Conflict simulation, peacebuilding, and development

simulating civilian populations in war

The latest issue of the Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 6, 4 (October 2009) has an article in on it by Jonathan K. Alt, Leroy A. ‘Jack’ Jackson, David Hudak, and Stephen Lieberman on “The Cultural Geography Model: Evaluating the Impact of Tactical Operational Outcomes on a Civilian Population in an Irregular Warfare Environment”—or, in plainer language, how one might set about modeling the politico-military behaviour of a civilian population amidst an insurgency:

The civilian population has been described as ‘the center of gravity in irregular warfare’. Understanding the behavioral response of the civilian population in irregular warfare operations presents a major challenge area to the joint modeling and simulation community where there is a clear need for the development of models, methods, and tools to address civilian behavior response. This paper provides a conceptual and theoretical overview of the Cultural Geography (CG) model, a government-owned, open-source agent-based model designed to address the behavioral response of civilian populations in conflict environments. With an embedded case study, we describe the development of cognitive modules to represent the civilian population and their implementation as Bayesian belief networks (BBNs), the social structure module implemented using homophily, the process of adjudicating the effects of tactical level outcomes on a population segment within the model, and a sample case study analysis using a designed experiment.

Despite the array of variables that the model uses to generate agent behaviour—age, gender, education, tribe, political affiliation, with various social, economic, and political orientations associated with each of these—I remain dubious about the extent to which one can then determine collective behaviours as a consequence. This is partly because the range of actual variables shaping behaviour is so large, the relationship between them contingent and unclear, and the high probability of exogenous variables arising that haven’t been anticipated. Then again, is there really any other way of trying to get a simulation handle on the behaviours of large groups of individuals over time, especially in a way that lends itself to use as either a training or operations planning tool?

As work on such issues continues, and computing power continues to grow, it is inevitable that we will see more of this. The critical issue may be only in part how the simulations are constructed, how agents are modeled, how attitudes and behaviours are correlated and aggregated, and how the complex interactions between these operate. Just as important may be the pedagogical approach that is used in utilizing such simulations, and how they are framed as heuristic learning devices. If they are used as a substitute for critically interrogating social assumptions, they run the risk of abstracting from reality in dangerous ways. If, on the other hand, the simulation itself is used as an entry into larger discussions of how we understand social, economic, and political dynamics in societies—and the limits of our knowledge (what I’ve earlier termed “simhumility“)—it could prove a much more useful approach.

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