Adaptive Two-Player Hierarchical Holographic Modeling Game for Counterterrorism Intelligence Analysis

Abstract

Intelligence gathering and analysis for countering terrorism is a vital and costly venture; therefore approaches need to be explored that can help determine the scope of collection and improve the efficacy of analysis efforts. The Adaptive Two-Player Hierarchical Holographic Modeling (HHM) Game introduced in this paper is a repeatable, adaptive, and systemic process for tracking terrorism scenarios. It builds on fundamental principles of systems engineering, systems modeling, and risk analysis. The game creates two opposing views of terrorism: one developed by a Blue Team defending against acts of terrorism, and the other by a Red Team planning to carry out a terrorist act. The HHM process identifies the vulnerabilities of potential targets that could be exploited in attack plans. These vulnerabilities can be used by the Blue Team to identify corresponding surveillance capabilities that can help to provide warning of a possible attack. Vulnerability-based scenario structuring, comprehensive risk identification and the identification of surveillance capabilities that can support preemption are all achieved through the deployment of HHM.State variables, which represent the essence of the system, play a pivotal role in the Adaptive Two-Player HHM Game, providing an enabling roadmap to intelligence analysts. Indeed, vulnerabilities are defined in terms of the system's state variables: Vulnerability is the manifestation of the inherent states of a system (e.g., physical, technical, organizational, cultural) that can be exploited by an adversary to cause harm or damage. Threat is a potential adversarial intent to cause harm or damage by adversely changing the states of the system. Threat to a vulnerable system may lead to risk, which is a measure of the probability and severity of adverse effects.Each player in the Adaptive Two-Player HHM Game deploys the same modeling tools. This ensures that the results from different models can be compared and integrated. If the membership of different teams is drawn from groups with different value systems, skills, and experience, it can be expected that modeling results will differ. This should help to identify the appropriate mix of skills for a modeling team to develop a robust model. In addition, Bayesian analysis is central to the adaptive characteristics of the proposed methodology. Not only do new samples of evidence serve as likelihood functions to generate additional probabilities for given scenarios, but the probabilities associated with one scenario can be used as likelihood functions for other scenarios. This cross-updating process is further exploited by the construction of multiple decompositions, each representing a different perspective, e.g., geographical, functional, temporal. A food-poisoning scenario with Red and Blue Teams was developed to demonstrate the approach.