Composition of the social

Our social ontology needs to reflect the insight that complex social happenings are almost invariably composed of multiple causal processes rather than existing as unitary systems. The phenomena of a great social whole — a city over a fifty-year span, a period of sustained social upheaval or revolution (Iran in the 1970s-1980s), an international trading system — should be conceptualized as the sum of a large number of separate processes with intertwining linkages and often highly dissimilar tempos. We can provide analysis and theory for some of the component processes, and we can attempt to model the results of aggregating these processes. And we can attempt to explain the patterns and exceptions that arise as the consequence of one or more of these processes. Some of the subordinate processes will be significantly amenable to theorizing and projection, and some will not. And the totality of behavior will be more than the “sum” of the relatively limited number of processes that are amenable to theoretical analysis. This means that the behavior of the whole will demonstrate contingency and unpredictability modulo the conditions and predictable workings of the known processes.

Consider the example of the development of a large city over time. The sorts of subordinate processes that I’m thinking of here might include —

  • The habitation dynamics created by the nodes of a transportation system
  • The dynamics of electoral competition governing the offices of mayor and city council
  • The politics of land use policy and zoning permits
  • The dynamics and outcomes of public education on the talent level of the population
  • Economic development policies and tax incentives emanating from state government
  • Dynamics of real estate system with respect to race
  • Employment and poverty characteristics of surrounding region

Each of these processes can be investigated by specialists — public policy experts, sociologists of race and segregation, urban politics experts. Each contributes to features of the evolving urban environment. And it is credible that there are consistent patterns of behavior and development within these various types of processes. This justifies a specialist’s approach to specific types of causes of urban change, and rigorous social science can result.

But it must also be recognized that, there are system interdependencies among these groups of factors. More in-migration of extremely poor families may put more stress on the public schools. Enhancement of quality or accessibility of public schools may increase in-migration (the Kalamazoo promise, for example). Political incentives within the city council system may favor land-use policies that encourage the creation of racial or ethnic enclaves. So it isn’t enough to understand the separate processes individually; we need to make an effort to discover these endogenous relations among them.

But over and above this complication of the causal interdependency of recognized factors, there is another and more pervasive complication as well. For any given complex social whole, it is almost always the case that there are likely to be additional causal processes that have not been separately analyzed or theorized. Some may be highly contingent and singular — for example, the many effects that September 11 had on NYC. Others may be systemic and important, but novel and previously untheorized — for example, the global information networks that Saskia Sassen emphasizes for the twenty-first century global city.

The upshot is that a complex social whole exceeds the particular theories we have created for this kind of phenomenon at any given point in time. The social whole is composed of lower-level processes; but it isn’t exhausted by any specific list of underlying processes. Therefore we shouldn’t imagine that the ideal result of investigation of urban phenomena is a comprehensive theory of the city — the goal is chimerical. Social science is always “incomplete”, in the sense that there are always social processes relevant to social outcomes that have not been theorized.

Is there any type of social phenomenon that is substantially more homogeneous than this description would suggest — with the result that we might be able to arrive a neat, comprehensive theories of this kind of social entity? Consider these potential candidates: inner city elementary schools, labor unions, wars of national liberation, civil service bureaus, or multi-national corporations. One might make the case that these terms capture a group of phenomena that are fairly homogeneous and would support simple, unified theories. But I think that this would be mistaken. Rather, much the same kind of causal complexity that is presented by the city of Chicago or London is also presented by elementary schools and labor unions. There are multiple social, cultural, economic, interpersonal, and historical factors that converge on a particular school in a particular place, or a particular union involving specific individuals and issues; and the characteristics of the school or the union are influenced by this complex convergence of factors. (On the union example, consider Howard Kimeldorf’s fascinating study, Battling for American Labor: Wobblies, Craft Workers, and the Making of the Union Movement. Kimeldorf demonstrates the historical contingency and the plurality of social and business factors that led to the significant differences among dock workers’ unions in the United States.)

What analytical frameworks available for capturing this understanding of the compositional nature of society? I have liked the framework of causal mechanisms, suggesting as it does the idea of there being separable causal processes underlying particular social facts that are diverse and amenable to investigation. The ontology of “assemblages” captures the idea as well, in its ontology of separable sub-processes. (Nick Srnicek provides an excellent introduction to assemblage theory in his master’s thesis.) And the language of microfoundations, methodological localism, and the agent-structure nexus convey much the same idea as well. In each case, we have the idea that the social entity is composed of underlying processes that take us back in the direction of agents acting within the context of social and environmental constraints. And we have a premise of causal openness: the behavior of the whole is not fully determined by a particular set of subordinate mechanisms or assemblages.

More on continental philosophy of social science

I encourage interested readers to take a look at the very thoughtful and extensive comment provided by Nick from accursedshare on my earlier posting on continental philosophy of social science. Nick highlights a number of very important lines of thought that are making progress in contemporary discussions of these issues within continental philosophy of science. I am particularly intrigued at his description of “assemblages” — quoting Nick, “assemblages look at the play of micro-level tools, intentions, habits, techniques, etc. They take these and look at how they spread throughout society (using Gabriel Tarde’s work on imitation, for example).” This is a very interesting approach, and one that emphasizes the themes of contingency and heterogeneity in social processes that I find particularly compelling. Nick’s comment makes it evident that there is a lot of fertile and imaginative work going on in this tradition. (He particularly mentions Foucault, Gilles Deleuze, Bruno Latour, William Connolly, Manuel DeLanda, Jane Bennett and Saskia Sassen.)

Nick is well into an extensive critical discussion of François Laruelle’s philosophy in his postings on accursedshare. Thanks, Nick!

Explaining rodeos

Suppose we have visited quite a few rodeos in Arizona and Texas and have observed a couple of things: there are more injuries in rodeos than in stock car racing or football, the stakes for the winners are lower than in golf, the rodeo riders score higher than average on the “introverted” component of the Myers-Briggs personality profile, some rodeos are almost silent places, and the parking lots are filled with a higher percentage of beat-up F150s than a typical baseball park. What would count as “giving an explanation” of these features of this particular type of social activity? And what kinds of mechanisms might serve as a basis for explanations?

Rodeos are an instance of a broader category of social activity that we migh describe along these lines: “mass entertainment events featuring professional athletes/performers and drawing extensive numbers of paying customers.” The comparative judments expressed above take the form of contrasts across several different sub-categories of this broad set — e.g., baseball, stock car racing, football, golf, soccer, circuses. We might further analyze this group of events in terms of several structural features: the character of the audience, the character and recruitment processes of the performers, the rules of the event, the culture of the activity, the meaning of the activity within the broader society, and the business fundamentals of the event (sources and quantity of revenue).

Let’s pull apart a few of the patterns noted above, and consider some social mechanisms that might explain them.

Personality profile of rodeo riders. Rodeo riders seem to have a different social psychology than baseball or soccer players; they are more solitary, introverted, and self-sufficient. This feature of rodeos presumably derives from the social selection processes through which an individual becomes a highly skilled rider; the personality features that are best suited to superior performance; and the cultural expectations of behavior within the activity and within the broader society. So an adequate explanation of this distinguishing feature of rodeos will probably invoke the processes of selection through which the performers are recruited, and the feedback and training they receive in the “minor leagues” of rodeo. Here the mechanisms are selection / filtering and training / inculturation.

Size of winnings. The size of the winner’s purse depends primarily on the revenue structure of the sport, which depends in turn upon the popularity of the sport, the affluence of the audience, and the size of the national or worldwide audience for the sport on television. The purse serves as a primary incentive for the most talented performers; rodeo operators compete with each other for the top performers; and competition among operators pushes the purse to a level commensurate with the total revenues generated by the sport. The audience for stock car racing is much larger than that for rodeo, both regionally and nationally; and the purses are correspondingly higher. Here the mechanism is business competition.

Incidence of injuries. Riding a bull is a generally hazardous activity. So we would expect that riders will be injured. But so is driving a car at 180 miles per hour — and yet the incidence of injuries in NASCAR racing is substantially lower than that in the rodeo circuit. So paying attention simply to the inherent danger of the activity probably doesn’t explain the difference. Instead, it seems reasonable to ask whether there are institutional differences across these sports — the structure of the safety systems that each embodies — that account for different rates of injury in different types of performance. Industries that have developed genuine institutional commitments and rules governing safety generally demonstrate better safety records. So we might hypothesize that the culture of safety is less strenuous in the rodeo circuit than in professional football or stockcar racing. Here the mechanism is the institutional setting in which the activity takes place, including the presence or absence of penalties for operators with bad safety records.

The parking lot. We notice that the distributions of cars and trucks in the parking lots are quite different at rodeos compared to soccer matches. The vehicles come with their owners; so these differences must derive from differences in the audiences of the two sports. Here we have another example of selection: the composition of the parking lot corresponds to the selection of people who are interested in attending rodeos; this group has a set of tastes and preferences that favor F150s over other vehicles; and the result is — a preponderance of pickup trucks in the rodeo parking lot. (And the trucks are in bad shape for several reasons — the generally lower income level of the rodeo fan and the likelihood that he/she does a lot of back-country driving.) The mechanism here is — social selection.

The silent audience. We observe once in a while that the audience at a small rodeo is plainly enjoying itself — but there is almost no clapping or shouting. There is no roar of the crowd. Why so? Here we may find a cultural explanation in the background — this rodeo is drawing an audience of Navajo people, and applauding and shouting are not the means by which Navajo fans express their appreciation and enjoyment. Here the mechanism is — cultural practices.

What seems interesting to me about this example, is the fact that there are a number of quite different social mechanisms at work that lead to the particular characteristics of such a mundane activity as the rodeo. There is no single process we should point to as the explanatory foundation of rodeo phenomena; instead, there are selection mechanisms, business incentives, cultural practices, media promotions, and socialization processes for both participants and audiences that influence the activity as a whole. And, interestingly enough, these mechanisms can lead to some common characteristics across the set of what appear to be a fairly arbitrary set of activities — public arenas where daring men and women ride large, dangerous animals for pay. The social characteristics of the audiences, the performers, and the local institutions defining the activity impress a unique stamp on these performances that distinguish them from other public entertainment activities such as circuses or NASCAR races.

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.

New angles on French history

In teaching an undergraduate seminar on the philosophy of history, I tried to come up with some readings that would stimulate some genuinely new thinking on this subject. Several things worked well, including simply reading some talented contemporary historians carefully. But the most truly innovative and stimulating twist was a week spent reading and discussing Robert Darnton’s numerous reviews of books on the period of the French Revolution in the New York Review of Books. (Darnton’s own book, The Great Cat Massacre: And Other Episodes in French Cultural History, was also a great addition to the seminar — but that’s another posting.)

Written over roughly a twenty-year period, Darnton’s smart reviews provide a great perspective on how the historiography of the French Revolution has changed. From the structural, class-centered approach of Albert Soboul, through Richard Cobb’s insistence on mentalités, or Simon Schama’s person-centered telling of the story, it is possible to see a shifting scene of historians’ judgments about causes, structures, ideas, movements, and scale. All by itself this is an important insight into historical understanding. And it illustrates an important fact about historical knowledge: no event is ever known with finality. (This parallels the point made in my recent posting on China’s Cultural Revolution.)

But in our discussions we also found that it is possible to look at Darnton’s reviews themselves as an extended and implicit historiographical essay. In his commentary on the writings of others Darnton also reveals many of his own historical intuitions. And of course Darnton’s own ethnographic turn in The Great Cat Massacre — evidently worked out while Darnton was teaching an interdisciplinary seminar with Clifford Geertz — is itself an important step on the historiography of French social change. And so the project of trying to discover whether there is a coherent and innovative philosophy of history nested within these reviews proved to be a fruitful one — there is. And this provides an interesting new avenue of approach to the problem of formulating a philosophy of history, a different wrinkle on the insight that we can learn a lot from observing the practice of great historians.

Several points come out of this set of reviews quite vividly: for example, the deep contingency of historical change, the importance of the particular, the importance of experience and mentalités, the dialectic of events and agents, and the difficulty of framing a large historical event.

(If you have a subscription to the New York Review of Books, all the reviews are available electronically in the archive.)

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.