USING BEHAVIOR MODELING TO ENABLE EMERGENCY RESPONDER DECISION-MAKING

Mistakes during training are expected and usually welcomed for their teaching potential, but when realistic training subjects emergency responders to dangerous scenarios then there is still a high level of risk. Training is crucial for reducing risks associated with real-life operations, but how can real-life scenarios be practiced where it can be safe to learn from mistakes? This research will investigate the question, 'to what extent can Monterey Phoenix (MP) behavior modeling be used to support low-risk training for emergency responders?' We use MP to first generate a baseline 'typical-case' model of an active shooter scenario from FBI and FEMA procedures. We next develop alternative models by adding SME-provided variables to generate all possible scenarios within a scope limit with MP. Multiple scenarios allow emergency responders to practice making good decisions and gain a better understanding of the scenario, creating opportunities to decrease injuries and fatalities. This research found that both of the MP models, the typical-case model and the alternative events model, provide trainees with deeper insights into the roles and their actions during an active shooter scenario. In the alternative events model, we also see the variables that can occur within the scenario and identify where critical decisions are made by the corresponding roles. Both models are useful tools for improving training programs or understanding critical decision points.