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Combatant Fragmentation and the Dynamics of Civil Wars

Published online by Cambridge University Press:  24 April 2012

Abstract

Civil war dynamics and outcomes are shaped by processes of change largely unaccounted for in current studies. This examination explores how the fragmentation of combatants, especially the weaker actors, affects the duration and outcomes of civil wars. Some results of a computational modelling analysis are consistent with the article's expectations, several of them are counterintuitive. They show that when combatants fragment, the duration of war does not always increase and such wars often end in negotiated agreements, contrasting with the expectations of literatures on spoilers, moderates and extremists. Empirical cases, such as Iraq, Congo, Chechnya and the Sudan, illustrate the importance of fragmentation. This study demonstrates the value of accounting for diverse changes in actors and circumstances when studying the dynamics of war.

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Articles
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

Department of Political Science, Brigham Young University (email: [email protected]); and Department of Political Science, Oklahoma State University (email: [email protected]), respectively. The authors wish to thank Eric Dickson, Paul Diehl, Stephen Haptonstahl, Patrick James, James Kuklinski, Sarah Mitchell, Glenn Palmer, Toby Rider, Scott Wolford and the Political Science Research Seminar at Oklahoma State University for helpful comments. Special thanks to Wills Hickman, Adam Harris, Ken Noyes and Jon Walton for valuable research assistance. Findley acknowledges support from National Science Foundation Grant No. 0904883 and Rudloff thanks the College of Arts and Sciences at Oklahoma State University for research and travel support through an Arts and Sciences Summer Research grant. Computer code and raw results are available upon request from the authors. The Appendix may be viewed at http://www.journals.cambridge.org/jps.

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73 By using the term ‘experiment’, we are not suggesting that the model in any way provides an empirical ‘test’ similar to a laboratory or field experiment. Rather, we begin with a baseline condition similar to a control group and then incrementally change parameters in such a way that we can compare each successive condition against the baseline. While this is not the same as a randomized experiment, because we can compare against a baseline or control condition, using the term ‘experiment’ is useful.

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80 The small number of victories in certain parameter combinations means that the duration findings are sensitive to a small number of simulations. In subsequent discussion, we exercise caution when interpreting findings on the duration of victories, especially when the number of victories is small.

81 This limit of ten is chosen to simplify the number of agents, such that the analysis of the simulation and resulting plots will be easier to interpret. In the sensitivity analyses reported in the Appendix, we change the maximum number to 15 and 21 and find that the results are qualitatively similar to those reported in the article.

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84 Like most evolutionary models, we allow mutation with a very small probability. We include mutation to allow the possibility that the strongest agents do not immediately defeat all other agents. Like many wars, as certain combatants become stronger, they can also lose their ability to adapt and can suffer setbacks. In the model, mutation alters one agent's beliefs, typically one of the strongest, about another agent such that the belief becomes less accurate on average. A mutation parameter μ represents the probability that a randomly selected agent will undergo mutation. A new random draw is made (uμ∈[0,1]), and if it is less than the probability of an agent mutating (i.e., if μ < s), then the selected agent (assume m) undergoes mutation in which one of agent m's β's is changed. After randomly determining the other agent, a target, another random draw (ut∈[0,1]) is used to change the mutating agent's β in regards to agent target such that

$$ {{\beta }_{m,target}}\, = \,{{\beta }_{m,target}}\, + \,(({{\beta }_{m,target}}\,\times \,\psi )\,\times \,(1{\rm{ - }}(2\,\times \,{{u}_t}))), $$

where ψ represents the maximum possible percentage change in agent m's β parameter in regards to the target agent.

85 In the sensitivity analysis, we consider the case in which the new agents’ beliefs (i.e., how much they deviate from the true capability ratios) are derived from the parent's beliefs. When doing so, the results are broadly similar as the probability of fragmentation increases, but less conclusive when one considers the effect of increasing the deviation of beliefs from the parent's. See Figures 21 and 22 of the Appendix.

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89 One alternative would be to allow both coalitions to fight battles with each other, but this does not match empirical cases very well. In the Democratic Republic of the Congo, for example, fighting often took place on multiple fronts with limited engagement of different groups. Furthermore, this alternative requires strong assumptions about actors’ abilities to update beliefs in extremely complicated circumstances. We nonetheless modelled this possibility and report the results in the Appendix (Figures 15 and 16). Overall, the results are qualitatively similar to those that we find here.

90 We constructed an alternative model in which all agents can observe battles (but not necessarily participate) and still update their beliefs about the battling agents. The results of the model presented in Figures 4 and 5 of this article are similar to the results in this alternative specification (see Appendix, Figures 7 and 8).

91 In the Appendix, we consider how the other baseline results (belief deviation, negotiation updating and battle updating) change, once fragmentation is introduced.

92 Each of the new parameters is set at a midpoint, and then varied using a high, medium-high, medium, medium-low, or low value (see Table 2), resulting in 25 experimental runs. Each of these experiments consisted of 1,000 runs of the simulation, and the results of 25 experiments appear in Figure 4, and 25 more experiments appear in Figure 5. We also ran a different set of experiments for each of the baseline runs where each of the baseline runs varied a single baseline parameter, sweeping each of the parameter ranges, for a final total of 1,134 experiments. Given the large number of experiments conducted, it would be impossible to do justice to each. An Appendix is available on the Journal website and details the results for most of the experiments. We also consider a number of additional sensitivity analyses, such as coalition shifting, coalition battling, increasing the maximum number of agents, among others, which we report in the Appendix.

93 We do this for a number of reasons. First, it seems unlikely that a war will end with a single member of each coalition agreeing to negotiated terms, without regard for the number and relative strength of other agents. The conflict in Darfur continues despite the agreement of the government and one of the SLM factions in the May 2006 peace agreement, for example. Secondly, as one might expect, settlements occur relatively quickly under settlement rules requiring the strongest of each coalition and a majority of each coalition to agree for settlement to take place, because fewer agents must agree to a settlement under these settlement rules.

94 Cunningham, ‘Veto Players and Civil War Duration’.

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