This project addresseed the challenge of effectively deterring threats and mitigating cascading risks at soft targets and crowded places, such as urban transportation systems.
This project addressed the significant gap in how agencies and stakeholders allocate resources to defend against threats to soft targets, as current methods often rely on intuition and outdated practices rather than data-driven strategies. Current practices lead to inefficiencies in deploying countermeasures and increase the vulnerability of soft targets to strategic attacks. The primary objective was to develop game-theoretic models, which strategically predict how humans will act and make decisions based on several factors. These models aid organizations in optimizing the allocation of security resources against strategic attackers based on these predictive models, which incorporate stakeholder-informed parameters and data, offering a tool for better decision-making and resource distribution to defend soft targets.
This project aimed to expand upon the limited library of detection and response strategies available regarding active shooter attacks. The objective was to create an algorithm and user-configurable software application capable of extending insights gained from collected data to optimize crisis response strategies. Developing new strategies intended to help researchers effectively create training modules, evaluate buildings, and provide crisis response guidance to civilians and law enforcement.