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.

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.

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.

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.)

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.)

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.)

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).

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).

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).

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.

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.

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.