Date of Award
Josef Korbel School of International Studies
Extrapolation Model, Insurgency, Power Law, Red Queen Hypothesis, Terrorism
Recent empirical studies suggest insurgencies may be accurately described by aggregated extrapolation models, such that past behavior becomes the best predictor for future action. I argue that aggregated extrapolation models possess two flaws that make it a poor choice for examining insurgencies. First, aggregated extrapolation models ask the wrong question. The more interesting question is to ask when present action is no longer explainable by past behavior. Secondly, aggregate models mask changes that a phenomenon undergoes over time which are only revealed upon disaggregating the data. Starting with a model and findings provided by Neil Johnson, I use casualty data from the Iraq War to offer an alternative method to identify changes in the phenomenon under observation with the addition of no new data. Presenting an alternate set of findings, I propose it is possible to identify `game changer' events with the introduction of breakpoints to observe for distinct departures from the baseline trajectory.
Dolcort-Silver, Micah S., "Recognizing 'Game Changers' in Extrapolation Models: An Application to Counterinsurgency" (2013). Electronic Theses and Dissertations. 166.
Recieved from ProQuest
Micah S. Dolcort-Silver