More on what can be explained

A previous posting argued that most social facts don’t admit of social explanation because they are too fundamentally conjunctural or too boringly ordinary. Let’s extend this thought by considering what sorts of social facts do admit of explanation.

One obvious category is the example of a perplexing mid-range social regularity. Why do used cars usually sell for less than their “real” value? Because of the asymmetry of information between buyer and seller (the market for lemons). Why does ethnic conflict turn violent more commonly in circumstances where the institutions of civil society are weak? Because weak civil institutions undermine trust between distinct intermixed groups. (Here is a posting summarizing some recent thinking on this connection between civil society and ethnic violence.) Why do collectivized farms usually witness lower labor productivity than privately owned farms? Because of some common features of labor management and supervision that are usually a part of collective farm practices but not of private farm practices, that are likely to result in individual effort that is of lower quality or intensity than the private alternative. (This is sometimes referred to as the “easy-rider” problem.)

This category encompasses cases of non-trivial regularity. These are all examples of what I call “phenomenal” social regularities. They are not manifestations of some underlying set of social laws, but rather the common results of a set of mechanisms or processes in a range of cases (article). In each case the explanation proceeds by identfying a common but non-obvious mechanism or structural feature that produces the observed outcome.

Another important category of social phenomena demanding explanation is the large, complex social occurrence. (William Sewell calls these “events” — historical occurrences that are “irreversible, contingent, uneven, discontinuous and transformational” (link). ) Here I have in mind things such as the great Pullman strike of 1894, the defeat of France in the Franco-Prussian War in 1870, the civil war in Lebanon in the 1980s, the Rwandan genocide, or the selection of alternating over direct current electricity transmission systems. In each case we want to know why the event occurred — the event is important and obscure — and it is credible that there may be a small number of social mechanisms and circumstances that can be discovered and that brought about the event.

So explanation of these singular but extended events takes the form of a causal narrative that turns on a small number of important causal factors or mechanisms. The burden of explanation is to discover the mid-level social processes and mechanisms that caused the outcome to occur. And a feature of generalization comes into this account as well, but at a different point in the story: to say that P caused O in the circumstances is also to imply that P would have similar effects in similar circumstances in other settings.

So the idea of a social regularity comes into this discussion twice. First, we are often led to ask for an explanation when we observe a curious regularity — instances of similar behaviors or outcomes in separate cases. “Why does this weak regularity obtain across independent cases?” And second, the type of explanation highlighted here is a causal explanation, which implies assertions about counterfactual regularities. “The outcome occurs because of the regular causal powers of such-and-so a causal mechanism.” A factor that possesses the causal power to help bring about a certain kind of effect plainly plays a role in statements like this: “whenever P occurs in circumstances substantially similar to C, O is likely to occur.” And this is a statement of an idealized regularity. This in turn lends some support for the idea that explanation and the discovery of a level of social generalization are linked — but not in the way that the nomological-deductive model of explanation would imply.

(See Varieties of Social Explanation for other perspectives on these questions.)

How much of social life can be explained?

How much of social life can be explained?

It may sound like a strange question — surely everything can be explained! And it’s true that nothing that occurs is “inexplicable”. But consider this homely example: if I spill my coffee on the desk, is there a scientific explanation of the particular shape that the splash of liquid takes? The final configuration of liquid on the desk is fully governed by physical laws and existing conditions; but chance and contingency play a critical role in the flow and splash of liquid as it moves into equilibrium. Some facts about the final equilibrium can be explained and predicted — the flat surface and shallow depth, for example. But the particular configuration of the radiating arms of the spill is highly contingent. So we might say that the depth of the pool has a scientific explanation but the shape does not.

Now bring the focus back to the social. The social universe contains a great deal of stuff that is random, chaotic, and conjunctural. Social outcomes are path-dependent: later events often depend critically on circumstances that occurred earlier in time. And this means that outcomes may be decisively shaped by accidental and ideographic events that occurred in the past.

Take collective behavior. In analogy to the coffee spill, we might be in a position to explain the behavior of each person in a crowd — and it may still be true that there is no explanation of the behavior of the group as a whole. (Maybe that is suggested by the beach crowd scene above.) Sometimes there is a salient explanation of group behavior, and sometimes there is not. And we might want to say that any social outcome that is random or depends primarily on a random concatenation of causes, cannot be explained but merely retraced. We can provide a narrative but not an explanation.

In fact, for a wide range of social phenomena, the outcome is simply the resultant of many small influences, and there is no salient reason for this particular outcome. There had to be some result, and the observed result is no more distinguished than any of the other possible outcomes. If the best causal story we can provide depends on unvarnished coincidence, then it seems reasonable to say that there is no explanation of this particular fact.

The most interesting social explanations arise when:

There is a large social trend or event that surprises us (change or unexpected persistence) and there is a previously unobserved factor that can be demonstrated to have caused the trend. Crudely, we might say that an outcome or pattern has an explanation just in case we have reason to believe there is a major causal factor that produces the pattern or outcome.

There appear to be a couple of pragmatic features to this question about whether something is amenable to scientific explanation. (This raises the question of the pragmatics of explanation in contrast to the logic of explanation.) First, it appears that there an implicature, in asking for an explanation of X, that X is unexpected. If so, there is an implication of contrast: contrary to the usual situations where X does not occur, X occurred on this instance. What caused X to occur? What factor in the situation led to the surprising outcome now? And second, there is the pragmatic preference for large and general factors rather than local and particular factors to serve as explanations.

So we might test out this idea: the proportion of social events that permit substantive scientific explanations is very low. Most social events are routine and expected, and they are the resultant of a large number of unimportant influences. And if either condition is present, then we might say that the event lacks an explanation.

Agent-based modeling as social explanation

Logical positivism favored a theory of scientific explanation that focused on subsumption under general laws. We explain an outcome by identifying one or more general laws, a set of boundary conditions, and a derivation of the outcome from these statements. A second and competing theory of scientific explanation can be called “causal realism.” On this approach, we explain an outcome by identifying the causal processes and mechanisms that give rise to it. And we explain a pattern of outcomes by identifying common causal mechanisms that tend to produce outcomes of this sort in circumstances like these. (If we observe that patterns of reciprocity tend to break down as villages become towns, we may identify the causal mechanism at work as the erosion of the face-to-face relationships that are a necessary condition for reciprocity.)

But there are other approaches we might take to social explanation and prediction. And one particularly promising avenue of approach is “agent-based simulation.” Here the basic idea is that we want to explain how a certain kind of social process unfolds. We can take our lead from the general insight that social processes depend on microfoundations at the level of socially situated individuals. Social outcomes are the aggregate result of intentional, strategic interactions among large numbers of agents. And we can attempt to implement a computer simulation that represents the decision-making processes and the structural constraints that characterize a large number of interacting agents.

Thomas Schelling’s writings give the clearest exposition to the logic of this approach Micromotives and Macrobehavior. Schelling demonstrates in a large number of convincing cases, how we can explain large and complex social outcomes, as the aggregate consequence of behavior by purposive agents pursuing their goals within constraints. He offers a simple model of residential segregation, for example, by modeling the consequences of assuming that blue residents prefer neighborhoods that are at least 50% blue, and red residents prefer neighborhoods at least 25% red. The consequence — a randomly distributed residential patterns becomes highly segregated in an extended series of iterations of individual moves.

It is possible to model various kinds of social situations by attributing a range of sets of preferences and beliefs across a hypothetical set of agents — and then run their interactions forward over a period of time. SimCity is a “toy” version of this idea — what happens when a region is developed by a set of players with a given range of goals and resources? By running the simulation multiple times it is possible to investigate whether there are patterned outcomes that recur across numerous timelines — or, sometimes, whether there are multiple equilibria that can result, depending on more or less random events early in the simulation.

Robert Axelrod’s repeated prisoners’ dilemma tournaments represent another such example of agent-based simulations. (Axelrod demonstrates that reciprocity, or tit-for-tat, is the winning strategy for a population of agents who are engaged in a continuing series of prisoners’ dilemma games with each other.) The most ambitious examples of this kind of modeling (and predicting and explaining) are to be found in the Santa Fe Institute’s research paradigm involving agent-based modeling and the modeling of complex systems. Interdisciplinary researchers at the University of Michigan pursue this approach to explanation at the Center for the Study of Complex Systems. (Mathematician John Casti describes a number of these sorts of experiments and simulations in Would-Be Worlds: How Simulation is Changing the Frontiers of Science and other books.)

This approach to social analysis is profoundly different from the “subsumption under theoretical principles” approach, the covering-law model of explanation. It doesn’t work on the assumption that there are laws or governing regularities pertaining to the social outcomes or complex systems at all. Instead, it attempts to derive descriptions of the outcomes as the aggregate result of the purposive and interactive actions of the many individuals who make up the social interaction over time. It is analogous to the simulation of swarms of insects, birds, or fish, in which we attribute very basic “navigational” rules to the individual organisms, and then run forward the behavior of the group as the compound of the interactive decisions made by the individuals. (Here is a brief account of studies of swarming behavior.)

How would this model of the explanation of group behavior be applied to real problems of social explanation? Consider one example: an effort to tease out the relationships between transportation networks and habitation patterns. We might begin with a compact urban population of a certain size. We might then postulate several things:

  • The preferences that each individual has concerning housing costs, transportation time and expense, and social and environmental environmental amenities.
  • The postulation of a new light rail system extending through the urban center into lightly populated farm land northeast and southwest
  • The postulation of a set of prices and amenities associated with possible housing sites throughout the region to a distance of 25 miles
  • The postulation of a rate of relocation for urban dwellers and a rate of immigration of new residents

Now run this set of assumptions forward through multiple generations, with individuals choosing location based on their preferences, and observe the patterns of habitation that result.

This description of a simulation of urban-suburban residential distribution over time falls within the field of economic geography. It has a lot in common with the nineteenth-century von Thunen’s Isolated State analysis of a city’s reach into the farm land surrounding it. (Click here for an interesting description of von Thunen’s method written in 1920.) What agent-based modeling adds to the analysis is the ability to use plentiful computational power to run models forward that include thousands of hypothetical agents; and to do this repeatedly so that it is possible to observe whether there are groups of patterns that result in different iterations. The results are then the aggregate consequence of the assumptions we make about large numbers of social agents — rather than being the expression of some set of general laws about “urbanization”.

And, most importantly, some of the results of the agent-based modeling and modeling of complexity performed by scholars associated with the Santa Fe Institute demonstrate the understandable novelty that can emerge from this kind of simulation. So an important theme of novelty and contingency is confirmed by this approach to social analysis.

There are powerful software packages that can provide a platform for implementing agent-based simulations; for example, NetLogo. Here is a screen shot from an implementation called “comsumer behavior” by Yudi Limbar Yasik. The simulation has been configured to allow the user to adjust the parameters of agents’ behavior; the software then runs forward in time through a number of iterations. The graphs provide aggregate information about the results.

Impersonal social causes?

There is a substantial place in social causation for mechanisms that link the intentions of powerful actors to the specific features of the outcome. “The outcome came about because the powerful actor wanted it to.” Why are there no petroleum refineries in mid-town Manhattan? Because zoning and planning boards have deliberately excluded such activities.

But what about causal mechanisms that are not the result of strategic choices by social actors? Are there impersonal social causes?

There are rare but real instances of social changes that occur without any intermediary of social action — for example, the eruption of Mount Vesuvius and the extinction of Pompeii. But these events fall outside the scope of the social sciences. And there are important social explanations that begin in impersonal features of the natural environment — for example, the configuration of rivers in China’s early history. But what makes these into social explanations is the analysis of the social behavior through which agents adapt these conditions to their needs. (See Mark Elvin’s truly excellent environmental history of China for more on this; The Retreat of the Elephants: An Environmental History of China.) But social explanations always involve actors — and that means that intentional social action always comes into the picture in some way. So we might begin by saying that there are no impersonal social explanations, if by that we mean “explanations of social outcomes that do not involve the actions of persons.”

It is important to observe that there are actually two distinctions that are relevant here. There is the “personal-impersonal” distinction, and there is the “intended-unintended” distinction. In an obvious sense all social causation is “personal”, in the sense that social causal mechanisms are always embodied in the constrained actions of socially constituted actors or persons. So the actions of deliberate actors are part of all social causation. But the intentions of the actors are often unrelated to the social outcome we are trying to explain. So in these cases the outcome is not caused by actors’ intention that it should come about. In the refinery example — it may be that there is no regulation prohibiting this kind of activity, but the cost of real estate makes the proposition unattractive from a cost-benefit perspective. On this scenario we would have the result occurring as an unintended consequence of the choices of a large numbers of independent actors.

These are the most interesting social explanations: explanations of social patterns or outcomes that are not the result of design or intention, but that nonetheless emerge through the purposive actions of large numbers of agents. These are “unintended consequences” explanations or “aggregative” explanations. We can quickly identify dozens of such examples: the silting of river deltas as a result of flood-management strategies upstream; the expansion of black-market sales of cigarettes as a result of new taxes on tobacco; the expansion of traffic flows as a result of the opening of the third harbor tunnel in Boston; etc. These explanations are “aggregative” in the sense that they work by “aggregating” the lower-level choices and preferences of individual actors into a higher-level social pattern. (Thomas Schelling offers numerous intriguing examples along these lines in his book, Micromotives and Macrobehavior.)

So now we can answer our original question. There are no social causes that work entirely independently from social actors, and actors are purposive. So all social causation stems from “intentional” human behavior; persons are always involved in social outcomes. However, there are many social outcomes that are unintended and unrecognized by all the participants. The participants’ intentions are local and parochial; whereas the social outcome is large and unforeseen. These instances are the most interesting problems for social inquiry. We might refer to these as “agency-based explanations of unintended and unforeseen outcomes.”

This suggests a different way of classifying social causes: outcomes that are the intended result of specific powerful actors (conspiracy, leadership, dictatorship); outcomes that are the result of strategic interaction among a small group of purposive agents (bargaining, collusion, cooperation); outcomes that result from concerted collective action by large groups with some sense of collective goals (boycotts, strikes); and outcomes that are the aggregate result of uncoordinated but constrained choices by large numbers of independent agents (markets, habitation patterns).

This classification also makes it more apparent why the concept of power is central in social explanation. The first three categories imply a distribution of powers across specific agents and groups, in order to account for the postulated connection between the agent’s purposes and the eventual outcome. And the fourth category implies the exercise of power by some other agency, to account for the observed constraints on choice that constitute the heart of this type of explanation.

Explaining large social formations: fascism

In a previous post I discussed the problem of explaining fascism. Let’s return to this issue as a topic for historical and social inquiry.

There are clearly a number of different explanatory questions we might have in mind: why did fascist movements emerge and gain popular support in the first three decades of the twentieth century? Why did these movements prevail in several countries and not in others? (This version parallels Skocpol’s question about revolutions.) Why did fascist states develop the political institutions they did in Germany, Italy, and Spain? How did fascist states and leaders exercise power? What prevented the rise of powerful fascist movements on France and Britain — in spite of the presence of ultra-nationalist leaders and organizations?

These are all different questions — even if there are relations among them. A particularly central question concerns the factors that were conducive to the emergence of extremist beliefs and organizations in certain periods and what factors favored the growth and power of some of these movements. This is a bundle of questions about the conditions that favor collective mobilization and ideological formation on a mass society. It is the sort of research question that Chuck Tilly and other scholars of popular mobilization have been concerned with.

Another set of questions about the course of fascism has more to do with institution building and state formation. Given the goal of creating powerful stare institutions within the general framework of fascist ideas and goals, what institutional and organizational possibilities existed? Here we might refer to the repertoire of mass organization that fascist “revolutionaries” brought to their movement, as well as the historical and practical options that existed. This area of inquiry may provide a basis for answering questions about the
particular nature of fascist political institutions.

Finally, the distinct question of why it was that fascist movements and leaders were able to defeat democratic movements and states requires that we identify some of the circumstances that weakened democratic regimes. This may be a wide range of factors: challenges of war, ideological conflict with communists and other critics of the state, and the economic circumstances of the great depression. (These fall in the same category as the circumstances that Skocpol brings forward as being relevant to the success or failure of revolutions.)

It would appear that social scientists and historians have better tools for addressing the issue of successful mobilization than the institutional or causal conditions surrounding seizure of power and state building. Schematically, we might consider a causal narrative along these lines: Conditions that favor fascism include the presence of a marginalized group of young people who are subject to great economic insecurity; an ideology that combines nationalism, ethnic
suspicion, and disaffection from established social institutions and values, and a compelling narrative of how and why this group ought to wield power. To this we might add a few propitious international conditions: the threat of war, a widening economic crisis, and a broad
view that the modern state isn’t up to handling these challenges.

This approach sketches out a view of what might be a basis for an explanation of the rise of fascist social movements. Here we have singled out several causal-social factors that facilitate popular mobilization and the politicization of social movements. What it doesn’t yet explain is why and in what circumstances these movements are likely to grow powerful enough to challenge the existing state structure; this remains for another discussion.

Explaining fascism

Kevin Passmore’s short introduction to fascism comes out at a good time (Fascism: A Very Short Introduction). Passmore does a great job of framing the problem. He poses a definitional question — what is fascism — and demonstrates that this apparently semantic issue requires careful historical and theoretical analysis. Arriving at a good definition of fascism is itself an empirical and historical task. Passmore asks a set of causal questions: How do the fascisms of Europe relate to important social forces in the early twentieth century (for example, the role of great social classes in conflict)? And he addresses the issue of saying what is involved in explaining fascism (the role of analysis and theory). Passmore also presents a very sophisticated treatment of the variety and diversity of human institutions — issues raised elsewhere under the topic of “heterogeneity”.

Of special interest for us is the question, Is fascism a particular social system (dictatorship with such-and-so attributes)? Or was it first and foremost a historically distinctive political and social movement with characteristic values and ideology (violence, nationalism, anti-communism)? Is it a historically specific moment, or is it a systemic development stimulated by some structural feature of modern society (deadlocked conflict between workers and the bourgeoisie)? Crudely — is fascism a social formation, an ideological complex, a social movement, or a type of government apparatus? And our efforts of explanation will depend on what sort of answer we give to these ontological questions.

These alternative definitions of fascism would give rise to very different explanatory challenges. And in fact, there is a wide variety of explanatory and causal questions that can be considered: Why did the fascist movements arise? Why did they gain a mass following? How did the social realities of capitalism affect the emergence and form of fascism? How important were the particular qualities and ideas of Hitler, Mussolini, or Franco in the evolution of fascism as a social system? Why did fascist dictatorships take the form they did? Why did official and affiliate group violence take the virulent forms that it did? How did fascist governments maintain power? Did these governments gain “legitimacy” and support in their populations? Is there a characteristic “pattern of development” for fascist regimes, or are their political histories deeply contingent on events and persons? Are Germany, Italy, and Spain variants of one social form, or are they simply independent social systems possessing some family resemblances in ideology, propaganda systems, and propensities for violence?

We might also consider whether explanation needs to occur at a lower level — not “why fascism?” but rather, “why the Iron Guard in Romania”, “why this or that feature of Italian fascism”, “why this particular feature of Spanish state-military relations in Franco’s fascism?”. Here the point might be that there are no general or comprehensive explanations of the emergence and development of fascism in all the places it occurred; no common causes that were always or usually instrumental; but rather that each national history needs to be treated in its own terms. But, as Passmore demonstrates, this would be somewhat too skeptical; there certainly were some large international and national forces that facilitated fascist mobilization and seizure of power in many different countries.

The historical phenomena of fascism are interesting and important, because they represented powerful social forces (movements and governments that had great influence on events in the twentieth century). We would like for historical social science to have something substantive and illuminating to say about the causes and trajectory of fascism. And, of course, we would be well advised to notice the warning signs if there are any!

(Another excellent very short introduction from Oxford that is relevant to this topic is Helen Graham, The Spanish Civil War: A Very Short Introduction.)