular politics
• comparative study of large historical social-political formations such as fascism, colonialism, fiscal systems
• descriptive analysis of social inequalities (race, gender, class, ethnicity) and their mechanisms
• descriptive and theoretical accounts of major social institutions (corporations, unions, universities, governments, religions, families) and how they work (mechanisms)
• Concrete studies of identity formation
  

So there is plenty for a post-positivist sociology to do. But more specifically, what can the science of sociology offer us? To start, we would like to understand some of the myriad social processes that surround us. We would like to understand how social stratification works; how economic power is translated into political power; why racial disadvantage persists from one generation to another; and what leads people to behave as they do in specific social settings. To put a name on this, we would like to have convincing theories of social mechanisms and processes, and some idea of how these aggregate into larger social processes.

Second, to whatever degree possible, we would like to have theories of social behavior that will permit us to intervene to prevent undesirable outcomes. We would like to greatly reduce the rate of teen violence in cities like Detroit and Chicago. And this requires theories of the factors that lead to the behavior so we can have some hope of designing solutions. So we would like for sociology to provide a degree of theoretical support for the design of helpful social policies.

Third, we would like for sociology to be an empirical discipline. And thus means that we want to "test" or otherwise empirically evaluate the hypotheses and theories produced by sociologists.

All three of these goals seem to point in the direction of a sociology of the middle range (as Robert Merton put it) -- theories that attempt to capture mid-range social processes such as racial discrimination in housing, power brokerage, or identity formation. The value of this level of focus is parallel to the three points just made. Mid-level analysis is suitable to investigation and discovery of social mechanisms. Mechanisms and processes at this level are likely to be most useful when it comes to designing policies and social interventions. And, finally, this level of sociological theory is most likely to admit of empirical investigation and validation through piecemeal inquiry.

What this suggests to me is that piecemeal inquiry into specific social phenomena is a more promising approach than grand unifying sociological theories. And this in turn suggests the metaphor of toolbox rather than orrery -- a collection of explanatory hypotheses rather than a unifying theoretical system.

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Posted in: CAT_epistemology,causal mechanism,theory

Scientific realism for the social sciences

What is involved in taking a realist approach to social science knowledge? Most generally, realism involves the view that at least some of the assertions of a field of knowledge make true statements about the properties of unobservable things, processes, and states in the domain of study.  Several important philosophers of science have taken up this issue in the past three decades, including Rom Harre (Causal Powers: Theory of Natural Necessity) and Roy Bhaskar (A Realist Theory of Science).  Peter Manicas's recent book, A Realist Philosophy of Social Science: Explanation and Understanding, is a useful step forward within this tradition. Here is how he formulates the perspective of scientific realism:

The real goal of science ... is understanding of the processes of nature. Once these are understood, all sorts of phenomena can be made intelligible, comprehensible, unsurprising. (14)

Explanation ... requires that there is a "real connection," a generative nexus that produced or brought about the event (or pattern) to be explained. (20)

So realism has to do with discovering underlying processes that give rise to observable phenomena. And causal mechanisms are precisely the sorts of underlying processes that are at issue.  Here is how Manicas summarizes his position:

Theory provides representations of the generative mechanisms,including hypotheses regarding ontology, for example, that there are atoms, and hypotheses regarding causal processes, for example, that atoms form molecules in accordance with principles of binding. We noted also that a regression to more fundamental elements and processes also became possible. So quantum theory offers generative mechanisms of processes in molecular chemistry. Typically, for any process, there will be at least one mechanism operating, although for such complex processes as organic growth there will be many mechanisms at work. Theories that represent generative mechanisms give us understanding. We make exactly this move as regards understanding in the social sciences, except that, of course, the mechanisms are social. (75)

Manicas's illustrations of causal powers and mechanisms are most often drawn from the natural world. But what basis do we have for thinking that social entities have stable causal properties -- let alone a profile of causal powers that are roughly invariant across instances?

Consider an example, Theda Skocpol's definition of social revolutions:

Social revolutions are rapid, basic transformations of socio-economic and political institutions, and--as Lenin so vividly reminds us--social revolutions are accompanied and in part effectuated through class upheavals from below. It is this combination of thorough-going structural transformation and massive class upheavals that sets social revolutions apart from coups, rebellions, and even political revolutions and national independence movements. (link)

Realism invites us to consider whether "social revolutions" really have the characteristics she attributes to them.  Do social revolutions have an underlying nature distinctive causal powers that might be identified by a social theory?  More generally, what basis do we have for thinking that certain types of social entities possess a specific set of causal powers?

The answer seems to be, very little.  Types of social entities -- revolutions, states, riots, market economies, fascist movements -- are heterogeneous groupings of concrete social formations rather than "kinds" along the lines of "metal" or "gene".  Each of the extended historical events that Skocpol offers as instances of the category "social revolution" is unique and contingent in a variety of ways; these historical episodes do not share a common causal nature.  It is legitimate to group them together under the term "social revolution"; but it is essential that we not commit the error of reification and imagine that the group so constituted must share a fundamental causal nature in common.  So the most direct application of this kind of realism to the social sciences seems somewhat unpromising.

But we are on firmer ground when we consider a particularly central type of assertion in the social sciences: claims about underlying causal mechanisms or social processes.  So what does it mean to assert that a given social mechanism "really exists"? 

Take the idea of "stereotype threat" as one of the mechanisms underlying an important social fact, the racial and gender differences in performance that have been observed on some standardized tests (Claude Steele and Joshua Aronson, "Stereotype Threat and the Intellectual Test Performance of African Americans" (link); see also this article in the Atlantic).  We can summarize the theory along these lines: "Prevalent assumptions about the characteristics and performance of various salient social groups can depress (or enhance) the performance of members of those groups on intellectual and physical tasks.  This provides a partial explanation of the observed differentials in performance."  This mechanism is hypothesized as one of the ways in which performance by individuals in various groups is socially influenced in such a way as to lead to differential performance across groups.  It postulates a set of internal psychological mechanisms surrounding cognition and problem-solving, all related to the individual's self-ascribed social identity.

The realism question is this: do these hypothetical psychological effects actually occur in real human individuals?  And do these differences in cognitive processes lead to differential performance across groups?  If we confirm both these points, then we can conclude that "stereotype threat is a real social psychological mechanism."  The microfoundations of this mechanism reside in two locations: the concrete cognitive processes of the individuals, and the social behaviors of persons around these individuals, giving subtle cues about stereotypes that are discerned by the test-taker.

So we might say that we can conclude that a postulated social mechanism "really" exists if we are able to provide piecemeal empirical and theoretical arguments demonstrating that the terms of the mechanism hypothesis are confirmed in the actions and behavior of agents; and that these patterns of action do in fact typically lead to the sorts of outcomes postulated.  In other words, we need to look at our hypotheses about social mechanisms as small, somewhat separable theories that need separate empirical, historical, and theoretical evaluation.  And when we are successful in providing convincing support for these mechanism-theories, we are also justified in concluding that the postulated mechanism really exists.  The social world really embodies stereotype threat if individuals are really affected in their cognitive performances by the sorts of subtle behavioral cues mentioned by the theory, in roughly the ways stipulated by the theory.  And we will feel most confident in this assertion if we also find new areas of behavior where this mechanism also appears to be at work.

This approach has an important implication about social ontology.  The reality of a social mechanism is dependent on facts about agents, their characteristics of agency, and the environment of social relationships within which they act.  So there is a close intellectual relationship between the ontology of methodological localism and realism about causal mechanisms.

(The smokestack image above illustrates a different kind of social mechanism -- the workings of externalities in a market economy, creating pollution by dumping public harms to save private costs.)

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Posted in: CAT_epistemology,causal mechanism,localism,mechanism,philosophy of science

Variation as a social fundamental

Over 700 historians, sociologists, demographers, and political scientists enjoyed a splendid program of panels at the Social Science History Association in Long Beach this week (link). There were panels on recent historical demography, comparative historical analysis, and social mobilization research, as well as a pair of great panels on the work of Charles Tilly. There was even a smattering of papers suggesting possible opportunities for innovation in theory and research methods in historical sociology.  (A book panel on Neil Smelser's recent The Odyssey Experience: Physical, Social, Psychological, and Spiritual Journeys illustrates this point: the book is highly original and demonstrates the value of seeking out new perspectives and angles of view on social behavior and social change.)

Here is one strong impression that emerges from the program.  Variation within a social or historical phenomenon seems to be all but ubiquitous. Think of the Cultural Revolution in China, demographic transition in early modern Europe, the ideology of a market society, or the experience of being black in America. We have the noun -- "Cultural Revolution" -- which can be explained or defined in a sentence or two as an extended social phenomenon of mobilization and conflict that took place in China from 1966-76; and we have the complex underlying social realities to which it refers, spread out over many cities, villages, and communes across China (The Chinese Cultural Revolution as History).  Or consider another general noun, "demographic transition," defined as a period in which a population experiences abrupt decline in mortality, followed by a decline in fertility.  Using a variety of statistical methods, historical demographers can document the occurrence of a demographic transition in different periods in Sweden, Italy, Britain, and China.  And it turns out that there are both common features and distinguishing characteristics that emerge from detailed study -- differences in timing, differences in social composition, differences in the mechanisms bringing these changes about.

In each case there is a very concrete and visible degree of variation in the factor over time and place. Historical and social research in a wide variety of fields confirms the non-homogeneity of social phenomena and the profound location-specific variations that occur in the characteristics of virtually all large social phenomena. Social nouns do not generally designate uniform social realities (post).  These facts of local and regional variation provide an immediate rationale for case studies and comparative research, selecting different venues of the phenomenon and identifying specific features of the phenomenon in this location. Through a range of case studies it is possible for the research community to map out both common features and distinguishing features of a given social process.

This description focuses on locational variation in processes -- village to village, country to country. But social scientists often also highlight variations across social segments within a given location: class, race, gender, religion, occupation.  Do sharecroppers have a different fertility profile over time than the wealthy in a particular region at a particular time?  Are there significant differences in survival strategies for distinct groups defined by race or ethnicity in a city or a group of cities?

This situation of variation and case-specific research raises a number of challenging questions. One is the question of whether the phenomenon designated by the noun is one integrated social reality, with varied expressions across locations, or whether instead the different locations are simply loosely similar but independent occurrences. Simon Schama's radical question -- was there a French Revolution, or were there simply a congeries of periods and locations of disturbance? -- illustrates this question (post), as does a previous discussion of the revolutions of 1848 (post).

A second major question is the challenge of discovering causal and social mechanisms connecting the various social locations encompassed by the phenomenon. How did the activism and ideology of Cultural Revolution spread from Beijing to Nanjing and other locations? How did activism spread from city to rural locations? How did local circumstances cause changes and variations in the political movement? How much path dependency existed in the spread of revolutionary ideas and strategies?

There is a more epistemic set of questions as well, concerning generalizability. Fundamentally, if there is substantial variation across locations and instances of a given phenomenon, then to what degree can we say anything about the phenomenon as a whole? And what does the study of one location allow us to say about the larger processes? Does study of the Tsinghua student Red Guard movement tell us anything about Red Guard mobilization in other places? Or is it simply one of many different and contingent develoments of contentious politics during the period?  Can we generalize from case studies and comparative research?

We can also look at the problem from the other end of the telescope: are there any social phenomena that occur fairly homogeneously across all places where this phenomenon occurs?  Candidates might include:

• Anti-Semitic violence across 19th-century Ukraine villages
• Marriage / fertility practices across rural Sweden 1700-1800
• Peasant revolts in medieval Germany
• Process of protoindustrialization in villages and towns in Low Countries 1300-1600 (Industrialization Before Industrialization)
  

For examples like these we can ask a symmetrical set of questions to those posed above. What factors explain the uniformity of results for these processes across separate locations? Various explanations are possible:

• There is a common set of conditions across the regions (e.g. famine or drought)
• There are common causes that mobilize people in many separate places (tax protests, land confiscations)
  
• There are common political traditions
• There is substantial inter-location communication and influence
• There are no large institutional or circumstantial variations that would drive significant variations in outcomes across locations

This is where the appeal to social mechanisms seems once more to be highly relevant and helpful.  If we work on the assumption that any large social process -- the dispersed locations of contention associated with the French Revolution, say -- is the compound result of a set of underlying causal social mechanisms, and if we hypothesize that many of these mechanisms are in play in some places but not in others; then we can explain both similarity and difference in the occurrence of the phenomenon across time and place.  Now the work of historical investigation can be put in these terms: identify some of the social mechanisms that evidently recur in various locations; identify some of the mechanisms that lead to significantly different results in some places; and identify some of the cross-location mechanisms that are at work to secure a degree of synchrony and parallel in the developments observed in different locations (communication systems, networks of leaders, dissemination of activists).  Case studies and comparative research permit both a degree of generalization and an explanation of variation.

In other words, the intellectual strategy here is to disaggregate the large social factor into the results of a larger number of underlying mechanisms; and then to attempt to discover how these mechanisms played out differently in different settings throughout the range of the French Revolution, protoindustrialization, or ethnic conflict in South Asia.  Significantly, this is exactly the strategy of research and explanation that Charles Tilly was led to in his emphasis on discovering the component social mechanisms that underlie social contention (McAdam, Tarrow, Tilly, Dynamics of Contention).

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Posted in: CAT_ontology,causal mechanism,comparative method,heterogeneity