ABSTRACT

Firearm-related harms are a pressing societal problem that imperils America?s health and prosperity. The US leads high-income countries in both firearm homicide and suicide rates, with almost forty thousand firearm-related deaths in 2017, surpassing motor vehicle-related deaths for the first time. Fundamental understanding of the causal relationships among potentially contributing factors, such as firearm prevalence, state legislation, media exposure, and perceptions of firearm safety is needed. In an engineering sense, these factors are linked in complex and dynamic ways. This Leading Engineering for America's Prosperity, Health, & Infrastructure (LEAP-HI) award supports fundamental research to extend engineering methods for the understanding of the firearm ecosystem, as informed by state-of-art research in public health and social science. Through advancements in complex systems theory, data science, and hypothesis-driven experiments, the project will provide insights for improving public safety in the US. The multi-disciplinary approach will help broaden participation of undergraduate and graduate students at four urban campuses, which thrive on racial and economic diversity, and expose underserved communities to engineering education.

This research will first investigate the firearm ecosystem on three different scales. On the macroscale, research will illuminate cause-and-effect relationships between firearm prevalence and firearm-related harms. On the mesoscale, the project will explore the ideological, economic, and political landscape underlying state approaches to firearm safety. On the microscale, research will delve into individual opinions about firearm safety. Next, the three scales will be integrated into a data-driven probabilistic model of the firearm ecosystem to afford predictions of the system evolution, comparative studies with other countries, and what-if analyses to improve health and prosperity in the US. This research will advance the state of knowledge in information-theoretic causality analysis, multilayer complex networks, dimensionality reduction techniques, and human-computer interactions toward an unprecedented engineering understanding of the firearm ecosystem. The techniques developed in this project will constitute a powerful toolbox that can be utilized in other engineering domains pertaining to complex systems, where there is a need for statistically-based methods to elucidate causal mechanisms underlying system dynamics and create predictive data-driven models.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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