This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Can mass shootings be forecast? Mofrad and colleagues constructed an agent-based model of mass shootings within a Bayesian inference framework.
In the model, a shooting arises from the intersection of three factors: the emergence of a motivated potential perpetrator, access to firearms and the presence of a suitable target population. Using demographic data from the U.S. Census, the researchers estimated a per capita rate of perpetrator emergence jointly with other model parameters.
Access to firearms was modeled using the local density of Federal Firearms License (FFL) holders as a proxy and emerged as the dominant predictor of risk within the model. The authors compared the model's predictions with mass shooting events recorded in the Mother Jones database from 1982–2024. This database includes about 160 events with three or more fatalities, excluding the perpetrator, that were not categorized as terrorism, gang-related violence or a domestic dispute.
The model assigned higher shooting risks to areas where shootings occurred, supporting its ability to distinguish areas with different levels of historical risk. The relative importance of firearm availability in the model suggests that access to a firearm may be an important limiting factor in mass shooting risk. The 10 highest-risk areas by expected events per decade are Maricopa County, Ariz.; Harris County, Texas; Dallas County, Texas; Los Angeles County, Calif.; Kings County, N.Y.; Clark County, Nev.; Orange County, Calif.; Queens County, N.Y.; Cook County, Ill.; and Philadelphia County, Pa.
According to the authors, agent-based models can help identify areas at elevated risk of mass shootings, outperform standard statistical baselines and may provide a framework for directing prevention resources. Forecasting the future of American mass shootings with Bayesian agent-based model calibration, PNAS Nexus (2026). academic.oup.com/pnasnexus/art … 93/pnasnexus/pgag294 Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space.
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