An earlier post addressed the question of the dynamics through which a stable community consisting of multiple groups may begin to polarize and fission into antagonisms and conflict. I speculated there that the tools of agent-based modeling might be of use here. What I had in mind was something like this. Suppose we have an urban population spread across space in a distribution that reflects a degree of differentiation of residence by income, religion, and race. Suppose religion is more segregated than either income or race across the region. And suppose we have some background theoretical beliefs about social networks, civic associations, communication processes and other factors influencing a disposition to mobilize. Perhaps ABM methods could allow us to probe different scenarios to see what effects these different settings produce for polarization and conflict.
There is a fair amount of effort at modeling this kind of social phenomena within the field of social simulation. Carlos Lemos et al provide an overview of applications of ABM techniques in social conflict and civil violence in “Agent-based modeling of social conflict, civil violence and revolution: state-of-the-art-review and further prospects” (link). Here is an overview statement of their findings about one specific approach, the threshold-based approach:
Social conflict, civil violence and revolution ABM are inspired on classical models that use simple threshold-based rules to represent collective behavior and contagion effects, such as Schelling’s model of segregation  and Granovetter’s model of collective behavior . Granovetter’s model is a theoretical description of social contagion or peer effects: each agent a has a threshold Ta and decides to turn “active” – e.g. join a protest or riot – when the number of other agents joining exceeds its threshold. Granovetter showed that certain initial distributions of the threshold can precipitate a chain reaction that leads to the activation of the entire population, whereas with other distributions only a few agents turn active. (section 3.1)
Here is a diagram of their way of conceptualizing the actors and the processes of social conflict into which they are sometimes mobilized.
Armano Srbljinovic and colleagues attempt to model the emergence of ethnic conflict in “An Agent-Based Model of Ethnic Mobilisation” (link). Their original impulse is to better explain the emergence of polarized and antagonistic ethnic conflict in the former Yugoslavia; their method of approach is to develop an agent-based model that might capture some of the parameters that induce or inhibit ethnic mobilization. They refer to the embracing project as “Social Correlates of the Homeland War”. They believe an ABM can potentially illuminate the messy and complex processes of ethnic mobilization observed on the ground:
Our more moderate goals are based on a seemingly reasonable assumption that the results observed in a simplified, artificial society could give us some clues of what is going on, or perhaps show us where to centre our attention in further and more detailed examination of a more complex real-world society. (paragraph 1.4)
They describe the eighties and nineties in this region in these terms:
So, by the end of the eighties and the beginning of the nineties, the ethnic roles in the society of the former Yugoslavia, that were kept toward the middle of Banton’s social roles-scale for more than forty years, now under the influence of political entrepreneurs, increased in importance. (paragraph 2.5)
And they would like to explain some aspects of the dynamics of this transition. They single out a handful of important social characteristics of individuals in the region: (a) ethnic membership, (b) ethnic mobilization, (c) civic mobilization, (d)grievance degree, (e) social network, (f) environmental conditions, and (g) appeals to action. Each actor in the model is assigned a value for factors a-e; environmental conditions are specified; and various patterns of appeals are inserted into the system over a number of trials
The algorithm of the model calculates the degree of mobilization intensity for all the agents as a function of the frequency of appeals, the antecedent grievance level of the agent, and a few features of the agents’ social networks. If we add a substantive hypothesis about the threshold of M after which group action arises, we then have a model of the occurrence of ethnic strife.
The model uses a “SWARM” methodology. It postulates 200 agents, half red and half blue; and it calculates for each agent a level of mobilization intensity for a sequence of times, according to the following formula:
- mi(t+1) = mi(t) + (miapp + misocnet + micool)Δt (paragraph 3.8)
This formula calculates the i^th individual’s new level of mobilization intensity m depending on the prior intensity, the delta created by the appeal, the delta created by the social network, and the “cooling” for the current period. (It is assumed that mobilization intensity decays over time unless re-stimulated by appeals and social network effects.)
This is a very interesting experiment in modeling of a complex interactive social process. But it also raises several important issues. One thing that is apparent from careful scrutiny of this model is that it is difficult to separate “veridical” results from artifacts. For example, consider this diagram:
Is the periodicity shown by Red and Blue mobilization intensities a real effect, or is it an artifact of the design of the model?
Second, it is important to notice the range of factors the simulation does not consider, which theorists like Tilly would think to be crucial: quality of leadership, quality and intensity of organization, content of appeals, differential pathways of appeals, and variety of political psychologies across agents. This simulation captures several important aspects of this particular kind of collective action. But it omits a great deal of substantial factors that theorists of collective action would take to be critical elements of the dynamics of the situation.
Here is a second example of an attempt to simulate aspects of ethnic mobilization provided by Stacey Pfautz and Michael Salwen, “A Hybrid Model of Ethnic Conflict, Repression, Insurgency and Social Strife” (link). Pfautz and Salwen describe their work in these terms:
Ethnic Conflict, Repression, Insurgency and Social Strife (ERIS) is a comprehensive, multi-level model of ethnic conflict that simulates how population dynamics impact state decision making and, in turn, respond to state actions and policies. Population pressures (e.g., relocation, civil unrest) affect and are affected by state actions. The long term goal of ERIS is to support operations development and analyses, enabling military planners to evaluate evolving situations, anticipate the emergence of ethnic conflict and its negative consequences, develop courses of action to defuse ethnic conflict, and mitigate the second and third order effects of U.S. actions on ethnic conflict. (211)
They refer to theirs as a hybrid model, incorporating a macro-level “systems dynamics” model and a micro-level ABM model. Their model thus attempts to represent both micro and macro causal forces on ethnic mobilization, illustrated in the diagram at the top. This model increases the level of “realism” in the assumptions represented in the simulation. Agents are heterogeneous, and their decision-making is contextualized to location on a GIS grid.
Agents represent 1000 individuals and are uniform with respect to religious affiliation. Agents are sampled with respect to age and sex ratio; however, skew sampling is used to create agents with different demographic profiles with respect to these attributes. Agents also have attributes to capture propensities to conflict and tolerance, which affect agent behavior and interact in the aggregate with the macro-level model to localize reports of conflict. (212)
Key variables in their simulation are religious identity, demographic change, population density, the history of recent inter-group conflict, and geographical location. The action space for individuals is: move location, mobilize for violence. And their model is calibrated to real data drawn from four states in Northwest India. Their basic finding is this: “Conflict is predicted in this model where islands or peninsulas of one ethnicity are surrounded by a sea of another (Figure 2.1).”
Kent McClelland offers a computational model that responds to Randall Collins’ concepts of “C-Escalation” and “D-Escalation” in inter-group conflict. McClelland’s piece is “CYCLES OF CONFLICT A Computational Modeling Alternative to Collins’s Theory of Conflict Escalation” (link). Here is how he describes his approach:
In this paper, I use a variation of systems theory to construct a multi-agent computational model of dynamic social interaction that shows how the conflict-escalation processes described by Collins can be generated in computer simulations. Like his, my model relies on feedback loops, but the mathematical formulas in my model use negative feedback loops, rather than positive feedback loops, to generate the collective processes of positive feedback described in Collins’s model of conflict escalation. My analysis relies on perceptual control theory (PCT), a dynamic-systems model of human behavior, which proposes that neural circuits in the brain are organized into hierarchies of negative-feedback control systems, and that individuals use these control systems to manipulate their own environments in order to control the flow of perceptual input in accordance with their internally generated preferences and expectations. (6)
Lars-Erik Cederman uses an ABM approach to model geopolitical boundaries (link). Here is how he describes his goals:
A decade ago, the Soviet Union ceased to exist, Yugoslavia started to disintegrate, and Germany reunified. Marking the end of the Cold War, these epochal events illustrate vividly that change in world politics features not just policy shifts but also can affect states’ boundaries and, sometimes, their very existence. Clearly, any theory aspiring to explain such transformations or, more generally, the longue durée of history, must endogenize the actors themselves.The current paper describes how agent based modeling can be used to capture transformations of this boundary transforming kind. This is a different argument from that advanced by most agent-based modelers, who resort to computational methods because they lend themselves to exploring heterogeneous and boundedly rational, but otherwise fixed, actors in complex social environments (1, 2). Without discounting the importance of this research, I will use illustrations from my own modeling framework to illustrate how it is possible to go beyond this mostly behavioral agenda. The main emphasis will be on the contribution of specific computational techniques to conceptualization of difficult to grasp notions such as agency, culture, and identity. Although a complete specification of the models goes beyond the current scope, the paper closes with a discussion of some of their key findings.
Cederman’s model incorporates three primary dynamics: “Emergent Polarity” (the idea that boundaries result from a process of conquest); “Democratic Cooperation” (the idea that “Democracy” functions as a tag facilitating cooperation among subsets of actors); and “Nationalist Systems Change” (the idea that boundaries result from actors seeking locations placing them in proximity to other actors possessing the same ethnic identity).
Here is a diagram representing stylized results of the simulation.
Epstein, Steinbruner, and Parker offer a model of civil violence (link). Here are the parameters that are assigned to all actors (population and cops): grievance, hardship, perceived legitimacy, risk aversiveness, field of vision, net risk, location, and decision to act. This is a very simple analysis of collective action, plainly derivative from a rational-choice approach. Each actor decides to act or not depending on his/her calculation of risk and hardship/grievance. These assumptions are vastly weaker than those offered by students of contentious politics like McAdam, Tarrow, and Tilly; but they generate interesting collective results when embodied in a generative ABM.
This research is specifically interesting in the context of the question posed here about fissioning. Consider this series of frames from an animation reflecting the results of random fluctuation of densities in an ethnically mixed community:
With “peace-keepers” the results are different:
These are interesting results. Plainly the presence or absence of peace-enforcers is relevant to the extent of ethnic violence that occurs. But notice once again how sparse the behavioral assumptions are. The simulations essentially serve to calculate the interactive effects of this particular set of assumptions about agents’ behavior — with no ability to represent organizations, communication, variations in motivation, etc.
All these models warrant study. They attempt to codify the behavior of individuals within geographic and social space and to work out the dynamics of interaction that result. But it is very important to recognize the limitations of these models as predictors of outcomes in specific periods and locations of unrest. These simulation models probably don’t shed much light on particular episodes of contention in Egypt or Tunisia during the Arab Spring. The “qualitative” theories of contention that have been developed probably shed more light on the dynamics of contention than the simulations do at this point in their development.