George and Bennett on case study methodology

 

Establishing causal relationships within the fabric of the social world is more challenging than in the biological or physical-chemical domains. The reasons for this difficulty are familiar — the high degree of contextuality and contingency that is characteristic of social change, the non-deterministic character of social causation, and the fact that most social outcomes are the result of unforeseen conjunctions of independent influences, to name several.

Alexander George and Andrew Bennett argue for the value of a case-study method of social research in Case Studies and Theory Development in the Social Sciences. The idea here is that social researchers can learn about the causation of particular events and sequences by examining them in detail and in comparison with carefully selected alternative examples.

Here is how they describe the case-study method:

The method and logic of structured, focused comparison is simple and straightforward. The method is “structured” in that the researcher writes general questions that reflect the research objective and that these questions are asked of each case under study to guide and standardize data collection, thereby making systematic comparison and cumulation of the findings of the cases possible. The method is “focused” in that it deals only with certain aspects of the historical cases examined. The requirements for structure and focus apply equally to individual cases since they may later be joined by additional cases. (67)

George and Bennett believe that the techniques and heuristics of the case study approach permit the researcher to arrive at rigorous and differentiated hypotheses about underlying social processes. In particular, they believe that the method of process-tracing has substantial power in social research, permitting the researcher to move from the details of a particular historical case to more general hypotheses about causal mechanisms and processes in other contexts as well (6). They discourage research strategies based on the covering-law model, in which researchers would seek out high-level generalizations about social events and outcomes: “highly general and abstract theories … are too general to make sharp theoretical predictions or to guide policy” (7). But they also note the limits of policy relevance of “independent, stable causal mechanisms” (7), because social mechanisms interact in context-dependent ways that are difficult or impossible to anticipate. It is therefore difficult to design policy interventions based on knowledge of a few relevant and operative mechanisms within the domain of behavior the policy is expected to govern, since the workings of the mechanisms in concrete circumstances are difficult to project.

Fundamentally they align with the causal mechanisms approach to social explanation. Here is how they define a causal mechanism:

We define causal mechanisms as ultimately unobservable physical, social, or psychological processes through which agents with causal capacities operate, but only in specific contexts or conditions, to transfer energy, information, or matter to other entities. In so doing, the causal agent changes the affected entity’s characteristics, capacities, or propensities in ways that press until subsequent causal mechanisms act upon it. (137)

And they believe that the case-study method is a suite of methodological approaches that permit identification and exploration of underlying causal mechanisms.

The case study approach – the detailed examination of an aspect of a historical episode to develop or test historical explanations that may be generalizable to other events – has come in and out of favor over the past five decades as researchers have explored the possibilities of statistical methods … and formal models. (5)

The case study method is designed to identify causal connections within a domain of social phenomena.

Scientific realists who have emphasized that explanation requires not merely correlational data, but also knowledge of intervening causal mechanisms, have not yet had much to say on methods for generating such knowledge. The method of process-tracing is relevant for generating and analyzing data on the causal mechanisms, or processes, events, actions, expectations, and other intervening variables, that link putative causes to observed effects. (214)

How is that to be accomplished? The most important tool that George and Bennett describe is the method of process tracing. "The process-tracing method attempts to identify the intervening causal process--the causal chain and causal mechanism--between an independent variable (or variables) and the outcome of the dependent variable" (206). Process tracing requires the researcher to examine linkages within the details of the case they are studying, and then to assess specific hypotheses about how these links might be causally mediated. 

Suppose we are interested in a period of violent mobilization VM in the countryside at time t, and we observe a marked upswing of religious participation RP in the villages where we have observations. We might hypothesize that the surge of religious participation contributed causally to the political mobilization that ensued. But a process-tracing methodology requires that we we consider as full a range of alternative possibilities as we can: that both religious and political activism were the joint effect of some other social process; that religious participation was caused by political mobilization rather than caused that mobilization; that the two processes were just contingent and unrelated simultaneous developments. What can we discover within the facts of the case that would allow us to disentangle these various causal possibilities? If RP was the cause of VM, there should be traces of the influence that VM exerted within the historical record -- priests who show up in the interrogation cells, organizational linkages that are uncovered through archival documents, and the like. This is the work of process tracing in the particular case. And I agree with George and Bennett that there is often ample empirical evidence available in the historical record to permit this kind of discovery.

Finally, George and Bennett believe that process-tracing can occur at a variety of levels:

The simplest variety of process-tracing takes the form of a detailed narrative or story presented in the form of a chronicle that purports to throw light on how an event came about.... A substantially different variety of process-tracing converts a historical narrative into an analytical causal explanation couched in explicit theoretical forms.... In another variety of process-tracing, the investigator constructs a general explanation rather than a detailed tracing of a causal process. (210-211)

One of the strengths of the book is an appendix presenting a very good collection of research studies that illustrate the case study methodology that they explore. There are examples from American politics, comparative politics, and international relations. These examples are very helpful because they give substance to the methodological ideas presented in the main body of the book.

Geddes on methods

Earlier posts have examined some recent thinking about social science methods (link, link). Here I will examine another recent contributor to this field, Barbara Geddes.

Geddes is a specialist in comparative politics, and her 2003 Paradigms and Sand Castles: Theory Building and Research Design in Comparative Politics is a thoughtful contribution to the debate about how the social sciences should proceed. Her central concern is with the topic of research design in comparative politics. How should a comparative researcher go about attempting to explain the varying outcomes we observe within the experiences of otherwise similar countries? How can we gain empirical grounds for validating or rejecting causal hypotheses in this field? And how do general theories of politics fare as a basis for explaining these concrete trajectories -- the rise of authoritarianism in one country, the collapse of communism in the USSR, an outbreak of democracy in that country, or a surprising populism in another? Geddes finds that the theories that guided comparative politics in the sixties, seventies, and eighties proved to be inadequate to the task of explaining the twists and turns the political systems of the world took during those decades and argues that the discipline needs to do better.

Geddes's proposed solution to this cul de sac is to bring theory and research design closer together. She wants to find a way of pursuing research in comparative politics that permits for more accumulation of knowledge in the field, both on the side of substantial empirical findings and well grounded theoretical premises. Theoretical premises need to be more carefully articulated, and plans for data collection need to be more purposefully guided so the resulting empirical findings are well suited to evaluating and probing the theoretical premises. Here is a good summary paragraph of her view:

The central message of this book is that we could steer a course through that narrow channel between untested theory and atheoretical data more successfully, and thus accumulate theoretical knowledge more rapidly, if certain research norms were changed. Although research norms are changing, basic principles of research design continue to be ignored in many studies. Common problems include inappropriate selection of cases from which to draw evidence for testing theories and a casual attitude towards nonquantitative measurement, both of which undermine the credibility of evidence gathered to support arguments. The failure to organize and store evidence in ways that make it accessible to others raises the cost of replication and that also slows theoretical progress. Uncritical acceptance by readers of theories that have not undergone systematic empirical test exacerbates the problem. (5)

What does Geddes mean by "theory" in this context? Her examples suggest that she thinks of a theory as a collection of somewhat independent causal hypotheses about a certain kind of large social outcome -- the emergence of democracy or the occurrence of sustained economic development, for example. So when she discusses the validity of modernization theory, she claims that some components were extensively tested and have held up (the correlation between democracy and economic development, for example; 9), whereas other components were not adequately tested and have not survived (the claim that the diffusion of values would rapidly transform traditional societies; 9).

Geddes does not explicitly associate her view of social science inquiry with the causal mechanisms approach. But in fact the intellectual process of inquiry that she describes has a great deal in common with that approach. On her view of theory, the theory comes down to a conjunction of causal hypotheses, each of which can in principle be tested in isolation. What she refers to as “models” could as easily be understood as schematic descriptions of common social mechanisms (33). The examples she gives of models are collective action problems and evolutionary selection of social characteristics; and each of these is a mechanism of social causation.

She emphasizes, moreover, that the social causal factors that are at work in the processes of political and economic development generally work in conjunction with each other, with often unpredictable consequences.

Large-scale phenomena such as democratic breakdown, economic development, democratization, economic liberalization, and revolution result from the convergence of a number of different processes, some of which occur independently from others. No simple theory is likely to explain such compound outcomes.  Instead of trying to "explain" such compound outcomes as wholes, I suggest a focus on the various processes that contribute to the final outcome, with the idea of theorizing these processes individually. (27)

What Geddes's conception of "theory" seems to amount to is more easily formulated in the language of causal mechanisms. We want to explain social outcomes at a variety of levels of scale -- micro, meso, macro. We understand that explanation requires discovery of the causal pathways and processes through which the outcome emerged. We recognize that social outcomes have a great deal of contingency and path dependency, so it is unlikely that a great outcome like democratization will be the result of a single pervasive causal factor. Instead, we look for mid-level causal mechanisms that are in place in the circumstances of interest -- say the outbreak of the Bolshevik uprising; and we attempt to discern the multiple causal factors that converged in these historical circumstances to bring about the outcome of interest. The components of theories to which Geddes refers are accounts of reasonably independent causal mechanisms and processes, and they combine in contingent and historically specific ways.

And in fact she sometimes adopts this language of independent mid-level causal mechanisms:

To show exactly what I mean, in the pages that follow I develop a concrete research strategy that begins with the disaggregation of the big question — why democratization occurs — into a series of more researchable questions about mechanisms. The second step is a theorization of the specific process chosen for study — in this case, the internal authoritarian politics that sometimes lead to transition. The third step is the articulation of testable implications derived from the theorization. (43)

And later:

I argued that greater progress could be made toward actually understanding how such outcomes [as democratization and authoritarian rule] by examining the mechanisms and processes that contribute to them, rather than through inductive searches for the correlates of the undifferentiated whole. (87)

(This parallels exactly the view taken by McAdam, Tarrow, and Tilly in Dynamics of Contention, where they argue systematically for a form of analysis of episodes of contention that attempts to identify recurring underlying processes and mechanisms.)

It emerges that what Geddes has in mind for testing mid-level causal hypotheses is largely quantitative: isolate a set of cases in which the outcome is present and examine whether the hypothesized causal factor varies appropriately across the cases. Do military regimes in fact persist with shorter average duration than civilian authoritarian regimes (78)? Like King, Keohane, and Verba in Designing Social Inquiry: Scientific Inference in Qualitative Research, Geddes is skeptical about causal methods based on comparison of a small number of cases; and like KKV, she is critical of Skocpol's use in States and Social Revolutions: A Comparative Analysis of France, Russia and China of Mill's methods in examining the handful of cases of social revolution that she examines. This dismissal of small-N research represents an unwelcome commitment to methodological monism, in my view.

In short, I find Geddes's book to be a useful contribution that aligns more closely than it appears with the causal mechanisms approach to social research. It is possible to paraphrase Geddes's approach to theory and explanation in the language of causal mechanisms, emphasizing meso-level analysis, conjunctural causation, and macro-level contingency. (More on this view of historical causation can be found here.)

Geddes's recommendations about how to probe and test the disaggregated causal hypotheses at which the researcher arrives represent one legitimate approach to the problem of giving greater empirical content to specific hypotheses about causal mechanisms. It is regrettable, however, that Geddes places her flag on the quantitative credo for the social sciences. One of the real advantages of the social mechanisms approach is precisely that we can gain empirical knowledge about concrete social mechanisms through detailed case studies, process tracing, and small-N comparisons of cases that is not visible at the level of higher-level statistical regularities. (A subsequent post will examine George and Bennett, Case Studies and Theory Development in the Social Sciences (Belfer Center Studies in International Security), for an alternative view of how to gain empirical knowledge of social processes and mechanisms.)

Social mechanisms and ABM methods

One particularly appealing aspect of agent-based models is the role they can play in demonstrating the inner workings of a major class of social mechanisms, the group we might refer to as mechanisms of aggregation. An ABM is designed to work out how a field of actors of a certain description, in specified kinds of interaction, lead through time to a certain kind of aggregate effect. This class of mechanisms corresponds to the upward strut of Coleman's boat. This is certainly a causal story; it is a generative answer to the question, how does it work?

However, anyone who thinks carefully about causation will realize that there are causal sequences that occur only once. Consider this scenario: X occurs, conditions Ci take place in a chronological sequence, and Y is the result. So X caused Y through the causal steps instigated by Ci. We wouldn't want to say the complex of interactions and causal links associated with the progress of the system through Ci as a mechanism linking X to Y; rather, this ensemble is the particular (in this case unique) causal pathway from X to Y. But when we think about mechanisms, we generally have in mind the idea of "recurring causal linkages", not simply a true story about how X caused Y in these particular circumstances. In other words, for a causal story to represent a mechanism, it needs to be a story that can be found to hold in an indefinite number of cases. Mechanisms are recurring complexes of causal sequences.

An agent-based model serves to demonstrate how a set of actors give rise to a certain aggregate outcome. This is plainly a species of causal argument. But it is possible to apply ABM methods to circumstances that are unique and singular. This kind of ABM model lacks an important feature generally included in the definition of a mechanism-- the idea of recurrence across a number of cases. So we might single out for special attention those ABMs that identify and analyze processes that recur across multiple social settings. Here we might refer, for example, to the "Schelling mechanism" of residential segregation. There are certainly other unrelated mechanisms associated with urban segregation -- mortgage lending practices or real estate steering practices, for example. But the Schelling mechanism is one contributing factor in a range of empirical and historical cases. And it is a factor that works through the local preferences of individual actors.

So this seems to answer one important question: in what ways can ABM simulations be said to describe social mechanisms? They do so when (i) they describe an aggregative process through which a given meso-level outcome arises, and (ii) the sequence they describe can be said to recur in multiple instances of social process.

A question that naturally arises here is whether there are social mechanisms that fall outside this group. Are there social mechanisms that could not be represented by an ABM model? Or would we want to say that mechanisms are necessarily aggregative, so all mechanisms should be amenable to representation by an ABM?

This is a complicated question. One possible response seems easily refuted: there are mechanisms that work from meso level (organizations) to macro level (rise of fascism) that do not invoke the features of individual actors. Therefore there are mechanisms that do not conform strictly to the requirements of methodological individualism. However, there is nothing in the ABM methodology that requires that the actors should be biological individuals. Certainly it is possible to design an ABM representing the results of competition among firms with different behavioral characteristics. This example still involves an aggregative construction, a generation of the macro behavior on the basis of careful specification of the behavioral characteristics of the units.

Another possible candidate for mechanisms not amenable to ABM analysis might include the use of network analysis to incorporate knowledge-diffusion characteristics into analysis of civil unrest and other kinds of social change. It is sometimes argued that there are structural features of networks that are independent of actor characteristics and choices. But given that ABM theorists often incorporate aspects of network theory into their formal representations of a social process, it is hard to maintain that facts about networks cannot be incorporated into ABM methods.

Another candidate is what Chuck Tilly and pragmatist sociologists (Gross, Abbott, Joas) refer to as the "relational characteristics" of a social situation. Abbott puts the point this way: often a social outcome isn't the result of an ensemble of individuals making discrete choices, but rather is a dance of interaction in which each individual's moves both inform and self-inform later stages of the interaction. This line of thought seems effective as a rebuttal to methodological individualism, or perhaps even analytical sociology, but I don't think it demonstrates a limitation of the applicability of ABM modeling. ABM methods are agnostic about the nature of the actors and their interactions. So it is fully possible for an ABM theorist to attempt to produce a representation of the iterative process just described; or to begin the analysis with an abstraction of the resultant behavioral characteristics found in the group.

I've argued here that it is legitimate to postulate meso-to-meso causal mechanisms. Meso-level things can have causal powers that allow them to play a role in causal stories about social outcomes. I continue to believe that is so. But considerations brought forward here make me think that even in cases where a theorist singles out a meso-meso causal mechanism, he or she is still offering some variety of disaggregative analysis of the item to be explained. It seems that providing a mechanism is always a process of delving below the level of the explananda to uncover the underlying processes and causal powers that bring it about.

So the considerations raised here seem to lead to a strong conclusion -- that all social mechanisms can be represented within the framework of an ABM (stipulating that ABM methods are agnostic about the kinds of agents they postulate). Agent-based models are to social processes as molecular biology is to the workings of the cell.

In fact, we might say that ABM methods simply provide a syntax for constructing social explanations: to explain a phenomenon, identify some of the constituents of the phenomenon, arrive at specifications of the properties of those constituents, and demonstrate how the behavior of the constituents aggregates to the phenomenon in question.

(It needs to be recognized that identifying agent-based social mechanisms isn't the sole use of ABM models, of course. Other uses include prediction of the future behavior of a complex system, "what if" experimentation, and data-informed explanations of complex social outcomes. But these methods certainly constitute a particularly clear and rigorous way of specifying the mechanism that underlies some kinds of social processes.)

Emergentism and generationism

media: lecture by Stanford Professor Robert Sapolsky on chaos and reduction

 

Several recent posts have focused on the topic of simulations in the social sciences. An interesting question here is whether these simulation models shed light on the questions of emergence and reduction that frequently arise in the philosophy of the social sciences. In most cases the models I've mentioned are "aggregation" models, in which the simulation attempts to capture the chief dynamics and interaction effects of the units and then work out the behavior and evolution of the ensemble. This is visibly clear when it comes to agent-based models. However, some of the scholars whose work I admire are "complexity" theorists, and a common view within complexity studies is the idea that the system has properties that are difficult or impossible to derive from the features of the units.

So does this body of work give weight to the idea of emergence, or does it incline us more in the direction of supervenience and ontological unit-ism?

John Miller and Scott Page provide an accessible framework within which to consider these kinds of problems in Complex Adaptive Systems: An Introduction to Computational Models of Social Life. They look at certain kinds of social phenomena as constituting what they call "complex adaptive systems," and they try to demonstrate how some of the computational tools developed in the sciences of complex systems can be deployed to analyze and explain complex social outcomes. Here is how they characterize the key concepts:

Adaptive social systems are composed of interacting, thoughtful (but perhaps not brilliant) agents. (kl 151)

Page and Miller believe that social phenomena often display "emergence" in a way that we can make sense of. Here is the umbrella notion they begin with:

The usual notion put forth underlying emergence is that individual, localized behavior aggregates into global behavior that is, in some sense, disconnected from its origins. Such a disconnection implies that, within limits, the details of the local behavior do not matter to the aggregate outcome. (kl 826)

And they believe that the notion of emergence has "deep intuitive appeal". They find emergence to be applicable at several levels of description, including "disorganized complexity" (the central limit theorem, the law of large numbers) and "organized complexity" (the behavior of sand piles when grains have a small amount of control).

Under organized complexity, the relationships among the agents are such that through various feedbacks and structural contingencies, agent variations no longer cancel one another out but, rather, become reinforcing. In such a world, we leave the realm of the Law of Large Numbers and instead embark down paths unknown. While we have ample evidence, both empirical and experimental, that under organized complexity, systems can exhibit aggregate properties that are not directly tied to agent details, a sound theoretical foothold from which to leverage this observation is only now being constructed. (kl 976)

Organized complexity, in their view, is a substantive and important kind of emergence in social systems, and this concept plays a key role in their view of complex adaptive systems.

Another -- and contrarian -- contribution to this field is provided by Joshua Epstein. His three-volume work on agent-based models is a fundamental text book for the field. Here are the titles:

Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science
Growing Artificial Societies: Social Science From the Bottom Up
Generative Social Science: Studies in Agent-Based Computational Modeling

Chapter 1 of Generative Social Science provides an overview of Epstein's approach is provided in "Agent-based Computational Models and Generative Social Science", and this is a superb place to begin (link). Here is how Epstein defines generativity:

Agent-based models provide computational demonstrations that a given microspecification is in fact sufficient to generate a macrostructure of interest.... Rather, the generativist wants an account of the configuration's attainment by a decentralized system of heterogeneous autonomous agents. Thus, the motto of generative social science, if you will, is: If you didn't grow it, you didn't explain its emergence. (42)

Epstein describes an extensive attempt to model a historical population using agent-based modeling techniques, the Artificial Anasazi project (link). This work is presented in Dean, Gumerman, Epstein, Axtell, Swedlund, McCarroll, and Parker, "Understanding Anasazi Culture Change through Agent-Based Modeling" in Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes. The model takes a time series of fundamental environmental, climate, and agricultural data as given, and he and his team attempt to reconstruct (generate) the pattern of habitation that would result. Here is the finding they arrive at:

Generativity seems to be directly incompatible with the idea of emergence, and in fact Epstein takes pains to cast doubt on that idea.

I have always been uncomfortable with the vagueness--and occasional mysticism--surrounding this word and, accordingly, tried to define it quite narrowly.... There, we defined "emergent phenomena" to be simply "stable macroscopic patterns arising from local interaction of agents." (53)

So Epstein and Page both make use of the methods of agent based modeling, but they disagree about the idea of emergence. Page believes that complex adaptive systems give rise to properties that are emergent and irreducible; whereas Epstein doesn't think the idea makes a lot of sense. Rather, Epstein's view depends on the idea that we can reproduce (generate) the macro phenomena based on a model involving the agents and their interactions. Macro phenomena are generated by the interactions of the units; whereas for Page and Miller, macro phenomena in some systems have properties that cannot be easily derived from the activities of the units.

At the moment, anyway, I find myself attracted to Herbert Simon's effort to split the difference by referring to "weak emergence" (link):

... reductionism in principle even though it is not easy (often not even computationally feasible) to infer rigorously the properties of the whole from knowledge of the properties of the parts. In this pragmatic way, we can build nearly independent theories for each successive level of complexity, but at the same time, build bridging theories that show how each higher level can be accounted for in terms of the elements and relations of the next level down. (Sciences of the Artificial 3rd edition 172)

This view emphasizes the computational and epistemic limits that sometimes preclude generating the phenomena in question -- for example, the problems raised by non-linear causal relations and causal interdependence. Many observers have noted that the behavior of tightly linked causal systems may be impossible to predict, even when we are confident that the system outcomes are the result of "nothing but" the interactions of the units and sub-systems.

Verisimilitude in models and simulations

Modeling always requires abstraction and simplification. We need to arrive at a system for representing the components of a system, the laws of action that describe their evolution and interaction, and a way of aggregating the results of the representation of the components and their interactions. Simplifications are required in order to permit us to arrive at computationally feasible representations of the reality in question; but deciding which simplifications are legitimate is a deeply pragmatic and contextual question. Ignoring air resistance is a reasonable simplification when we are modeling the trajectories of dense, massive projectiles through the atmosphere; it is wholly unreasonable if we are interested in modeling the fall of a leaf or a feather under the influence of gravity (link).

Modeling the social world is particularly challenging for a number of reasons. Not all social actors are the same; actors interact with each other in ways that are difficult to represent formally; and actors change their propensities for behavior as a result of their interactions. They learn, adapt, and reconfigure; they acquire new preferences and new ways of weighing their circumstances; and they sometimes change the frames within which they deliberate and choose.

Modeling the social world certainly requires the use of simplifying assumptions. There is no such thing as what we might call a Borges-class model -- one that represents every feature of the terrain. This means that the scientist needs to balance realism, tractability, and empirical adequacy in arriving at a set of assumptions about the actor and the environment, both natural and social. These judgments are influenced by several factors, including the explanatory and theoretical goals of the analysis. Is the analysis intended to serve as an empirical representation of an actual domain of social action -- the effects on habitat of the grazing strategies of a vast number of independent herders, say? Or is it intended to isolate the central tendency of a few key factors -- short term cost-benefit analysis in a context of a limited horizon of environmental opportunities, say?

If the goal of the simulation is to provide an empirically adequate reconstruction of the complex social situation, permitting adjustment of parameters in order to answer "what-if" questions, then it is reasonable to expect that the baseline model needs to be fairly detailed. We need to build in enough realism about the intentions and modes of reasoning of the actors, and we need a fair amount of detail concerning the natural, social, and policy environments in which they choose.

The discipline of economic geography provides good examples of both extremes of abstraction and realism of assumptions. At one extreme we have the work of von Thunen in his treatment of the Isolated State, producing a model of habitation, agriculture, and urbanization that reflects the economic rationality of the actors.

At the other extreme we have calibrated agent-based models of land use that build in more differentiated assumptions about the intentions of the actors and the legal and natural environment in which they make their plans and decisions. A very good and up-to-date volume dedicated to the application of calibrated agent-based models in economic geography is Alison Heppenstall, Andrew Crooks, Linda See, and Michael Batty, Agent-Based Models of Geographical Systems. The contribution by Crooks and Heppenstall provides an especially good introduction to the approach ("Introduction to Agent-Based Modelling"). Crook and Heppenstall describe the distinguishing features of the approach in these terms:

To understand geographical problems such as sprawl, congestion and segregation, researchers have begun to focus on bottom-up approaches to simulating human systems, specifically researching the reasoning on which individual decisions are made. One such approach is agent-based modelling (ABM) which allows one to simulate the individual actions of diverse agents, and to measure the resulting system behaviour and outcomes over time. The distinction between these new approaches and the more aggregate, static conceptions and representations that they seek to complement, if not replace, is that they facilitate the exploration of system processes at the level of their constituent elements. (86)

The volume also pays a good deal of attention to the problem of validation and testing of simulations. Here is how Manson, Sun, and Bonsal approach the problem of validation of ABMs in their contribution, "Agent-Based Modeling and Complexity":

Agent-based complexity models require careful and thorough evaluation, which is comprised of calibration, verification, and validation (Manson 2003 ) . Calibration is the adjustment of model parameters and specifications to fit certain theories or actual data. Verification determines whether the model runs in accordance with design and intention, as ABMs rely on computer code susceptible to programming errors. Model verification is usually carried out by running the model with simulated data and with sensitivity testing to determine if output data are in line with expectations. Validation involves comparing model outputs with real-world situations or the results of other models, often via statistical and geovisualization analysis. Model evaluation has more recently included the challenge of handling enormous data sets, both for the incorporation of empirical data and the production of simulation data. Modelers must also deal with questions concerning the relationship between pattern and process at all stages of calibration, verification, and validation. Ngo and See ( 2012 ) discuss these stages in ABM development in more detail. (125)

An interesting current illustration of the value of agent-based modeling in analysis and explanation of historical data is presented by Kenneth Sylvester, Daniel Brown, Susan Leonard, Emily Merchant, and Meghan Hutchins in "Exploring agent-level calculations of risk and return in relation to observed land-use changes in the US Great Plains, 1870-1940" (link). Their goal is to see whether it is possible to reproduce important features of land use in several Kansas counties by making specific assumptions about decision-making by the farmers, and specific information about the changing weather and policy circumstances within which choices were made. 

 

Here is how Sylvester and co-authors describe the problem of formulating a representation of the actors in their simulation:

Understanding the processes by which farming households made their land-use decisions is challenging because of the complexity of interactions between people and the places in which they lived and worked, and the often insufficient resolution of observed information. Complexity characterizes land-use processes because observed historical behaviors often represent accumulated decisions of heterogeneous actors who were affected by a wide range of environmental and human factors, and by specific social and spatial interactions. (1)

Here is a graph of the results of the Sylvester et al agent-based model, simulating the allocation of crop land across five different crops given empirical weather and rainfall data.

So how well does this calibrated agent-based model do as a simulation of the observed land use patterns? Not particularly well, in the authors' concluding remarks; their key finding is sobering:

Our base model, assuming profit maximization as the motive for land-use decision making, reproduced the historical record rather poorly in terms of both land use shares and farm size distributions in each township. We attribute the differences to deviations in decision making from profit-maximizing behavior. Each of the subsequent experiments illustrates how relatively simple changes in micro-level processes lead to different aggregate outcomes. With only minor adjustments to simple mechanisms, the pace, timing, and trajectories of land use can be dramatically altered.

However, they argue that this lack of fit does not discredit the ABM approach, but rather disconfirms the behavioral assumption that farmers are simple maximizers of earning. They argue, as sociologists would likely agree, that "trajectories of land-use depended not just on economic returns, but other slow processes of change, demographic, cultural, and ecological feedbacks, which shaped the decisions of farmers before and long after the middle of the twentieth century." And therefore it is necessary to provide more nuanced representations of actor intentionality if the model is to do a good job of reproducing the historical results and the medium-term behavior of the system.

 

(In an earlier post I discussed a set of formal features that have been used to assess the adequacy of formal models in economics and other mathematized social sciences (link). These criteria are discussed more fully in On the Reliability of Economic Models: Essays in the Philosophy of Economics.)

(Above I mentioned the whimsical idea of "Borges-class models" -- the unrealizable ideal of a model that reproduces every aspect of the phenomena that it seeks to simulate. Here is the relevant quotation from Jorge Borges.

On Exactitude in Science
Jorge Luis Borges, Collected Fictions, translated by Andrew Hurley.

…In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that that vast Map was Useless, and not without some Pitilessness was it, that they delivered it up to the Inclemencies of Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the Disciplines of Geography.

—Borges quoting Suarez Miranda,Viajes devarones prudentes, Libro IV,Cap. XLV, Lerida, 1658)

Computational models for social phenomena

There is a very lively body of work emerging in the intersection between computational mathematics and various fields of the social sciences. This emerging synergy between advanced computational mathematics and the social sciences is possible, in part, because of the way that social phenomena emerge from the actions and thoughts of individual actors in relationship to each other. This is what allows us to join mathematics to methodology and explanation. Essentially we can think of the upward strut of Coleman’s boat — the part of the story that has to do with the “aggregation dynamics” of a set of actors — and can try to create models that can serve to simulate the effects of these actions and interactions.

source: Hedstrom and Ylikoski (2010) "Causal Mechanisms in the Social Sciences" (link)

 

Here is an interesting example in the form of a research paper by Rahul Narain and colleagues on the topic of modeling crowd behavior ("Aggregate Dynamics for Dense Crowd Simulation", link). Here is their abstract:

Large dense crowds show aggregate behavior with reduced individual freedom of movement. We present a novel, scalable approach for simulating such crowds, using a dual representation both as discrete agents and as a single continuous system. In the continuous setting, we introduce a novel variational constraint called unilateral incompressibility, to model the large-scale behavior of the crowd, and accelerate inter-agent collision avoidance in dense scenarios. This approach makes it possible to simulate very large, dense crowds composed of up to a hundred thousand agents at near- interactive rates on desktop computers.

Federico Bianchi takes up this intersection between computational mathematics and social behavior in a useful short paper called "From Micro to Macro and Back Again: Agent-based Models for Sociology" (link). His paper focuses on one class of computational models, the domain of agent-based models. Here is how he describes this group of approaches to social explanation:

An Agent-Based Model (ABM) is a computational method which enables to study a social phenomenon by representing a set of agents acting upon micro-level behavioural rules and interacting within environmental macro-level (spatial, structural, or institutional) constraints. Agent-Based Social Simulation (ABSS) gives social scientists the possibility to test formal models of social phenomena, generating a virtual representation of the model in silico through computer programming, simulating its systemic evolution over time and comparing it with the observed empirical phenomenon. (1) 

 And here is how he characterizes the role of what I called "aggregation dynamics" above:

Solving the complexity by dissecting the macro-level facts to its micro-level components and reconstructing the mechanism through which interacting actors produce a macro-level social outcome. In other words, reconstructing the micro-macro link from interacting actors to supervenient macrosociological facts. (2)

Or in other words, the task of analysis is to provide a testable model that can account for the way the behaviors and interactions at the individual level can aggregate to the observed patterns at the macro level.

Another more extensive example of work in this area is Gianluca Manzo, Analytical Sociology: Actions and Networks. Manzo's volume proceeds from the perspective of analytical sociology and agent-based models. Manzo provides a very useful introduction to the approach, and Peter Hedstrom and Petri Ylikoski extend the introduction to the field with a chapter examining the role of rational-choice theory within this approach. The remainder of the volume takes the form of essays by more than a dozen sociologists who have used the approach to probe and explain specific kinds of social phenomena.

Manzo provides an account of explanation that highlights the importance of "generating" the phenomena to be explained. Here are several principles of methodology on this topic:

• P4: in order to formulate the "generative model," provide a realistic description of the relevant micro-level entities (P4a) and activities (P4b) assumed to be at work, as well as of the structural interdependencies (P4c) in which these entities are embedded and their  activities unfold;
• P5: in order rigorously to assess the internal consistency of the "generative model" and to determine its high-level consequences, translate the "generative model" into an agent-based computational model;
• P6: in order to assess the generative sufficiency of the mechanisms postulated, compare the agent-based computational model's high-level consequences with the empirical description of the facts to be explained (9)

So agent-based modeling simulations are a crucial part of Manzo's understanding of the logic of analytical sociology. As agent-based modelers sometimes put the point, "you haven't explained a phenomenon until you've shown how it works on the basis of a detailed ABM." But the ABM is not the sole focus of sociological research, on Manzo's approach. Rather, Manzo points out that there are distinct sets of questions that need to be investigated: how do the actors make their choices? What are the structural constraints within which the actors exist? What kinds of interactions and relations exist among the actors? Answers to all these kinds of question are needed if we are to be able to design realistic and illuminating agent-based models of concrete phenomena.

Here is Manzo's summary table of the research cycle (8). And he suggests that each segment of this representation warrants a specific kind of analysis and simulation.

This elaborate diagram indicates that there are different locations within a complex social phenomenon where different kinds of analysis and models are needed. (In this respect the approach Manzo presents parallels the idea of structuring research methodology around the zones of activity singled out by the idea of methodological localism; link.) This is methodologically useful, because it emphasizes to the researcher that there are quite a few different kinds of questions that need to be addressed in order to successfully explain a give domain of phenomena.

The content-specific essays in the volume focus on one or another of the elements of this description of methodology. For example, Per-Olof Wikstrom offers a "situational action theory" account of criminal behavior; this definition of research focuses on the "Logics of Action" principle 4b.

People commit acts of crime because they perceive and choose (habitually or after some deliberation) a particular kind of act of crime as an action alternative in response to a specific motivation (a temptation or a provocation). People are the source of their actions but the causes of their actions are situational. (75)

SAT proposes that people with a weak law-relevant personal morality and weak ability to exercise self-control are more likely to engage in acts of crime because they are more likely to see and choose crime as an option. (87)

Wikstrom attempts to apply these ideas by using a causal model to reproduce crime hotspots based on situational factors (90).

The contribution of Gonzalez-Bailon et al, "Online networks and the diffusion of protest," focuses on the "Structural Interdependency" principle 4c.

One of the programmatic aims of analytical sociology is to uncover the individual-level mechanisms that generate aggregated patterns of behaviour.... The connection between these two levels of analysis, often referred to as the micro-macro link, is characterised by the complexity and nonlinearity that arises from interdependence; that is, from the influence that actors exert on each other when taking a course of action. (263)

Their contribution attempts to provide a basis for capturing the processes of diffusion that are common to a wide variety of types of social behavior, based on formal analysis of interpersonal networks.

Networks play a key role in diffusion processes because they facilitate threshold activation at the local level. Individual actors are not always able to monitor accurate the behavior of everyone else (as global thresholds assume) or they might be more responsive to a small group of people, represented in their personal networks. (271)

They demonstrate that the structure of the local network matters for the diffusion of an action and the activation of individual actors.

In short, Analytical Sociology: Actions and Networks illustrates a number of points of intersection between computational mathematics, simulation systems, and concrete sociological research. This is a very useful effort as social scientists attempt to bring more complex modeling tools to bear on concrete social phenomena.

Varieties of social methodology

What are the frameworks that generally come to mind in discussions of methodology in the social sciences? Several families of methodological frameworks are indicated in the diagram above. These are deliberately presented as a wheel, with no sense of priority among them.

(A) Quantitative methodology -- what Andrew Abbott refers to as the variables paradigm. This is the approach that analyzes the social world as a set of individuals, groups, and properties, and simply sorts through to find correlations, associations, and possible causal relationships using a range of statistical tools. This is an inductivist approach. In this approach, the role of the social sciences is to accurately observe the facts and to build up systems of regularities among them. This seems like an assumption-free framework, but there is an underlying ontology here no less than the other frameworks mentioned here -- the idea that the the social world is governed by some system of underlying laws or regularities.

(B) Interpretive methods. Clifford Geertz recommends an approach to social research within a generally interpretive worldview. He maintains that the most important feature of the social world is the fact of meaning. He urges us to consider as most important the meanings individuals and groups attach to behaviors and performances. So the research task is to reconstruct those meanings by observing and interacting with the social actors in a particular setting. The observer should observe the patterns of action and interaction he/she finds and carefully investigate the patterns of meaning the participants weave around their worlds.

A related framework is ethnomethodology, the approach taken by qualitative sociologists like Goffman and Garfinkel. This is the idea that one important function of the social sciences is to figure out the underlying grammar of the assumptions and rules that individuals are following as they interact with each other. The ontological assumption here is that individuals are basic in the social world, and individuals are complex. On this approach the methodology is to observe ordinary behavior and try to discover the underlying rules and expectations that indicate something like a grammar or normative frame that drives or generates interpersonal behavior.

(C) A family of approaches we might call realist methodology. These approaches begin with the premise that the social world consists of certain kinds of entities, forces, and processes, and then guides the researcher to attempt to discover the characteristics of those structures. This is a process of hypothesis formation and theory development and of testing out theories -- large or small -- of things like class, charisma, or bureaucratic state apparatus.

(D) A number of methods of analysis developed in the comparative social sciences, including causal methods associated with comparative historical sociology. That includes the methods of paired comparisons, Mill's methods, and methods of similarity and difference.  The researcher attempts to work out which factors are necessary or sufficient, enhancing or inhibiting. We can call this comparative methodology.

(E) Causal mechanisms methodology. This framework is a variety of realist methodology. On this approach we work on the assumption that there are social causes and that causes take the form of concrete causal mechanisms. The task of research is to gain enough empirical detail about selected cases to be able to piece together assumptions about the mechanisms at work. Ideas associated with the notion of process tracing have a natural fit here.

(F) Methods emphasizing techniques of formal modeling. This methodology is especially prominent in political science and economics. Here the goal of the research is to arrive at elegant, simple mathematical models of the phenomena. On this approach the evaluation of the model is not so much empirical but rather mathematical and formal. This approach is commonly faulted exactly because it is not sufficiently responsive to empirical constraints do standards. Its empirical relevance is not so clear. If we believe the social sciences are empirical then a formal model that is a valued for its abstract elegance is unsatisfactory.   It needs to contribute to an understanding of real empirical phenomena. At the least this means that we should be able to tie the model to some real behavioral characteristics. In the best case -- for example, with ABMs or CGE models-- we should be able to begin to reproduce important features of real empirical cases by calibrating the model to empirical circumstances.

(G) There are two other aspects of methodology that need to be called out. One has to do with the methods of data collection which are recommended, which differ substantially from domain to domain. The other is methods of empirical evaluation of the theories we advance. Social sciences differ substantially in both these ways -- what kind of data is needed, how it should be collected (e.g. survey methodology), and how we should validate the results. These are often discipline-specific and substantially more concrete than prescriptions in the philosophy of science. The ways in which we should evaluate a social science construction also varies significantly by national research tradition. Gabriel Abend points out that schemes of evaluation vary substantially across the sociological traditions of mexico and the United States in terms of the standards in play about what constitutes rigor,empirical argument and theoretical argument."

Each of these is a methodology in the loose sense I favor.  It is a guide for the researcher, indicating what kinds of factors he or she should be looking for by postulating a social ontology; an indication of what an explanatory account ought to look like, and an indication of what counts as warrant for such explanations.

We might observe that if we favor a pluralist social ontology, according to which there are properties individuals, relationships, structures, networks, meanings, and values, then the method we use to acquire knowledge about these things should be pluralistic as well. Our methods should allow us to pose research questions about all these kinds of things.

Heuristics for a mechanisms-based methodology

Let’s imagine that I’m a young sociologist or political scientist who has gotten interested in the social-mechanisms debates, and I’d like to frame my next research project around a set of heuristics that are suggested by the mechanisms approach. What might some of those heuristics look like? What is a "mechanisms-based methodology" for sociological research? And how would my research play out in concrete terms? Here are a few heuristics we might consider.

1. Identify one or more clear cases of the phenomenon I’m interested in understanding
2. Gain enough empirical detail about the cases to permit close examination of possible causal linkages
3. Acquaint myself with a broad range of social mechanisms from a range of the social sciences (political science, economics, anthropology, public choice theory, critical race studies, women’s studies, …)
4. Attempt to segment the phenomena into manageable components that may admit of separate study and analysis
5. Use the method of process-tracing to attempt to establish what appear to be causal linkages among the phenomena
6. Use my imagination and puzzle-solving ability to attempt to fit one or more of the available mechanisms into the phenomena I observe
7. Engage in quasi-experimental reasoning to probe the resulting analysis: if mechanism M is involved, what other effects would we expect to be present as well? Do the empirical realities of the case fit these hypothetical expectations?

These heuristics represent in a rough-and-ready way the idea that there are some well understood social processes in the world that have been explored in a lot of empirical and theoretical detail. The social sciences collectively provide a very rich toolbox of mechanisms that researchers have investigated and validated. We know how these mechanisms work, and we can observe them in a range of settings. This is a realist observation: the social world is not infinitely variable, and there is a substrate of activity, action, and interaction whose workings give rise to a number of well understood mechanisms. Here I would include free rider problems, contagion, provocation, escalation, coercion, and log-rolling as a very miscellaneous set of exemplars. So if we choose to pursue a mechanisms-based methodology, we are basically following a very basic intuition of realism by asking the question, "how does this social phenomenon work in the settings in which we find it?".

So how might a research project unfold if we adopt heuristics like these? Here is a striking example of a mechanisms approach within new-institutionalist research, Jean Ensminger's account of bridewealth in the cattle-herding culture of Kenya (Making a Market: The Institutional Transformation of an African Society). First, some background. The cattle-herding economic regime of the Orma pastoralists of Kenya underwent substantial changes in the 1970s and 1980s. Commons grazing practices began to give way to restricted pasturage; wage labor among herders came to replace familial and patron-client relations; and a whole series of changes in the property system surrounding the cattle economy transpired as well. This is an excellent example for empirical study from a new-institutionalist perspective. What explained the particular configuration of norms and institutions of the earlier period? And what social pressures led to the transition towards a more impersonal relationship between owners and herders? These are questions about social causation at multiple levels.

Ensminger examines these questions from the perspective of the new institutionalism. Building on the theoretical frameworks of Douglass North and others, she undertakes to provide an analysis of the workings of traditional Orma cattle-management practices and an explanation of the process of change and dissolution that these practices underwent in the decades following 1960. The book puts forward a combination of close ethnographic detail and sophisticated use of theoretical ideas to explain complex local phenomena.

How does the new institutionalism approach help to explain the features of the traditional Orma cattle regime identified by Ensminger’s study? The key institutions in the earlier period are the terms of employment of cattle herders in mobile cattle camps. The traditional employment practice takes the pattern of an embroidered patron-client relation. The cattle owner provides a basic wage contract to the herder (food, clothing, and one head of cattle per year). The good herder is treated paternally, with additional “gifts” at the end of the season (additional clothing, an additional animal, and payment of the herder’s bridewealth after years of service). The relation between patron and client is multi-stranded, enduring, and paternal.

Ensminger understands this traditional practice as a solution to an obvious problem associated with mobile cattle camps, which is fundamentally a principal-agent problem. Supervision costs are very high, since the owner does not travel with the camp. The owner must depend on the herder to use his skill and diligence in a variety of difficult circumstances—rescuing stranded cattle, searching out lost animals, and maintaining control of the herd during harsh conditions. There are obvious short-term incentives and opportunities for the herder to cheat the employer—e.g. allowing stranded animals to perish, giving up on searches for lost animals, or even selling animals during times of distress. The patron-client relation is one possible solution to this principal-agent problem. An embedded patron-client relation gives the herder a long-term incentive to provide high-quality labor, for the quality of work can be assessed at the end of the season by assessment of the health and size of the herd. The patron has an incentive to cheat the client—e.g. by refusing to pay the herder’s bridewealth after years of service. But here the patron’s interest in reputation comes into play: a cattle owner with a reputation for cheating his clients will find it difficult to recruit high-quality herders.

This account serves to explain the evolution and persistence of the patron-client relation in cattle-camps on the basis of transaction costs (costs of supervision). Arrangements will be selected that serve to minimize transaction costs. In the circumstances of traditional cattle-rearing among the Orma the transaction costs of a straight wage-labor system are substantially greater than those associated with a patron-client system. Therefore the patron-client system is selected.

This analysis identifies mechanisms at two levels. First, the patron-client relation is the mechanism through which the endemic principal-agent problem facing cattle owners is solved. The normal workings of this relation give both patron and client a set of incentives that leads to a stable labor relation. The higher-level mechanism is somewhat less explicit, but is needed for the explanation to fully satisfy us. This is the mechanism through which the new social relationship (patron-client interdependency) is introduced and sustained. It may be the result of conscious institutional design or it may be a random variation in social space that is emulated when owners and herders notice the advantages it brings. Towards the end of the account we are led to inquire about another higher-level mechanism, the processes through which the traditional arrangement is eroded and replaced by short-term labor contracts.

This framework also illustrates the seventh heuristic above, the use of counterfactual reasoning. This account would suggest that if transaction costs change substantially (through improved transportation, for example, or through the creation of fixed grazing areas), that the terms of employment would change as well (in the direction of less costly pure wage-labor contracts). And in fact this is what Ensminger finds among the Orma. When villages begin to establish “restricted grazing areas” in the environs of the village, it is feasible for cattle owners to directly supervise the management of their herds; and in these circumstances Ensminger finds an increase in pure wage labor contracts.

What are the scientific achievements of this account? There are several. First, it takes a complicated and detailed case of collective behavior and it makes sense of the case. It illuminates the factors that influence choices by the various participants. Second, it provides insight into how these social transactions work (the mechanisms that are embodied in the story). Third, it begins to answer -- or at least to pose in a compelling way -- the question of the driving forces in institutional change. This too is a causal mechanism question; it is a question that focuses our attention on the concrete social processes that push one set of social behaviors and norms in the direction of another set of behaviors and norms. Finally, it is an empirically grounded account that gives us a basis for a degree of rational confidence in the findings. The case has the features that we should expect it to have if the mechanisms and processes in fact worked as they are described to do.

A final achievement of this account is very helpful in the context of our efforts to arrive at explanations of features of the social world. This is the fact that the account is logically independent of an effort to arrive at strong generalizations about behavior everywhere. The account that Ensminger provides is contextualized and specific, and it does not depend on the assumption that similar social problems will be solved in the same way in other contexts. There is no underlying assumption that this interesting set of institutional facts should be derivable from a general theory of behavior and institutions. Instead, the explanation is carefully crafted to identify the specific (and perhaps unique) features of the historical setting in which the phenomenon is observed.

(Here is a nice short article by David Collier on the logic of process-tracing; link. And here is an interesting piece by Aussems, Boomsma, and Snijders on the use of quasi-experimental methods in the social sciences; link.)

Margaret Archer on social change

In Late Modernity: Trajectories towards Morphogenic Society Margaret Archer and several talented collaborators attempt to lay out a framework of thinking that will permit them to better conceptualize the nature of change the modern social world. The book continues a process of reflection and collaboration that began last year with the publication of Social Morphogenesis. Ultimately the aim is to create a full theory of "morphogenic society" based on Archer's concepts of morphostasis and morphogenesis The idea of a morphogenic society is the notion that something important has happened historically to change the relationship between stasis and genesis (structure and agency) in the social world, and that the modern world embodies a significantly different kind of social change than earlier epochs. (Here is a discussion of Social Morphogenesis (link), and here is an earlier discussion of Archer's theory of morphogenesis (link).)

Here is a helpful statement of Archer's general approach in her current work:

Both the celebration of contingency and the importance attached to acceleration are hostile to the morphogenetic approach, as a framework for explanation that generically examines the sequence {structural / cultural conditioning → social interaction → structural / cultural elaboration or stasis}. This entails examining the specific ‘what’, ‘why’, ‘whom’ and ‘how’ of particular changes or instances of morphogenesis / morphostasis. Instead, both ‘liquidity’ and ‘acceleration’ theorists eschew such specification and the ultimate aim of detecting underlying ‘generative mechanisms’ in favour of talking metaphorically about ‘flows’ and ‘speed’. Thus, both ignore the growing predominance of positive feedback over negative feedback (morphogenesis over morphostasis) as the rock-bottom mechanism that makes considering the advent of Morphogenic society (in multiple forms) worthy of being entertained — the agnostic aim of this series of books. (Late Modernity, kl 178)

This passage captures the core of the morphogenetic approach. Archer is consistent in referring to these three "moments" of the M/M sequence, which she breaks into three phases T1, T2-T3, T4:

T1 structural / cultural conditioning → 
T2-T3 social interaction → 
T4 structural / cultural elaboration or stasis

At the risk of over-simplifying, we might summarize her view in these terms. Her view is that each phase involves constraints on action and interaction. T1 involves the large structural and cultural contexts in which individuals take shape and act. T2-T3 involves the interactions of individuals who bear interests and group identities and who strive to bring about outcomes that favor those interests and identities. And T4 represents a new formation (elaboration) of a complex of structural and cultural constraints. (It is striking how closely this summary resembles the theory of strategic action fields put forward by Fligstein and McAdam; link.)

An important line of criticism that comes through this volume is a persistent critique of unanalyzed ideas about speed and acceleration. "The modern world changes rapidly"; what does that tell us if we haven't specified the dimensions and clocks according to which we measure the speed of a process? So the language of speed is simply uninformative, according to Archer and her collaborators.

More specifically, Archer and her colleagues take issue with the metaphor of “liquid modernity" as a way of understanding the contemporary world.  (Here is my earlier discussion of Zygmunt Bauman’s development of this idea; link.) Archer takes aim at the intuition that the contemporary social world is highly contingent and plastic (a central theme of a number of earlier postings here). She criticizes "liquid modernity" in these terms:

Labile 'flows' comprehensively displaced and replaced the determinate (not deterministic) influences of social structure and cultural systems on tendential change or stability. As structure and culture were pulverised under the tidal bore of liquidity, so was agency condemned to serial self-reinvention. (kl 166)

Archer's complaint against liquid modernity (LM), then, is what she takes to be its extreme version of plasticity on the part of structures, cultures, and actors. LM views the individual as a free-styler, pursuing fleeting purposes and impulses within fast-changing circumstances, with the result of a form of chaotic Brownian motion in the social world.

She prefers substantive analysis of the social world organized around the poles of morphostasis (the mechanisms that preserve the properties of a social structure or cultural feature) and morphogenesis (the generative mechanisms of change that work to disrupt and change the existing structure and cultural elements). Both are critical and persistent features of the social world, and it is absurd (she believes) to imagine that modernity consists exclusively of morphogenesis unbound (7).

Archer puts the central question of her current work (and that of her active collaborators) in these terms:

Is some degree of enduring stability necessary amidst intensifying social change? Do agents and actors need this in order to plan their own lives and the courses of action they will take in the social order? (kl 152)

Of course the answer to this question, when considered in its most extreme version, is "yes", for reasons much the same as those that led Kant to believe that the categories of space, time, and causation were necessary synthetic apriori. If there were no continuing stability in the social world from one hour to the next, then planning and action would be impossible. The harder question is this: how much stability is necessary for intelligible action within the social world? And the answer seems to be, rather less than Archer believes.

An important complexity in Archer's account of stability is the fact that she believes that the processes of change (morphogenesis) can create their own conditions of stability and continuity that transcend the traditional forms inherited from the morphogenetic past. So the opposition is not between morphostasis and morphogenesis, but between activity and various forms of stability, both morphostasis and morphogenesis. "The alternative -- not always recognized -- is that there are forms of 'stabilization' produced by morphogenesis itself that furnish an equally adequate (and more consonant) basis for planning activities" (14). And this turns out to be a point to which Archer attaches a great deal of importance (19).

Several contributions to the volume are particularly noteworthy, in addition to Archer's own introduction and her substantive contribution on "The Generative Mechanism Re-configuring Late Modernity".

First, Douglas Porpora's contribution to the volume ("Contemporary Mechanisms of Social Change") is a valuable extension of the framework. Porpora relates the morphostasis/morphogenesis approach (MM) to Marx's famous aphorism concerning structure and agency (78). Here is my own reflection on Marx's aphorism in a post on actor-centered history (link):

In Marx's famous contribution to the philosophy of history, he writes that "men make their own history; but not in circumstances of their own choosing." And circumstances can be both inhibiting and enabling; they constitute the environment within which individuals plan and act. It is an important circumstance that a given time possesses a fund of scientific and technical knowledge, a set of social relationships of power, and a level of material productivity. It is also an important circumstance that knowledge is limited; that coercion exists; and that resources for action are limited. Within these opportunities and limitations, individuals, from leaders to ordinary people, make out their lives and ambitions through action.

On this line of thought, history is a flow of human action, constrained and propelled by a shifting set of environmental conditions (material, social, epistemic). There are conditions and events that can be described in causal terms: enabling conditions, instigating conditions, cause and effect, ... But here my point is to ask the reader to consider whether this language of cause and effect does not perhaps impose a discreteness of historical events that does not actually reflect the flow of history very well. History is continuous and analog; causal structures are discontinuous and digital.

What all of this suggests to me is an alternative way of thinking about history that has a different structure from the idea of history as a stream of causes and effects. This approach might be called "actor-centered history": we explain an epoch when we have a story about what people thought and believed; what they wanted; and what social and environmental conditions framed their choices. It is a view of history that sounds more like composing a biography of a complex individual than it does telling the story of a bridge collapse. And it is a view that gives close attention to states of knowledge, ideology, and agency, as well as institutions, organizations, and structures. (Little, Understanding Society link)

This short reflection on actor-centered history seems to have significant affinities with the morphogenetic approach.

 

Porpora provides an analysis of four large causal mechanisms of social change in his piece:

• capitalism
• information, globalization, flows and networks
• world inter-state dynamics
• the environment and its effects

And he believes that these large causes work conjuncturally to bring about many of the large processes of change that we witness in the modern world: "Change derives from a conjuncture of different mechanisms, some but not all of which can be traced back to capitalist relations" (88).

Pierpaolo Donati's contribution, "Morphogenic Society and the Structure of Social Relations", is also of interest. Donati focuses on the nature of social relations as the "connectors that mediate between agency and social structure" (143). Though he does not use the terminology, it is possible to regard his work here as an effort to provide some of the microfoundations of the processes of morphogenesis that are postulated by Archer's approach. Donati refers to the social relation as the "molecule of the social" (153), a concept that I've turned to in my explication of the idea of methodological localism (link).

 This volume, like its companion, Social Morphogenesis, is an impressive demonstration of the value of collaborative research in social theory and the philosophy of social science. It is evident that the contributors to the two volumes have developed their ideas in interaction with each other, and the framework has acquired a great deal of substance and coherence as a result.

Realism and methodology

Methodology has to do with the strategies and heuristics through which we attempt to understand a complicated empirical reality (link). Our methodological assumptions guide us in the ways in which we attempt to collect data, the kinds of data we collect, the explanatory hypotheses we bring forward for that range of empirical findings, and the ways we seek to validate our findings. Methodology is to the philosophy of social science as historiography is to the philosophy of history.

Realism is also a set of assumptions that we bring to empirical investigation. But in this case the assumptions are about ontology -- how the world works, in the most general ways. Realism asserts that there are real underlying causes, structures, processes, and entities that give rise to the observations we make of the world, natural and social. And it postulates that it is scientifically appropriate to form theories and hypotheses about these underlying causes in order to arrive at explanations of what we observe.

This description of realism is couched in terms of a distinction between what is observable and what is unobservable but nonetheless real -- the "observation-theoretic" distinction. But of course the dividing line between the two categories shifts over time. What was once hypothetical becomes observable. Extra-solar planetary bodies, bosons, and viruses were once unobservable; they are now observable using various forms of scientific instrumentation and measurement. So the distinction is not fundamental; this was an essential part of the argument against positivist philosophy of science. And we might say the same about many social entities and structures as well. We understand "ideology" much better today than when Marx theorized about this idea in the mid-19th century, and using a variety of social research methods (public opinion surveys, World Values Survey, ethnographic observation, structured interviews) we can identify and track shifts in the ideology of a group over time. We can observe and track ideologies in a population. (We may now use a different vocabulary -- mentality, belief framework, political values.)

There are several realist methodologies that are possible in the social sciences. The methodology of paired comparisons is a common part of research strategies in the historical social sciences. This is often referred to as "small-N research." (Here is a description of the method as practiced by Sid Tarrow; link, link.) The method of paired comparisons is also based on realism and derives from causal ideas; but it is not specifically derived from the idea of causal mechanisms.  Rather, it derives from the simpler notion that causal factors function as something like necessary and/or sufficient conditions for outcomes. So if we can find cases that differ in outcome and embody only a small number of potential contributing causal factors, we can use Mill's methods (or more general truth-table methods) to sort out the causal roles played by the factors. (Here is a discussion of some of these concepts; link.) These ideas contribute to methodology at two levels: they give the investigator a specific idea about how to lay out his/her research ("seek out relevantly similar cases with different outcomes"), and they embody a method of inference from findings to conclusions about causal relations (the truth-table method). These methods allow the researcher to arrive at statements about which factors play a role in the production of other factors. (This is a logically similar role to the use of multiple regression in quantitative studies.)

Another possible realist approach to methodology is causal mechanisms theory (CM). It rests on the idea that events and outcomes are caused by specific happenings and powers, and it proposes that a good approach to a scientific explanation of an outcome or pattern is to discover the real mechanisms that typically bring it about. It also brings forward an old idea about causation -- no action at a distance. So if we want to maintain that class privilege causes ideological commitment, we need to be able to tell an empirically grounded story about how the first kind of thing conveys its influence to changes in the second kind of thing. (This is essentially the call for microfoundations; link.) Causal mechanisms theory is more basic than either paired comparisons or statistical causal modeling, in that it provides a further explanation for findings produced by either of these other methods. Once we have a conception of the mechanisms involved in a given social process, we are in a position to interpret a statistical finding as well as a finding about the necessary and/or sufficient conditions provided by a list of antecedent conditions for an outcome.

It is an interesting question to consider whether realism in ontology leads to important differences in methodology. In particular, does the idea that things happen as the result of an ensemble of real causal mechanisms that can be separately understood lead to important new ideas about methodology and inquiry?

Craver and Darden argue in In Search of Mechanisms: Discoveries across the Life Sciences that mechanisms theory does in fact contribute substantially to contemporary research in biology, at a full range of levels (link). They maintain that the key goal for much research in contemporary biology is to discover the mechanisms that produce an outcome, and that a central component of this methodology is the effort to explain a given phenomenon by trying to fit one or more known mechanisms to the observed process. So working with a toolbox of known mechanisms and "problem-solving" to account for the new phenomenon is an important heuristic in biology. This approach is both ontological and methodological; it presupposes that there are real underlying mechanisms, and it recommends to the researcher that he/she be well acquainted with the current inventory of known mechanisms that may be applied to new settings.

I think there is a strong counterpart to this idea in a lot of sociological research as well. There are well understood social mechanisms that sociologists, political scientists, and other researchers have documented -- easy riders, prisoners dilemmas, conditional altruism -- and the researcher often can systematically explore whether one or more of the known mechanisms is contributing to the complex social outcomes he or she is concerned with. A good example is found in Howard Kimeldorf's Reds or Rackets?: The Making of Radical and Conservative Unions on the Waterfront. Kimeldorf compares two detailed case histories and strives to identify the concrete social mechanisms that led to different outcomes in the two cases. The mechanisms are familiar from other sociological research; Kimeldorf's work serves to show how specific mechanisms were in play in the cases he considers.

This kind of work can be described as problem-solving heuristics based on application of a known inventory of mechanisms. It could also be described as a "normal science" process where small theories of known processes are redeployed to explain novel outcomes. As Kuhn maintains, normal science is incremental but creative and necessary in the progress of science.

A somewhat more open-ended kind of inquiry is aimed at discovery of novel mechanisms. McAdam, Tarrow and Tilly sometimes engage in this second kind of discovery in Dynamics of Contention -- for example, the mechanism of social disintegration (kl 3050). Another good example of discovery of mechanisms is Akerlof's exposition of the "market for lemons" (link), where he lays out the behavioral consequences of market behavior with asymmetric knowledge between buyer and seller.

So we might say that mechanisms theory gives rise to two different kinds of research methodology -- application of the known inventory to novel cases and search for novel mechanisms (based on theory or empirical research).

Causal-mechanisms theory also suggests a different approach to data gathering and a different mode of reasoning from both quantitative and comparative methods. The approach is the case-studies method: identify a small set of cases and gain enough knowledge about how they played out to be in a position to form hypotheses about the specific causal linkages that occurred (mechanisms).

This approach is less interested in finding high-level generalizations and more concerned about the discovery of the real inner workings of various phenomena. Causal mechanisms methodology can be applied to single cases (the Russian Revolution, the occurrence of the Great Leap Forward famine), without the claim to offering a general causal account of famines or revolutions. So causal mechanisms method (and ontology) pushes downward the focus of research, from the macro level to the more granular level.

The inference and validation component associated with CM looks like a combination of piecemeal verification (link) and formal modeling (link). The case-studies approach permits the researcher to probe the available evidence to validate specific hypotheses about the mechanisms that were present in the historical case. The researcher is also able to try to create a simulation of the social situation under study, confirm as much of the causal internal connectedness as possible from study of the case, and examine whether the model conforms in important respects to the observed outcomes. Agent-based models represent one such set of modeling techniques; but there are others.

So the methodological ideas associated with CM theory differ from both small-N and large-N research. The search for causal mechanisms is largely agnostic about high-level regularities -- either of things like revolutions or things like metals. It is an approach that encourages a more specific focus on this case or that small handful of cases, rather than a focus on finding general causal properties of high-level entities. And it is more open to and tolerant of the possibility of a degree of contingency and variation within a domain of phenomena. To postulate that civil disorders are affected by a group of well-understood social mechanisms does not imply that there are strong regularities across all civil disorders, or that these mechanisms work in exactly the same way in all circumstances. So the features of contingency and context dependence play an organic role within CM methodology and fit badly in paired-comparisons research and statistical modeling approaches.

So it seems that the ontology of causal-mechanisms theory does in fact provide a set of heuristics and procedures for undertaking social research. CM does have implications for social-science methodology.