A new study has used a type of computer simulation called an agent-based model, combined with a statistical method known as Bayesian inference, to examine factors that predict the risk of mass shootings. Researchers led by Mohammad R.K. Mofrad identified three main factors that contribute to mass shootings: the emergence of a motivated individual who could commit such an act, access to firearms, and the presence of a suitable target population. The study aimed to understand how these factors interact to increase the likelihood of mass shootings in different areas. To build the model, the researchers used demographic data from the U.S. Census to estimate how often motivated individuals might emerge in different regions, along with other parameters. They represented access to firearms by looking at the local density of Federal Firearms License (FFL) holders, which are businesses legally allowed to sell guns. This measure was found to be the most significant predictor of mass shooting risk in the model. The researchers tested their model by comparing its predictions with data from the Mother Jones database, which includes about 160 mass shooting events between 1982 and 2024. These events involved three or more fatalities and were not classified as terrorism, gang-related violence, or domestic disputes. The model successfully identified areas where mass shootings had occurred as having higher risk, showing its ability to distinguish between regions with different levels of historical risk. The study highlights that firearm availability plays a crucial role in the model, suggesting that access to guns may be a key limiting factor in the risk of mass shootings. According to the researchers, areas with the highest expected number of mass shootings per decade include Maricopa County in Arizona, Harris and Dallas counties in Texas, Los Angeles and Kings counties in California and New York, Clark County in Nevada, Orange County in California, Queens in New York, Cook County in Illinois, and Philadelphia County in Pennsylvania. The researchers argue that agent-based models like this one can be more effective than traditional statistical methods in identifying areas at risk for mass shootings. They believe such models could help guide the allocation of resources for prevention efforts, offering a more nuanced understanding of how different factors contribute to mass shooting risk.