New forms of collective behavior?


Personal electronic communication and the Internet — have these new technologies changed the game for collective action? Here I am thinking of email and instant messaging, but also cell phones and other personal communications devices, as well as the powerful capacity for dissemination of ideas over the web — has this dense new network of communication and coordination fundamentally changed the ability of groups to pursue their political or social goals?

There is no doubt that these technologies are relevant to collective action. Communication, coordination, and assurance are crucial features of successful collective action — and these are precisely the qualities that current technologies offer. Moreover, the ability for a party or movement to disseminate its programs, ideas, and promises to potential followers is crucial for its ability to gather support; and this is what the Web offers better than any prior form of communications technology.

A couple of data points are relevant.

  • The City of New York has recently subpoenaed the software and records of TXTmob from an MIT graduate student (story). TXTmob is a software tool created more or less on the fly before the party conventions in 2004 to permit demonstrators to use text messages to assemble and disperse quickly and effectively.
  • Will.i.am’s music video of Barak Obama’s “Yes We Can” speeches has been viewed by eight million people since posting on YouTube — generating funds, votes, and passion for the candidate.
  • Cell phone photos and videos have made their way out of Tibet and Burma documenting the crackdowns that have occurred in those places — allowing passionate groups of people outside the area to bring their protests to bear.

So what is genuinely new in this list? Covert cameras and travelers have existed for a long time. Cell phones were available in Gdansk and Teheran during street protests there in the 1970s. And newspapers, magazines, and television and radio have disseminated ideas widely. So, again, is there any reason to think that current communications technologies have changed anything fundamental — either the nature of popular mobilization or the balance of power between the powerful and the numerous?

Two factors are important enough to significantly change the nature of struggles between the powerful and the popular. First is the capacity for coordination among a large group that is created by cell phones and IM devices. A “flash mob” can form and dissolve in minutes. This can make their actions and demonstrations more effective and more difficult to repress. And there is a secondary benefit for the organization — rapid multi-sided communication can help to maintain solidarity and commitment within the group.

Second, the low cost and broad distribution of web-based communication gives a new advantage to the numerous but poor. Swift Boaters required hundreds of thousands of dollars to disseminate their attack ads against candidate Kerry — whereas a six-minute video can reach millions of people on YouTube for free. This tips the balance of power away from the deep pockets towards the creative activist group.

So it seems reasonable to judge that these communications technologies are indeed a significant new element in the field of play of collective action. Groups can self-organize more effectively; they can coordinate their actions; and they can share and reinforce the urgency of their commitments through the use of cell phones, text messages, web pages, and dissemination points such as YouTube.

All this has implications for popular politics within a law-governed democracy. It is less clear that these technologies offer as much leverage for the powerless within an authoritarian state. Combine a powerful authoritarian state’s ability to monitor communications with a perfect readiness to repress activists and dissidents and to control the technology — and you get a situation in which these tools of communication are much less useful for an opposition.

Chaos and coordination in social life

Much social behavior is chaotic, in that it simply emerges from the independent choices of numerous agents during a period of time. It is analogous to Brownian motion — particles in a liquid moving in random motions as a result of innumerable bumps and pushes at the molecular level.

However, there are also many patterns that become visible in social behavior — examples of what I would like to call “coordinated social action”: stock market panic selling, holiday travel, rumors, style, riots, pickpockets in train stations. And we can identify many causes of coordination of individual behavior into larger patterns: commands, regulations, institutions, customs, conventions, collective plans, shared beliefs about social behavior, common sources of information, and common changes in the environment of choice, for example.

What I mean by “coordination” here is the opposite of chaos — something analogous to the coherence of photons associated with the laser effect. In a laser a set of photons are stimulated to fire coherently with each other, resulting in a beam of light that possesses focus and parallel propagation that is different in kind from the scattered diffusion of photons from an incandescent bulb. “Coordinated” social action is a set of actions that possess synchronicity or regularity in their occurrence, resulting in an observable regularity of behavior over time and space. A crucial problem for social inquiry is to provide an explanation of the mechanisms that underlie the instances of coordinated social action that we can identify.

Examples of coordinated social action can easily be offered, and specific mechanisms can be identified that produce these forms of social coordination. An army moves in concert across a landscape (command). People drive on the right in North America (regulation). People send their children to school (institution). People greet each other with a polite “good morning” (custom). Villagers come together to fish as a group in the morning (convention). People discuss a spontaneous demonstration in front of the mayor’s office on Wednesday, and many appear (collective plans). Drivers choose Route 3 rather than Route 1 because they expect a lot of traffic on Route 1 (shared beliefs). People buy a large number of batteries and chocolate, anticipating an approaching hurricane (common environmental change).

These are all mechanisms that create a degree of coordination or synchronization of behavior among independent agents. There seem to be several large categories of mechanisms here: hierarchical coordination (command, regulation); common response coordination (each individually responds to the same signal); communications and network coordination (individuals exchange messages to secure coordination); and strategic coordination (each intends to behave in a way that will be desirable given his/her expectation of actions by others). Might we try out the thought that all forms of regularities of social behavior derive from one or another of these forms of coordination? This thought is probably somewhat too strong a claim. For example, there are probably social regularities that derive from our biology and evolutionary histories — limitations of memory, bonds of intra-group loyalty, kin altruism. But the impulse is a sound one: when we are able to observe patterns of social behavior, there must be a cause of those regularities that works its way through influence on the individual actors who constitute the domain of action. And there are only so many mechanisms that might serve.

These sorts of regularities and mechanisms constitute part of the regularity of social life, but perhaps only part. It may be that they don’t capture other kinds of more “structural” regularities — for example, “racial discrimination increases health disparities,” “feudal political systems are slow to respond to external aggression”, “capitalist market systems are more innovaative than planned economies”. But there is an important aspect of social explanation that centers exactly on this question: what are the social mechanisms that bring a degree of coherence and coordination among the actions of a population of independent actors?

Is network analysis inconsistent with agent-centered explanation?


Quite a few researchers who study dynamic social processes are making use of some of the tools of network analysis. And it is sometimes maintained that this approach is inconsistent with an agent-centered approach to social processes. Some of these researchers take the view that “it’s not what is in the heads of various actors, but rather their relationships in networks that provide the causal underpinnings of social change.” And they sometimes maintain that the actor’s psychological states can’t even be identified in isolation from his/her social relationships. So, once again, explanation cannot rest upon facts about individuals alone. And this sort of finding is thought to cast doubt on methodological individualism in particular, and agent-centered explanatory strategies more generally. (Chuck Tilly and co-authors sometimes take a view along these lines; for example, Doug McAdam, Sidney Tarrow and Charles Tilly, Dynamics of Contention.)

There is something right about the intuition that we can’t ground social explanations on assumptions that are too narrowly confined to features of individual psychology. Individuals are socially constucted and socially developed, and our explanations of social processes need
to reflect this fact. This is why I prefer the phrase “methodological localism” to “methodological individualism.” But both ontologies are agent-centered. So the question remains: does the causal salience of social networks demonstrate that agent-centered accounts are inherently incomplete — or even worse, inherently unworkable (because we can’t even specify the individual agent’s powers and motives independently of his/her networks)?

I don’t think so, for several reasons. First, what is a network but a set of socially constructed agents in concrete relations with each other — communication, coordination, power, subordination, and recognition? The facts about the network are exhausted by a description of the social beliefs of the relevant actors and their material relations to each other.

Second, it is certainly true that an agent’s possibilities for exercising power are a function of facts beyond his/her own psychological characteristics. So Albert, the peasant activist in the tiny Breton village, is much more empowered than his psychological twin across the border in Normandy, by the fact that he alone has strong relationships with leaders in both the Catholic Church and the wine-growers’ guild. His social networks permit him to amplify the scope of action and effect he may attempt. What this means is that Albert’s social networks are a causal component in his ability to wield influence. In this sense it is reasonable as well to attribute causal status to the network and to characterize this standing as being independent of Albert as an individual.

But it remains true that all of the causal powers associated with the network depend on the states of agency of the many persons who make it up. We therefore need to be able to provide an agent-centered account of the network’s causal powers, distributed over the many agents who make it up. We must have “microfoundations” for the claim that the network exercises social influence. If the actors who constitute nodes within the network didn’t have the right mental frameworks, motivational dispositions, or bodies of knowledge, then they would not in fact behave in a way that was sustaining of the network’s social-causal properties.

So, it seems inescapable that, when we say that “Albert’s power as a peasant activist depends upon the social fact that he is part of such-and-so networks” — that we have only uncovered another field of research where more agent-centered research is needed. The network’s social-causal properties must themselves disaggregate onto a set of facts about the agents who constitute the network. The current causal properties of the network and the agents who make it up are the complex and iterative result of many inter-related actions and alliances of prior generations of agents.

And this in turn demonstrates that network analysis is by no means inconsistent with an agent-centered approach to social explanation.

(See “Levels of the social” for more on this subject.)

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.

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