A Non-numerical Predictive Model for Asymmetric Analysis

Abstract

Predicting asymmetric threats (e.g., terrorist events) is becoming ever more important. Prior works have focused on tactical, statistical, and data-fusion systems. The thrust of our work has been the development of a non-numerical predictive model for amplifying intelligence analysts’ recognition of emergent threats. The intelligence community uses a Template schema for assessing courses of action. Our predictive model processes non-numerical data to arrive at automated assessment and confidence scores for these Templates. The predictive model is traceable, transparent, and utilizes Human-in-the-Loop data-fusion. For future work, this predictive model will be further enhanced with behavioral filtering. Behavioral filtering adjusts the assessment and confidence of the predictions by intelligently evaluating characteristic behavioral data. This non-numerical predictive model has been tested and verified in the Asymmetric Threat Response and Analysis Program (ATRAP).