Adversarial risk analysis for counterterrorism modeling.

Area of Science:

• Operations Research
• Decision Analysis
• Game Theory

Background:

• Recent large-scale terrorist attacks necessitate improved resource allocation models.
• Existing models often use game theory but struggle with intelligent adversaries and uncertain outcomes.
• There is a need for decision-analytic approaches to counterterrorism.

Purpose of the Study:

• To explore the application of Adversarial Risk Analysis (ARA) to standard counterterrorism models.
• To compare ARA solutions with typical game-theoretic approaches for resource allocation.
• To demonstrate how ARA supports defender decisions against intelligent attackers.

Main Methods:

• Critical assessment of game-theoretic approaches for counterterrorism models.
• Application of the Adversarial Risk Analysis framework to simultaneous and sequential defend-attack models.
• Coherent assessment of predictive probability models for adversary actions within ARA.

Main Results:

• ARA provides a structured framework for decision-making against intelligent adversaries in counterterrorism.
• The study analyzes simultaneous defend-attack, sequential defend-attack-defend, and sequential defend-attack models with private information.
• ARA offers a method to integrate adversary behavior prediction into resource allocation decisions.

Conclusions:

• Adversarial Risk Analysis is a valuable framework for enhancing counterterrorism resource allocation.
• ARA provides a coherent method for modeling adversary actions, supporting defender decision-making.
• The explored models serve as foundational building blocks for more complex counterterrorism risk analyses.