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27 April 2018 Forecasting aggressive state behavior and assessing courses of action to successfully deter hostile adversaries
Brian Levey, Steve Shellman
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Proceedings Volume 10653, Next-Generation Analyst VI; 106530I (2018) https://doi.org/10.1117/12.2307601
Event: SPIE Defense + Security, 2018, Orlando, FL, United States
Abstract
This project employs data extracted from unstructured text and quantitative behavioral models to understand, forecast, and mitigate US adversaries' aggressive actions against the US and our allies. We use a combination of quasi-experimental causal modeling and counterfactual assessment techniques to assess the effectiveness of US courses of action (COAs) to quell aggressive states’ hostile activities. Results illustrate actions may yield unintended consequences through their impacts on other contextual factors. Additional analyses employ forecasting and ensemble techniques to examine the likely anticipated consequences of various US COAs in future scenarios and cases. Ultimately, the data, methods, and results provide a useful decision-support tool for planners and analysts faced with how best to mitigate against unfavorable outcomes.
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Brian Levey and Steve Shellman "Forecasting aggressive state behavior and assessing courses of action to successfully deter hostile adversaries", Proc. SPIE 10653, Next-Generation Analyst VI, 106530I (27 April 2018); https://doi.org/10.1117/12.2307601
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KEYWORDS
Data modeling
Machine learning
Analytical research
Factor analysis
Quantitative analysis
Statistical analysis
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