Causal mechanisms

The central tenet of causal realism is a thesis about causal mechanisms or causal powers. We can only assert that there is a causal relationship between X and Y if we can offer a credible hypothesis of the sort of underlying mechanism that might connect X to the occurrence of Y. The sociologist Mats Ekström puts the view this way: “the essence of causal analysis is ... the elucidation of the processes that generate the objects, events, and actions we seek to explain” (Ekstrom 1992, p. 115). Authors who have urged the centrality of causal mechanisms for both explanatory and purposes include Nancy Cartwright (Nature's Capacities and Their Measurements), Jon Elster (Explaining Social Behavior: More Nuts and Bolts for the Social Sciences), Rom Harré (Causal Powers), and Wesley Salmon (Scientific Explanation and the Causal Structure of the World). (Hedstrom and Swedberg's collection, Social Mechanisms: An Analytical Approach to Social Theory, is a useful source. An important advocate for a realist interpretation of science is Roy Bhaskar's A Realist Theory of Science.)

Nancy Cartwright is one of the most original voices within contemporary philosophy of science. Cartwright places real causal mechanisms at the center of her account of scientific knowledge. As she and John Dupré put the point, “things and events have causal capacities: in virtue of the properties they possess, they have the power to bring about other events or states” (Dupré and Cartwright 1988). Cartwright argues, for the natural sciences, that the concept of a real causal connection among a set of events is more fundamental than the concept of a law of nature. And most fundamentally, she argues that identifying causal relations requires substantive theories of the causal powers (capacities, in her language) that govern the entities in question. Causal relations cannot be directly inferred from facts about association among variables. As she puts the point, “No reduction of generic causation to regularities is possible” (Nature's Capacities and Their Measurements, p. 90). The importance of this idea for sociological research is profound; it confirms the notion shared by many researchers that attribution of social causation depends inherently on the formulation of good, middle-level theories about the real causal properties of various social forces and entities.

What is a causal mechanism? Consider this formulation: a causal mechanism is a sequence of events, conditions, and processes leading from the explanans to the explanandum (Varieties Of Social Explanation, p. 15). A causal relation exists between X and Y if and only if there is a set of causal mechanisms that connect X to Y. This is an ontological premise, asserting that causal mechanisms are real and are the legitimate object of scientific investigation.

Aage Sørensen summarizes a causal realist position for sociology in these words: “Sociological ideas are best reintroduced into quantitative sociological research by focusing on specifying the mechanisms by which change is brought about in social processes” (Sørensen 1998, p. 264). He argues that sociology requires better integration of theory and evidence. Central to an adequate explanatory theory, however, is the specification of the mechanism that is hypothesized to underlie a given set of observations. “Developing theoretical ideas about social processes is to specify some concept of what brings about a certain outcome—a change in political regimes, a new job, an increase in corporate performance, … The development of the conceptualization of change amounts to proposing a mechanism for a social process” (239-240). Sørensen makes the critical point that one cannot select a statistical model for analysis of a set of data without first asking the question, what in the nature of the mechanisms we wish to postulate to link the influences of some variables with others? Rather, it is necessary to have a hypothesis of the mechanisms that link the variables before we can arrive at a justified estimate of the relative importance of the causal variables in bringing about the outcome.

The general nature of the mechanisms that underlie sociological causation has been very much the subject of debate. Two broad approaches may be identified: agent-based models and social influence models. The former follow the strategy of aggregating the results of individual-level choices into macro-level outcomes; the latter attempt to identify the factors that work behind the backs of agents to influence their choices. (Sørensen refers to these as “pull” and “push” models; Sørensen, 1998.) Thomas Schelling’s apt title Micromotives and Macrobehavior captures the logic of the former approach, and his work profoundly illustrates the sometimes highly unpredictable results of the interactions of locally rational behavior. Jon Elster has also shed light on the ways in which the tools of rational choice theory support the construction of largescale sociological explanations (The Cement of Society: A Survey of Social Order). The second approach (the “push” approach) attempts to identify socially salient influences such as race, gender, educational status, and to provide detailed accounts of how these factors influence or constrain individual trajectories—thereby affecting sociological outcomes.

Emphasis on causal mechanisms for adequate social explanation has several salutary effects on sociological method. It takes us away from uncritical reliance on uncritical statistical models. But it also may take us away from excessive emphasis on large-scale classification of events into revolutions, democracies, or religions, and toward more specific analysis of the processes and features that serve to discriminate among instances of large social categories. Charles Tilly emphasizes this point in his arguments for causal narratives in comparative sociology (Tilly 1995). He writes, “I am arguing that regularities in political life are very broad, indeed transhistorical, but do not operate in the form of recurrent structures and processes at a large scale. They consist of recurrent causes which in different circumstances and sequences compound into highly variable but nonetheless explicable effects” (Tilly 1995, p. 1601).

Citations

1. Dupré, John, and Nancy Cartwright. 1988. Probability and Causality: Why Hume and Indeterminism Don't Mix. Nous 22:521-536.
2. Ekstrom, Mats. 1992. Causal explanation of social action: The Contribution of Max Weber and of Critical Realism to a Generative View of Causal Explanation in the Social Sciences. Acta Sociologica 35 (2):107(16).
3. Sørensen, Aage B. 1998. Theoretical mechanisms and the empirical study of social processes. In Social Mechanisms: An Analytical Approach to Social Theory, edited by P. Hedström and R. Swedberg.
4. Tilly, Charles. 1995. To Explain Political Processes. American Journal of Sociology.

Causal mechanisms

The central tenet of causal realism is a thesis about causal mechanisms or causal powers. We can only assert that there is a causal relationship between X and Y if we can offer a credible hypothesis of the sort of underlying mechanism that might connect X to the occurrence of Y. The sociologist Mats Ekström puts the view this way: “the essence of causal analysis is ... the elucidation of the processes that generate the objects, events, and actions we seek to explain” (Ekstrom 1992, p. 115). Authors who have urged the centrality of causal mechanisms for both explanatory and purposes include Nancy Cartwright (Nature's Capacities and Their Measurements), Jon Elster (Explaining Social Behavior: More Nuts and Bolts for the Social Sciences), Rom Harré (Causal Powers), and Wesley Salmon (Scientific Explanation and the Causal Structure of the World). (Hedstrom and Swedberg's collection, Social Mechanisms: An Analytical Approach to Social Theory, is a useful source. An important advocate for a realist interpretation of science is Roy Bhaskar's A Realist Theory of Science.)

Nancy Cartwright is one of the most original voices within contemporary philosophy of science. Cartwright places real causal mechanisms at the center of her account of scientific knowledge. As she and John Dupré put the point, “things and events have causal capacities: in virtue of the properties they possess, they have the power to bring about other events or states” (Dupré and Cartwright 1988). Cartwright argues, for the natural sciences, that the concept of a real causal connection among a set of events is more fundamental than the concept of a law of nature. And most fundamentally, she argues that identifying causal relations requires substantive theories of the causal powers (capacities, in her language) that govern the entities in question. Causal relations cannot be directly inferred from facts about association among variables. As she puts the point, “No reduction of generic causation to regularities is possible” (Nature's Capacities and Their Measurements, p. 90). The importance of this idea for sociological research is profound; it confirms the notion shared by many researchers that attribution of social causation depends inherently on the formulation of good, middle-level theories about the real causal properties of various social forces and entities.

What is a causal mechanism? Consider this formulation: a causal mechanism is a sequence of events, conditions, and processes leading from the explanans to the explanandum (Varieties Of Social Explanation, p. 15). A causal relation exists between X and Y if and only if there is a set of causal mechanisms that connect X to Y. This is an ontological premise, asserting that causal mechanisms are real and are the legitimate object of scientific investigation.

Aage Sørensen summarizes a causal realist position for sociology in these words: “Sociological ideas are best reintroduced into quantitative sociological research by focusing on specifying the mechanisms by which change is brought about in social processes” (Sørensen 1998, p. 264). He argues that sociology requires better integration of theory and evidence. Central to an adequate explanatory theory, however, is the specification of the mechanism that is hypothesized to underlie a given set of observations. “Developing theoretical ideas about social processes is to specify some concept of what brings about a certain outcome—a change in political regimes, a new job, an increase in corporate performance, … The development of the conceptualization of change amounts to proposing a mechanism for a social process” (239-240). Sørensen makes the critical point that one cannot select a statistical model for analysis of a set of data without first asking the question, what in the nature of the mechanisms we wish to postulate to link the influences of some variables with others? Rather, it is necessary to have a hypothesis of the mechanisms that link the variables before we can arrive at a justified estimate of the relative importance of the causal variables in bringing about the outcome.

The general nature of the mechanisms that underlie sociological causation has been very much the subject of debate. Two broad approaches may be identified: agent-based models and social influence models. The former follow the strategy of aggregating the results of individual-level choices into macro-level outcomes; the latter attempt to identify the factors that work behind the backs of agents to influence their choices. (Sørensen refers to these as “pull” and “push” models; Sørensen, 1998.) Thomas Schelling’s apt title Micromotives and Macrobehavior captures the logic of the former approach, and his work profoundly illustrates the sometimes highly unpredictable results of the interactions of locally rational behavior. Jon Elster has also shed light on the ways in which the tools of rational choice theory support the construction of largescale sociological explanations (The Cement of Society: A Survey of Social Order). The second approach (the “push” approach) attempts to identify socially salient influences such as race, gender, educational status, and to provide detailed accounts of how these factors influence or constrain individual trajectories—thereby affecting sociological outcomes.

Emphasis on causal mechanisms for adequate social explanation has several salutary effects on sociological method. It takes us away from uncritical reliance on uncritical statistical models. But it also may take us away from excessive emphasis on large-scale classification of events into revolutions, democracies, or religions, and toward more specific analysis of the processes and features that serve to discriminate among instances of large social categories. Charles Tilly emphasizes this point in his arguments for causal narratives in comparative sociology (Tilly 1995). He writes, “I am arguing that regularities in political life are very broad, indeed transhistorical, but do not operate in the form of recurrent structures and processes at a large scale. They consist of recurrent causes which in different circumstances and sequences compound into highly variable but nonetheless explicable effects” (Tilly 1995, p. 1601).

Citations

1. Dupré, John, and Nancy Cartwright. 1988. Probability and Causality: Why Hume and Indeterminism Don't Mix. Nous 22:521-536.
2. Ekstrom, Mats. 1992. Causal explanation of social action: The Contribution of Max Weber and of Critical Realism to a Generative View of Causal Explanation in the Social Sciences. Acta Sociologica 35 (2):107(16).
3. Sørensen, Aage B. 1998. Theoretical mechanisms and the empirical study of social processes. In Social Mechanisms: An Analytical Approach to Social Theory, edited by P. Hedström and R. Swedberg.
4. Tilly, Charles. 1995. To Explain Political Processes. American Journal of Sociology.

Causal mechanisms

The central tenet of causal realism is a thesis about causal mechanisms or causal powers. We can only assert that there is a causal relationship between X and Y if we can offer a credible hypothesis of the sort of underlying mechanism that might connect X to the occurrence of Y. The sociologist Mats Ekström puts the view this way: “the essence of causal analysis is ... the elucidation of the processes that generate the objects, events, and actions we seek to explain” (Ekstrom 1992, p. 115). Authors who have urged the centrality of causal mechanisms for both explanatory and purposes include Nancy Cartwright (Nature's Capacities and Their Measurements), Jon Elster (Explaining Social Behavior: More Nuts and Bolts for the Social Sciences), Rom Harré (Causal Powers), and Wesley Salmon (Scientific Explanation and the Causal Structure of the World). (Hedstrom and Swedberg's collection, Social Mechanisms: An Analytical Approach to Social Theory, is a useful source. An important advocate for a realist interpretation of science is Roy Bhaskar's A Realist Theory of Science.)

Nancy Cartwright is one of the most original voices within contemporary philosophy of science. Cartwright places real causal mechanisms at the center of her account of scientific knowledge. As she and John Dupré put the point, “things and events have causal capacities: in virtue of the properties they possess, they have the power to bring about other events or states” (Dupré and Cartwright 1988). Cartwright argues, for the natural sciences, that the concept of a real causal connection among a set of events is more fundamental than the concept of a law of nature. And most fundamentally, she argues that identifying causal relations requires substantive theories of the causal powers (capacities, in her language) that govern the entities in question. Causal relations cannot be directly inferred from facts about association among variables. As she puts the point, “No reduction of generic causation to regularities is possible” (Nature's Capacities and Their Measurements, p. 90). The importance of this idea for sociological research is profound; it confirms the notion shared by many researchers that attribution of social causation depends inherently on the formulation of good, middle-level theories about the real causal properties of various social forces and entities.

What is a causal mechanism? Consider this formulation: a causal mechanism is a sequence of events, conditions, and processes leading from the explanans to the explanandum (Varieties Of Social Explanation, p. 15). A causal relation exists between X and Y if and only if there is a set of causal mechanisms that connect X to Y. This is an ontological premise, asserting that causal mechanisms are real and are the legitimate object of scientific investigation.

Aage Sørensen summarizes a causal realist position for sociology in these words: “Sociological ideas are best reintroduced into quantitative sociological research by focusing on specifying the mechanisms by which change is brought about in social processes” (Sørensen 1998, p. 264). He argues that sociology requires better integration of theory and evidence. Central to an adequate explanatory theory, however, is the specification of the mechanism that is hypothesized to underlie a given set of observations. “Developing theoretical ideas about social processes is to specify some concept of what brings about a certain outcome—a change in political regimes, a new job, an increase in corporate performance, … The development of the conceptualization of change amounts to proposing a mechanism for a social process” (239-240). Sørensen makes the critical point that one cannot select a statistical model for analysis of a set of data without first asking the question, what in the nature of the mechanisms we wish to postulate to link the influences of some variables with others? Rather, it is necessary to have a hypothesis of the mechanisms that link the variables before we can arrive at a justified estimate of the relative importance of the causal variables in bringing about the outcome.

The general nature of the mechanisms that underlie sociological causation has been very much the subject of debate. Two broad approaches may be identified: agent-based models and social influence models. The former follow the strategy of aggregating the results of individual-level choices into macro-level outcomes; the latter attempt to identify the factors that work behind the backs of agents to influence their choices. (Sørensen refers to these as “pull” and “push” models; Sørensen, 1998.) Thomas Schelling’s apt title Micromotives and Macrobehavior captures the logic of the former approach, and his work profoundly illustrates the sometimes highly unpredictable results of the interactions of locally rational behavior. Jon Elster has also shed light on the ways in which the tools of rational choice theory support the construction of largescale sociological explanations (The Cement of Society: A Survey of Social Order). The second approach (the “push” approach) attempts to identify socially salient influences such as race, gender, educational status, and to provide detailed accounts of how these factors influence or constrain individual trajectories—thereby affecting sociological outcomes.

Emphasis on causal mechanisms for adequate social explanation has several salutary effects on sociological method. It takes us away from uncritical reliance on uncritical statistical models. But it also may take us away from excessive emphasis on large-scale classification of events into revolutions, democracies, or religions, and toward more specific analysis of the processes and features that serve to discriminate among instances of large social categories. Charles Tilly emphasizes this point in his arguments for causal narratives in comparative sociology (Tilly 1995). He writes, “I am arguing that regularities in political life are very broad, indeed transhistorical, but do not operate in the form of recurrent structures and processes at a large scale. They consist of recurrent causes which in different circumstances and sequences compound into highly variable but nonetheless explicable effects” (Tilly 1995, p. 1601).

Citations

1. Dupré, John, and Nancy Cartwright. 1988. Probability and Causality: Why Hume and Indeterminism Don't Mix. Nous 22:521-536.
2. Ekstrom, Mats. 1992. Causal explanation of social action: The Contribution of Max Weber and of Critical Realism to a Generative View of Causal Explanation in the Social Sciences. Acta Sociologica 35 (2):107(16).
3. Sørensen, Aage B. 1998. Theoretical mechanisms and the empirical study of social processes. In Social Mechanisms: An Analytical Approach to Social Theory, edited by P. Hedström and R. Swedberg.
4. Tilly, Charles. 1995. To Explain Political Processes. American Journal of Sociology.

What social science can do

Quite a few postings here emphasize the limits of social science knowledge. Prediction of the behavior of large social wholes is difficult to impossible. There are few strong regularities among social phenomena. Social entities and processes are heterogeneous, plastic, and path-dependent. So the question arises: what can the social sciences do that takes them beyond the realm of description and reportage of the blooming, buzzing confusion of social comings and goings, to something that is more explanatory and generalizable?

I think there is an answer to this, and it has to do with identifying mid-level mechanisms and processes that recur in roughly similar ways in a range of different social settings. The social sciences can identify a fairly large number of these sorts of recurring mechanisms. For example --

• public goods problems
• political entrepreneurship
• principal-agent problems
• features of ethnic or religious group mobilization
• market mechanisms and failures
• rent-seeking behavior
• the social psychology associated with small groups
• the moral emotions of family and kinship
• the dynamics of a transport network
• the communications characteristics of medium-size social networks
• the psychology and circumstances of solidarity

Further, the social sciences can attempt to discover the circumstances at the level of individual agents that make these mechanisms robust across social settings. They can model the dynamics and features of aggregation that they possess. And they can attempt to discover the workings of such mechanisms in particular social and historical settings, and work towards explanations of particular features of these events based on their theories of the properties of the mechanisms. Finally, they can attempt to find rigorous ways of attempting to model the effects of aggregating multiple mechanisms in a particular setting.

What this comes down to is the view that the main theoretical and generalizing contribution that the social sciences can make is the discovery and analysis of a wise range of recurring social mechanisms grounded in features of human agency and common institutional and material settings. They can help to constitute a rich tool box for social explanation. And, in a weak and fallible way, they can lay the basis for some limited social generalizations -- for example, "In circumstances where a group of independent individuals make private decisions about their actions, the public goods shared by the group will be under-provided."

This approach affords a degree of explanatory capacity and generalization to the social sciences. What it does not underwrite is the ability to offer general, comprehensive theories about any complex kind of phenomenon -- cities, schools, revolutions. And it does not provide a foundation for confidence about large predictions about the future behavior of complex social wholes.

What social science can do

Quite a few postings here emphasize the limits of social science knowledge. Prediction of the behavior of large social wholes is difficult to impossible. There are few strong regularities among social phenomena. Social entities and processes are heterogeneous, plastic, and path-dependent. So the question arises: what can the social sciences do that takes them beyond the realm of description and reportage of the blooming, buzzing confusion of social comings and goings, to something that is more explanatory and generalizable?

I think there is an answer to this, and it has to do with identifying mid-level mechanisms and processes that recur in roughly similar ways in a range of different social settings. The social sciences can identify a fairly large number of these sorts of recurring mechanisms. For example --

• public goods problems
• political entrepreneurship
• principal-agent problems
• features of ethnic or religious group mobilization
• market mechanisms and failures
• rent-seeking behavior
• the social psychology associated with small groups
• the moral emotions of family and kinship
• the dynamics of a transport network
• the communications characteristics of medium-size social networks
• the psychology and circumstances of solidarity

Further, the social sciences can attempt to discover the circumstances at the level of individual agents that make these mechanisms robust across social settings. They can model the dynamics and features of aggregation that they possess. And they can attempt to discover the workings of such mechanisms in particular social and historical settings, and work towards explanations of particular features of these events based on their theories of the properties of the mechanisms. Finally, they can attempt to find rigorous ways of attempting to model the effects of aggregating multiple mechanisms in a particular setting.

What this comes down to is the view that the main theoretical and generalizing contribution that the social sciences can make is the discovery and analysis of a wise range of recurring social mechanisms grounded in features of human agency and common institutional and material settings. They can help to constitute a rich tool box for social explanation. And, in a weak and fallible way, they can lay the basis for some limited social generalizations -- for example, "In circumstances where a group of independent individuals make private decisions about their actions, the public goods shared by the group will be under-provided."

This approach affords a degree of explanatory capacity and generalization to the social sciences. What it does not underwrite is the ability to offer general, comprehensive theories about any complex kind of phenomenon -- cities, schools, revolutions. And it does not provide a foundation for confidence about large predictions about the future behavior of complex social wholes.

What social science can do

Quite a few postings here emphasize the limits of social science knowledge. Prediction of the behavior of large social wholes is difficult to impossible. There are few strong regularities among social phenomena. Social entities and processes are heterogeneous, plastic, and path-dependent. So the question arises: what can the social sciences do that takes them beyond the realm of description and reportage of the blooming, buzzing confusion of social comings and goings, to something that is more explanatory and generalizable?

I think there is an answer to this, and it has to do with identifying mid-level mechanisms and processes that recur in roughly similar ways in a range of different social settings. The social sciences can identify a fairly large number of these sorts of recurring mechanisms. For example --

• public goods problems
• political entrepreneurship
• principal-agent problems
• features of ethnic or religious group mobilization
• market mechanisms and failures
• rent-seeking behavior
• the social psychology associated with small groups
• the moral emotions of family and kinship
• the dynamics of a transport network
• the communications characteristics of medium-size social networks
• the psychology and circumstances of solidarity

Further, the social sciences can attempt to discover the circumstances at the level of individual agents that make these mechanisms robust across social settings. They can model the dynamics and features of aggregation that they possess. And they can attempt to discover the workings of such mechanisms in particular social and historical settings, and work towards explanations of particular features of these events based on their theories of the properties of the mechanisms. Finally, they can attempt to find rigorous ways of attempting to model the effects of aggregating multiple mechanisms in a particular setting.

What this comes down to is the view that the main theoretical and generalizing contribution that the social sciences can make is the discovery and analysis of a wise range of recurring social mechanisms grounded in features of human agency and common institutional and material settings. They can help to constitute a rich tool box for social explanation. And, in a weak and fallible way, they can lay the basis for some limited social generalizations -- for example, "In circumstances where a group of independent individuals make private decisions about their actions, the public goods shared by the group will be under-provided."

This approach affords a degree of explanatory capacity and generalization to the social sciences. What it does not underwrite is the ability to offer general, comprehensive theories about any complex kind of phenomenon -- cities, schools, revolutions. And it does not provide a foundation for confidence about large predictions about the future behavior of complex social wholes.

Safety as a social effect

Some organizations pose large safety issues for the public because of the technologies and processes they encompass. Industrial factories, chemical and nuclear plants, farms, mines, and aviation all represent sectors where safety issues are critically important because of the inherent risks of the processes they involve. However, "safety" is not primarily a technological characteristic; instead, it is an aggregate outcome that depends as much on the social organization and management of the processes involved as it does on the technologies they employ. (See an earlier posting on technology failure.)

We can define safety by relating it to the concept of "harmful incident". A harmful incident is an occurrence that leads to injury or death of one or more persons. Safety is a relative concept, in that it involves analysis and comparison of the frequencies of harmful incidents relative to some measure of the volume of activity. If the claim is made that interstate highways are safer than county roads, this amounts to the assertion that there are fewer accidents per vehicle-mile on the former than the latter. If it is held that commercial aviation is safer than automobile transportation, this amounts to the claim that there are fewer harms per passenger-mile in air travel than auto travel. And if it is observed that the computer assembly industry is safer than the mining industry, this can be understood to mean that there are fewer harms per person-day in the one sector than the other. (We might give a parallel analysis of the concept of a healthy workplace.)

This analysis highlights two dimensions of industrial safety: the inherent capacity for creating harms associated with the technology and processes in use (heavy machinery, blasting, and uncertain tunnel stability in mining, in contrast to a computer and a red pencil on the editorial offices of a newspaper), and the processes and systems that are in place to guard against harm. The first set of factors is roughly "technological," while the second set is social and organizational.

Variations in safety records across industries and across sites within a given industry provide an excellent tool for analyzing the effects of various institutional arrangements. It is often possible to pinpoint a crucial difference in organization -- supervision, training, internal procedures, inspection protocols, etc. -- that can account for a high accident rate in one factory and a low rate in an otherwise similar factory in a different state.

One of the most important findings of safety engineering is that organization and culture play critical roles in enhancing the safety characteristics of a given activity -- that is to say, safety is strongly influenced by social factors that define and organize the behaviors of workers, users, or managers. (See Charles Perrow, Normal Accidents: Living with High-Risk Technologies and Nancy Leveson, Safeware: System Safety and Computers, for a couple of excellent treatments of the sociological dimensions of safety.)

This isn't to say that only social factors can influence safety performance within an activity or industry. In fact, a central effort by safety engineers involves modifying the technology or process so as to remove the source of harm completely -- what we might call "passive" safety. So, for example, if it is possible to design a nuclear reactor in such a way that a loss of coolant leads automatically to shutdown of the fission reaction, then we have designed out of the system the possibility of catastrophic meltdown and escape of radioactive material. This might be called "design for soft landings".

However, most safety experts agree that the social and organizational characteristics of the dangerous activity are the most common causes of bad safety performance. Poor supervision and inspection of maintenance operations leads to mechanical failures, potentially harming workers or the public. A workplace culture that discourages disclosure of unsafe conditions makes the likelihood of accidental harm much greater. A communications system that permits ambiguous or unclear messages to occur can lead to air crashes and wrong-site surgeries.

This brings us at last to the point of this posting: the observation that safety data in a variety of industries and locations permit us to probe organizational features and their effects with quite a bit of precision. This is a place where institutions and organizations make a big difference in observable outcomes; safety is a consequence of a specific combination of technology, behaviors, and organizational practices. This is a good opportunity for combining comparative and statistical research methods in support of causal inquiry, and it invites us to probe for the social mechanisms that underlie the patterns of high or low safety performance that we discover.

Consider one example. Suppose we are interested in discovering some of the determinants of safety records in deep mining operations. We might approach the question from several points of view.

• We might select five mines with "best in class" safety records and compare them in detail with five "worst in class" mines. Are there organizational or techology features that distinguish the cases?
• We might do the large-N version of this study: examine a sample of mines from "best in class" and "worst in class" and test whether there are observed features that explain the differences in safety records. (For example, we may find that 75% of the former group but only 10% of the latter group are subject to frequent unannounced safety inspection. This supports the notion that inspections enhance safety.)
• We might compare national records for mine safety--say, Poland and Britain. We might then attempt to identify the general characteristics that describe mines in the two countries and attempt to explain observed differences in safety records on the basis of these characteristics. Possible candidates might include degree of regulatory authority, capital investment per mine, workers per mine, ...
• We might form a hypothesis about a factor that should be expected to enhance safety -- a company-endorsed safety education program, let's say -- and then randomly assign a group of mines to "treated" and "untreated" groups and compare safety records. (This is a quasi-experiment; see an earlier posting for a discussion of this mode of reasoning.) If we find that the treated group differs significantly in average safety performance, this supports the claim that the treatment is causally relevant to the safety outcome.

Investigations along these lines can establish an empirical basis for judging that one or more organizational features A, B, C have consequences for safety performance. In order to be confident in these judgments, however, we need to supplement the empirical analysis with a theory of the mechanisms through which features like A, B, C influence behavior in such a way as to make accidents more or less likely.

Safety, then, seems to be a good area of investigation for researchers within the general framework of the new institutionalism, because the effects of institutional and organizational differences emerge as observable differences in the rates of accidents in comparable industrial settings. (See Mary Brinton and Victor Nee, The New Institutionalism in Sociology, for a collection of essays on this approach.)

Safety as a social effect

Some organizations pose large safety issues for the public because of the technologies and processes they encompass. Industrial factories, chemical and nuclear plants, farms, mines, and aviation all represent sectors where safety issues are critically important because of the inherent risks of the processes they involve. However, "safety" is not primarily a technological characteristic; instead, it is an aggregate outcome that depends as much on the social organization and management of the processes involved as it does on the technologies they employ. (See an earlier posting on technology failure.)

We can define safety by relating it to the concept of "harmful incident". A harmful incident is an occurrence that leads to injury or death of one or more persons. Safety is a relative concept, in that it involves analysis and comparison of the frequencies of harmful incidents relative to some measure of the volume of activity. If the claim is made that interstate highways are safer than county roads, this amounts to the assertion that there are fewer accidents per vehicle-mile on the former than the latter. If it is held that commercial aviation is safer than automobile transportation, this amounts to the claim that there are fewer harms per passenger-mile in air travel than auto travel. And if it is observed that the computer assembly industry is safer than the mining industry, this can be understood to mean that there are fewer harms per person-day in the one sector than the other. (We might give a parallel analysis of the concept of a healthy workplace.)

This analysis highlights two dimensions of industrial safety: the inherent capacity for creating harms associated with the technology and processes in use (heavy machinery, blasting, and uncertain tunnel stability in mining, in contrast to a computer and a red pencil on the editorial offices of a newspaper), and the processes and systems that are in place to guard against harm. The first set of factors is roughly "technological," while the second set is social and organizational.

Variations in safety records across industries and across sites within a given industry provide an excellent tool for analyzing the effects of various institutional arrangements. It is often possible to pinpoint a crucial difference in organization -- supervision, training, internal procedures, inspection protocols, etc. -- that can account for a high accident rate in one factory and a low rate in an otherwise similar factory in a different state.

One of the most important findings of safety engineering is that organization and culture play critical roles in enhancing the safety characteristics of a given activity -- that is to say, safety is strongly influenced by social factors that define and organize the behaviors of workers, users, or managers. (See Charles Perrow, Normal Accidents: Living with High-Risk Technologies and Nancy Leveson, Safeware: System Safety and Computers, for a couple of excellent treatments of the sociological dimensions of safety.)

This isn't to say that only social factors can influence safety performance within an activity or industry. In fact, a central effort by safety engineers involves modifying the technology or process so as to remove the source of harm completely -- what we might call "passive" safety. So, for example, if it is possible to design a nuclear reactor in such a way that a loss of coolant leads automatically to shutdown of the fission reaction, then we have designed out of the system the possibility of catastrophic meltdown and escape of radioactive material. This might be called "design for soft landings".

However, most safety experts agree that the social and organizational characteristics of the dangerous activity are the most common causes of bad safety performance. Poor supervision and inspection of maintenance operations leads to mechanical failures, potentially harming workers or the public. A workplace culture that discourages disclosure of unsafe conditions makes the likelihood of accidental harm much greater. A communications system that permits ambiguous or unclear messages to occur can lead to air crashes and wrong-site surgeries.

This brings us at last to the point of this posting: the observation that safety data in a variety of industries and locations permit us to probe organizational features and their effects with quite a bit of precision. This is a place where institutions and organizations make a big difference in observable outcomes; safety is a consequence of a specific combination of technology, behaviors, and organizational practices. This is a good opportunity for combining comparative and statistical research methods in support of causal inquiry, and it invites us to probe for the social mechanisms that underlie the patterns of high or low safety performance that we discover.

Consider one example. Suppose we are interested in discovering some of the determinants of safety records in deep mining operations. We might approach the question from several points of view.

• We might select five mines with "best in class" safety records and compare them in detail with five "worst in class" mines. Are there organizational or techology features that distinguish the cases?
• We might do the large-N version of this study: examine a sample of mines from "best in class" and "worst in class" and test whether there are observed features that explain the differences in safety records. (For example, we may find that 75% of the former group but only 10% of the latter group are subject to frequent unannounced safety inspection. This supports the notion that inspections enhance safety.)
• We might compare national records for mine safety--say, Poland and Britain. We might then attempt to identify the general characteristics that describe mines in the two countries and attempt to explain observed differences in safety records on the basis of these characteristics. Possible candidates might include degree of regulatory authority, capital investment per mine, workers per mine, ...
• We might form a hypothesis about a factor that should be expected to enhance safety -- a company-endorsed safety education program, let's say -- and then randomly assign a group of mines to "treated" and "untreated" groups and compare safety records. (This is a quasi-experiment; see an earlier posting for a discussion of this mode of reasoning.) If we find that the treated group differs significantly in average safety performance, this supports the claim that the treatment is causally relevant to the safety outcome.

Investigations along these lines can establish an empirical basis for judging that one or more organizational features A, B, C have consequences for safety performance. In order to be confident in these judgments, however, we need to supplement the empirical analysis with a theory of the mechanisms through which features like A, B, C influence behavior in such a way as to make accidents more or less likely.

Safety, then, seems to be a good area of investigation for researchers within the general framework of the new institutionalism, because the effects of institutional and organizational differences emerge as observable differences in the rates of accidents in comparable industrial settings. (See Mary Brinton and Victor Nee, The New Institutionalism in Sociology, for a collection of essays on this approach.)

Safety as a social effect

Some organizations pose large safety issues for the public because of the technologies and processes they encompass. Industrial factories, chemical and nuclear plants, farms, mines, and aviation all represent sectors where safety issues are critically important because of the inherent risks of the processes they involve. However, "safety" is not primarily a technological characteristic; instead, it is an aggregate outcome that depends as much on the social organization and management of the processes involved as it does on the technologies they employ. (See an earlier posting on technology failure.)

We can define safety by relating it to the concept of "harmful incident". A harmful incident is an occurrence that leads to injury or death of one or more persons. Safety is a relative concept, in that it involves analysis and comparison of the frequencies of harmful incidents relative to some measure of the volume of activity. If the claim is made that interstate highways are safer than county roads, this amounts to the assertion that there are fewer accidents per vehicle-mile on the former than the latter. If it is held that commercial aviation is safer than automobile transportation, this amounts to the claim that there are fewer harms per passenger-mile in air travel than auto travel. And if it is observed that the computer assembly industry is safer than the mining industry, this can be understood to mean that there are fewer harms per person-day in the one sector than the other. (We might give a parallel analysis of the concept of a healthy workplace.)

This analysis highlights two dimensions of industrial safety: the inherent capacity for creating harms associated with the technology and processes in use (heavy machinery, blasting, and uncertain tunnel stability in mining, in contrast to a computer and a red pencil on the editorial offices of a newspaper), and the processes and systems that are in place to guard against harm. The first set of factors is roughly "technological," while the second set is social and organizational.

Variations in safety records across industries and across sites within a given industry provide an excellent tool for analyzing the effects of various institutional arrangements. It is often possible to pinpoint a crucial difference in organization -- supervision, training, internal procedures, inspection protocols, etc. -- that can account for a high accident rate in one factory and a low rate in an otherwise similar factory in a different state.

One of the most important findings of safety engineering is that organization and culture play critical roles in enhancing the safety characteristics of a given activity -- that is to say, safety is strongly influenced by social factors that define and organize the behaviors of workers, users, or managers. (See Charles Perrow, Normal Accidents: Living with High-Risk Technologies and Nancy Leveson, Safeware: System Safety and Computers, for a couple of excellent treatments of the sociological dimensions of safety.)

This isn't to say that only social factors can influence safety performance within an activity or industry. In fact, a central effort by safety engineers involves modifying the technology or process so as to remove the source of harm completely -- what we might call "passive" safety. So, for example, if it is possible to design a nuclear reactor in such a way that a loss of coolant leads automatically to shutdown of the fission reaction, then we have designed out of the system the possibility of catastrophic meltdown and escape of radioactive material. This might be called "design for soft landings".

However, most safety experts agree that the social and organizational characteristics of the dangerous activity are the most common causes of bad safety performance. Poor supervision and inspection of maintenance operations leads to mechanical failures, potentially harming workers or the public. A workplace culture that discourages disclosure of unsafe conditions makes the likelihood of accidental harm much greater. A communications system that permits ambiguous or unclear messages to occur can lead to air crashes and wrong-site surgeries.

This brings us at last to the point of this posting: the observation that safety data in a variety of industries and locations permit us to probe organizational features and their effects with quite a bit of precision. This is a place where institutions and organizations make a big difference in observable outcomes; safety is a consequence of a specific combination of technology, behaviors, and organizational practices. This is a good opportunity for combining comparative and statistical research methods in support of causal inquiry, and it invites us to probe for the social mechanisms that underlie the patterns of high or low safety performance that we discover.

Consider one example. Suppose we are interested in discovering some of the determinants of safety records in deep mining operations. We might approach the question from several points of view.

• We might select five mines with "best in class" safety records and compare them in detail with five "worst in class" mines. Are there organizational or techology features that distinguish the cases?
• We might do the large-N version of this study: examine a sample of mines from "best in class" and "worst in class" and test whether there are observed features that explain the differences in safety records. (For example, we may find that 75% of the former group but only 10% of the latter group are subject to frequent unannounced safety inspection. This supports the notion that inspections enhance safety.)
• We might compare national records for mine safety--say, Poland and Britain. We might then attempt to identify the general characteristics that describe mines in the two countries and attempt to explain observed differences in safety records on the basis of these characteristics. Possible candidates might include degree of regulatory authority, capital investment per mine, workers per mine, ...
• We might form a hypothesis about a factor that should be expected to enhance safety -- a company-endorsed safety education program, let's say -- and then randomly assign a group of mines to "treated" and "untreated" groups and compare safety records. (This is a quasi-experiment; see an earlier posting for a discussion of this mode of reasoning.) If we find that the treated group differs significantly in average safety performance, this supports the claim that the treatment is causally relevant to the safety outcome.

Investigations along these lines can establish an empirical basis for judging that one or more organizational features A, B, C have consequences for safety performance. In order to be confident in these judgments, however, we need to supplement the empirical analysis with a theory of the mechanisms through which features like A, B, C influence behavior in such a way as to make accidents more or less likely.

Safety, then, seems to be a good area of investigation for researchers within the general framework of the new institutionalism, because the effects of institutional and organizational differences emerge as observable differences in the rates of accidents in comparable industrial settings. (See Mary Brinton and Victor Nee, The New Institutionalism in Sociology, for a collection of essays on this approach.)

Heterogeneity of the social

I think heterogeneity is a very basic characteristic of the domain of the social. And I think this makes a big difference for how we should attempt to study the social world "scientifically". What sorts of things am I thinking about here?

Let's start with some semantics. A heterogeneous group of things is the contrary of a homogeneous group, and we can define homogeneity as "a group of fundamentally similar units or samples". A homogeneous body may consist of a group of units with identical properties, or it may be a smooth mixture of different things, consisting of a similar composition at many levels of scale. A fruitcake is non-homogeneous, in that distinct volumes may include just cake or a mix of cake and dried cherries, or cake and the occasional walnut. The properties of fruitcake depend on which sample we encounter. A well mixed volume of oil and vinegar, by contrast, is homogeneous in a specific sense: the properties of each sample volume are the same as any other. The basic claim about the heterogeneity of the social comes down to this: at many levels of scale we continue to find a diversity of social things and processes at work. Society is more similar to fruitcake than cheesecake.

Heterogeneity makes a difference because one of the central goals of positivist science is to discover strong regularities among classes of phenomena, and regularities appear to presuppose homogeneity of the things over which the regularities are thought to obtain. So to observe that social phenomena are deeply heterogeneous at many levels of scale, is to cast fundamental doubt on the goal of discovering strong social regularities.

Let's consider some of the forms of heterogeneity that the social world illustrates.

First is the heterogeneity of social causes and influences. Social events are commonly the result of a variety of different kinds of causes that come together in highly contingent conjunctions. A revolution may be caused by a protracted drought, a harsh system of land tenure, a new ideology of peasant solidarity, a communications system that conveys messages to the rural poor, and an unexpected spar within the rulers -- all coming together at a moment in time. And this range of causal factors, in turn, shows up in the background of a very heterogeneous set of effects. (A transportation network, for example, may play a causal role in the occurrence of an epidemic, the spread of radical ideas, and a long, slow process of urban settlement.) The causes of an event are a mixed group of dissimilar influences with different dynamics and temporalities, and the effects of a given causal factor are also a mixed and dissimilar group.

Second is the heterogeneity that can be discovered within social categories of things -- cities, religions, electoral democracies, social movements. Think of the diversity within Islam documented so well by Clifford Geertz (Islam Observed: Religious Development in Morocco and Indonesia); the diversity at multiple levels that exists among great cities like Beijing, New York, Geneva, and Rio (institutions, demography, ethnic groups, economic characteristics, administrative roles, ...); the institutional variety that exists in the electoral democracies of India, France, and Argentina; or the wild diversity across the social movements of the right.

Third is the heterogeneity that can be discovered across and within social groups. It is not the case that all Kansans think alike -- and this is true for whatever descriptors we might choose in order to achieve greater homogeneity (evangelical Kansans, urban evangelical Kansans, ...). There are always interesting gradients within any social group. Likewise, there is great variation in the nature of ordinary, lived experience -- for middle-class French families celebrating quatorze Juillet, for Californians celebrating July 4, and for Brazilians enjoying Dia da Independência on September 7.

A fourth form of heterogeneity takes us within the agent herself, when we note the variety of motives, moral frameworks, emotions, and modes of agency on the basis of which people act. This is one of the weaknesses of doctrinaire rational choice theory or dogmatic Marxism, the analytical assumption of a single dimension of motivation and reasoning. Instead, it is visible that one person acts for a variety of motives at a given time, persons shift their motives over time, and members of groups differ in terms of their motivational structure as well. So there is heterogeneity of motives and agency within the agent.

These dimensions of heterogeneity make the point: the social world is an ensemble, a dynamic mixture, and an ongoing interaction of forces, agents, structures, and mentalities. Social outcomes emerge from this heterogeneous and dynamic mixture, and the quest for general laws is deeply quixotic.

Where does the heterogeneity principle take us? It suggests an explanatory strategy: instead of looking for laws of whole categories of events and things, rather than searching for simple answers to questions like "why do revolutions occur?", we might instead look to a "concatenation" strategy. That is, we might simply acknowledge the fact of molar heterogeneity and look instead for some of the different processes and things in play in a given item of interest, and the build up a theory of the whole as a concatenation of the particulars of the parts.

Significantly, this strategy takes us to several fruitful ideas that already have some currency.

First is the idea of looking for microfoundations for observed social processes; (Microfoundations, Methods, and Causation: On the Philosophy of the Social Sciences). Here the idea is that higher-level social processes, causes, and events, need to be placed within the context of an account of the agent-level institutions and circumstances that convey those processes.

Second is the method of causal mechanisms advocated by McAdam, Tarrow, and Tilly, and discussed frequently here (Dynamics of Contention (Cambridge Studies in Contentious Politics)). Put simply, the approach recommends that we explain an outcome as the contingent result of the concatenation of a set of independent causal mechanisms (escalation, intra-group competition, repression, ...).

And third is the theory of "assemblages", recommended by Nick from accursedshare and derived from some of the theories of Gilles Deleuze. (Manuel Delanda describes this theory in A New Philosophy of Society: Assemblage Theory And Social Complexity.)

Each of these ideas gives expression to the important truth of the heterogeneity principle: that social outcomes are the aggregate result of a number of lower-level processes and institutions that give rise to them, and that social outcomes are contingent results of interaction and concatenation of these lower-level processes.

Heterogeneity of the social

I think heterogeneity is a very basic characteristic of the domain of the social. And I think this makes a big difference for how we should attempt to study the social world "scientifically". What sorts of things am I thinking about here?

Let's start with some semantics. A heterogeneous group of things is the contrary of a homogeneous group, and we can define homogeneity as "a group of fundamentally similar units or samples". A homogeneous body may consist of a group of units with identical properties, or it may be a smooth mixture of different things, consisting of a similar composition at many levels of scale. A fruitcake is non-homogeneous, in that distinct volumes may include just cake or a mix of cake and dried cherries, or cake and the occasional walnut. The properties of fruitcake depend on which sample we encounter. A well mixed volume of oil and vinegar, by contrast, is homogeneous in a specific sense: the properties of each sample volume are the same as any other. The basic claim about the heterogeneity of the social comes down to this: at many levels of scale we continue to find a diversity of social things and processes at work. Society is more similar to fruitcake than cheesecake.

Heterogeneity makes a difference because one of the central goals of positivist science is to discover strong regularities among classes of phenomena, and regularities appear to presuppose homogeneity of the things over which the regularities are thought to obtain. So to observe that social phenomena are deeply heterogeneous at many levels of scale, is to cast fundamental doubt on the goal of discovering strong social regularities.

Let's consider some of the forms of heterogeneity that the social world illustrates.

First is the heterogeneity of social causes and influences. Social events are commonly the result of a variety of different kinds of causes that come together in highly contingent conjunctions. A revolution may be caused by a protracted drought, a harsh system of land tenure, a new ideology of peasant solidarity, a communications system that conveys messages to the rural poor, and an unexpected spar within the rulers -- all coming together at a moment in time. And this range of causal factors, in turn, shows up in the background of a very heterogeneous set of effects. (A transportation network, for example, may play a causal role in the occurrence of an epidemic, the spread of radical ideas, and a long, slow process of urban settlement.) The causes of an event are a mixed group of dissimilar influences with different dynamics and temporalities, and the effects of a given causal factor are also a mixed and dissimilar group.

Second is the heterogeneity that can be discovered within social categories of things -- cities, religions, electoral democracies, social movements. Think of the diversity within Islam documented so well by Clifford Geertz (Islam Observed: Religious Development in Morocco and Indonesia); the diversity at multiple levels that exists among great cities like Beijing, New York, Geneva, and Rio (institutions, demography, ethnic groups, economic characteristics, administrative roles, ...); the institutional variety that exists in the electoral democracies of India, France, and Argentina; or the wild diversity across the social movements of the right.

Third is the heterogeneity that can be discovered across and within social groups. It is not the case that all Kansans think alike -- and this is true for whatever descriptors we might choose in order to achieve greater homogeneity (evangelical Kansans, urban evangelical Kansans, ...). There are always interesting gradients within any social group. Likewise, there is great variation in the nature of ordinary, lived experience -- for middle-class French families celebrating quatorze Juillet, for Californians celebrating July 4, and for Brazilians enjoying Dia da Independência on September 7.

A fourth form of heterogeneity takes us within the agent herself, when we note the variety of motives, moral frameworks, emotions, and modes of agency on the basis of which people act. This is one of the weaknesses of doctrinaire rational choice theory or dogmatic Marxism, the analytical assumption of a single dimension of motivation and reasoning. Instead, it is visible that one person acts for a variety of motives at a given time, persons shift their motives over time, and members of groups differ in terms of their motivational structure as well. So there is heterogeneity of motives and agency within the agent.

These dimensions of heterogeneity make the point: the social world is an ensemble, a dynamic mixture, and an ongoing interaction of forces, agents, structures, and mentalities. Social outcomes emerge from this heterogeneous and dynamic mixture, and the quest for general laws is deeply quixotic.

Where does the heterogeneity principle take us? It suggests an explanatory strategy: instead of looking for laws of whole categories of events and things, rather than searching for simple answers to questions like "why do revolutions occur?", we might instead look to a "concatenation" strategy. That is, we might simply acknowledge the fact of molar heterogeneity and look instead for some of the different processes and things in play in a given item of interest, and the build up a theory of the whole as a concatenation of the particulars of the parts.

Significantly, this strategy takes us to several fruitful ideas that already have some currency.

First is the idea of looking for microfoundations for observed social processes; (Microfoundations, Methods, and Causation: On the Philosophy of the Social Sciences). Here the idea is that higher-level social processes, causes, and events, need to be placed within the context of an account of the agent-level institutions and circumstances that convey those processes.

Second is the method of causal mechanisms advocated by McAdam, Tarrow, and Tilly, and discussed frequently here (Dynamics of Contention (Cambridge Studies in Contentious Politics)). Put simply, the approach recommends that we explain an outcome as the contingent result of the concatenation of a set of independent causal mechanisms (escalation, intra-group competition, repression, ...).

And third is the theory of "assemblages", recommended by Nick from accursedshare and derived from some of the theories of Gilles Deleuze. (Manuel Delanda describes this theory in A New Philosophy of Society: Assemblage Theory And Social Complexity.)

Each of these ideas gives expression to the important truth of the heterogeneity principle: that social outcomes are the aggregate result of a number of lower-level processes and institutions that give rise to them, and that social outcomes are contingent results of interaction and concatenation of these lower-level processes.