On optimal civilian response during active shooter incidents

The number of active shooter incidents in the US has been increasing alarmingly. It is imperative for the government as well as the public to understand these events. Though both analytic and agent-based models have been proposed for studying active shooter incidents, there are only a few analytic models in the literature, and none incorporates civilian resistance. This article analytically investigates the optimal action of a civilian during an active shooter incident when he can hide, fight, or run

Developing novel optimization and machine learning frameworks to improve and assess the safety of workplaces

This study proposes several decision-making tools utilizing optimization and machine learning frameworks to assess and improve the safety of the workplaces. The first chapter of this study presents a novel mathematical model to optimally locate a set of detectors to minimize the expected number of casualties in a given threat area. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. Several sensitivity analyses illustrate the model\u27s robustness and draw key managerial insights. One of the prevailing threats in the last decades, Active Shooting (AS) violence, poses a serious threat to public safety. The second chapter proposes an innovative mathematical model which captures several essential features (e.g., the capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT.® (RHF) program in an academic environment. Given most of the past incidents took place in built environments (e.g., educational and commercial buildings), there is an urgent need to methodologically assess the safety of the buildings under an active shooter situation. Finally, the third chapter aims to bridge this knowledge gap by developing a learning technique that can be used to model the behavior of the shooter and the trapped civilians in an active shooter incident. Understanding how the civilians responded to different simulated environments, a number of actions could have been undertaken to bolster the safety measures of a given facility. Finally, this study provides a customized decision-making tool that adopts a tailored maximum entropy inverse reinforcement learning algorithm and utilizes safety measurement metrics, such as the percentage of civilians who can hide/exit in/from the system, to assess a workplace\u27s safety under an active shooter incident

Evaluating the Military Police Corps\u27 Active Shooter Preparedness Plan

The Military Police Corps\u27 active shooter preparedness plan is inadequate because several updated tactics, techniques, and procedures that have been developed over the past 20 years and implemented by civilian law enforcement agencies have not been incorporated, leaving the Corps less prepared during active shooter events. The purpose of this phenomenological study was to examine how Military Police Corps leaders trained their law enforcement and support personnel to respond to an active shooter event. The institutional analysis and development framework was used to analyze the day-to-day operational decisions within the Military Police Corps. Data for the qualitative case study were collected through semi structured interviews with 15 Military Police Corps leaders and soldiers across 5 military police battalions in the United States and Europe and military police training records. These data were subjected to axial and open coding, followed by a thematic analysis procedure. Participants perceived that the Corps\u27 active shooter preparedness training hours and methodology are insufficient to maintain proficiency in active shooter preparedness, that dispatchers are not properly trained on receiving active shooter calls, and that live exercise training for first responders is inadequate. Recommendations for Military Police Corps leadership include updating the training methodology for first responders and dispatchers, providing better tactical equipment for first responders, and revising policies in order to improve the Military Police Corps\u27 active shooter preparedness program. Implementation of these recommendations may promote public safety

ASSESSING INTEROPERABILITY BETWEEN BEHAVIOR DIAGRAMS CONSTRUCTED WITH SYSTEMS MODELING LANGUAGE (SYSML) AND MONTEREY PHOENIX (MP)

Systems engineers have long struggled to identify and understand system behaviors in the operational environment. System Modeling Language (SysML) is a graphical language used among systems engineers to relay details of the system’s design to various stakeholders. Monterey Phoenix (MP) is a behavioral modeling approach and tool utilizing a lightweight formal method and language to generate diagrams and display expected and unexpected emergent system behaviors. Through systematic analysis of SysML and MP behavior models, this research presents recommendations for improving MP in future releases to accommodate SysML compliance. The ability to merge MP’s scope complete event trace generation into a SysML compliant format would provide great insights and benefits into the DOD acquisition process. Findings from this research include several simple additions to MP diagrams that will better align them with SysML standards while preserving MP’s capability to enable identification of emergent behavior early in the design process, when the risks can be addressed before system design features are ever manufactured or tested.National Security Agency (NSA)Outstanding ThesisCivilian, Missile Defense AgencyCivilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the ArmyApproved for public release. Distribution is unlimited

Spartan Daily, February 19, 2019

Volume 152, Issue 11https://scholarworks.sjsu.edu/spartan_daily_2019/1010/thumbnail.jp

Policy Decisions and Options-Based Responses to Active Shooters in Public Schools

Active shooter events in K-12 schools have increased since 1990, and developing response policies to such events is a responsibility of school personnel. A paucity of data regarding options-based response practices existed with no focus on policy processes. The purpose of this qualitative multi-case study was to describe the decision-making processes used in school districts when approving the inclusion of options-based responses to active shooter events in Emergency Operations Plans (EOPs). The research questions addressed processes that shaped the development of options-based responses to active shooter policies in 3 K-12 school districts within the Midwest. The conceptual framework was informed by the theory of policy paradox and the concepts of situational awareness and resilience. Structured interviews were conducted with 12 school personnel and safety professionals involved in 3 high schools; EOPs and state and federal regulations and guidelines were reviewed. An analysis of the interview responses and document reviews using four levels of descriptive coding required a cross-case analytic technique to discover patterns, connections, and themes. Law enforcement and school personnel worked together to create policy and to implement trainings related to options-based response. Results included enhancing situational awareness and empowering teachers and students to become responsible for their safety. These findings can be used to inform and guide school leaders in their efforts to make policy and implementation decisions regarding active shooter policies in EOPs. The potential for social change exists in more school personnel understanding and implementing options-based response policies and making the lives of K-12 students safer

NIOSH science and service awards 2022

NSSA-Book_2022_508.pd

Designing a Curriculum in Active Shooter Behavioral Indicators for Students in Institutions of Higher Education in the State of New Jersey

The research in this study was conducted to explore proactive preventative measures to address the increasingly violent problem of active shooter scenarios for institutions of higher education within the State of New Jersey. Colleges and universities pose a unique threat when assessing risk factors, as these venues are open and, as a result, do not afford substantial protection against a violent assailant. The State of New Jersey has traditionally embraced a reactionary model of response, learning from past incidents and adjusting necessitated tactics accordingly. Active shooter training has become commonplace on college campuses in the State of New Jersey, and each institution is mandated by the state attorney general’s office to have a response policy specific to their venue. The law enforcement community, in conjunction with higher education administrations, have effectively organized comprehensive response protocols for an active shooter scenario. Unfortunately, current plans in a higher educational environment, are limited to reactive responses initiated after the initial act of aggression. Current research has identified recognizable warning indicators and behaviors present in individuals that have conducted active shooting incidents in the past. This emerging research has the potential to guide a new proactive response methodology that is permeating the security mindset for college campuses. By identifying an individual that overtly manifests specific warning signs recognized by professionals from the study of past assailants, the possibility now exists for averting future incidents rather than simply reacting to them. By collating current research regarding warning behavior identification and using this information to create a contemporary higher education curriculum, individuals exposed to this material will essentially become force multipliers in the fight against future shootings. Historically, violators of incidents of this nature emerge from the student population, suggesting that the peer group to which the attacker belongs are the community members most able to recognize these signs. Employing the framework developed by Stark and Lattuca (1997), the curriculum was carefully constructed with a focus on the undergraduate student population and designed for optimal learning by college students entering higher educational facilities in the State of New Jersey

Optimizing Natural Walking Usage in VR using Redirected Teleportation

Virtual Reality (VR) has come a long way since its inception and with the recent advancements in technology, high end VR headsets are now commercially available. Although these headsets offer full motion tracking capabilities, locomotion in VR is yet to be fully solved due to space constraints, potential VR sickness and problems with retaining immersion. Teleportation is the most popular locomotion technique in VR as it allows users to safely navigate beyond the confines of the available positional tracking space without inducing VR sickness. It has been argued that the use of teleportation doesn’t facilitate the use of natural walking input which is considered to have a higher presence because teleportation is faster, requires little physical effort and uses limited available tracking space. When a user walks to the edge of the tracking space, he/she must switch to teleportation. When navigating in the same direction, available walking space does not increase, which forces users to remain stationary and continue using teleportation. We present redirected teleportation, a novel locomotion method that increases tracking space usage and natural walking input by subtle reorientation and repositioning of the user. We first analyzed the positional tendencies of the users as they played popular games implementing teleportation and found the utilization of the tracking space to be limited. We then compared redirected teleportation with regular teleportation using a navigation task in three different environments. Analysis of our data show that although redirected walking takes more time, users used significantly fewer teleports and more natural walking input while using more of the available tracking space. The increase in time is largely due to users walking more, which takes more time than using teleportation. Our results provide evidence that redirected teleportation may be a viable approach to increase the usage of natural walking input while decreasing the dependency on teleportation