Mathematicians at UC Irvine in California have designed parameters to measure how to best prevent both one-on-one killings and mass shootings in the United States.
“It’s time to bring a scientific framework to this problem,” said lead author Dominik Wodarz, a mathematical biologist who works on disease and evolutionary dynamics. His co-author and wife, Natalia Komarova, a mathematician who studies biomedical and social trends, added: “Can we design a rational way to argue about guns?”
Both were appalled not just by the December shooting deaths of 20 youngsters and eight adults in Newtown, Conn., but also by the bitterly emotional dispute over weapons that erupted anew. They decided to put their professional expertise to work.
“This debate cannot be settled satisfactorily by verbal arguments alone, since these are often driven by opinion and lack a solid scientific backing,” the authors write. “What is under debate is essentially an epidemiological problem: How do different gun control strategies affect the rate at which people become killed by attackers, and how can this rate be minimized?”
The duo reviewed available data stretching as far back as World War I, then drew up equations to compute whether policies ranging from a total firearm ban to “arm everyone” increase or decrease homicides. After running the numbers, they found that in more common domestic and one-on-one crimes, reduced legal gun availability – if properly enforced – is likelier to lower deaths. But in rare mass shootings, armed citizens might save lives if sufficiently trained to avoid accidentally shooting fleeing bystanders.
They note that data is missing that could strengthen their results. For instance, homeowners who used a weapon to stop a robbery might not make a report to police. “Stand your ground” laws being widely discussed in the wake of Trayvon Martin’s killing could influence the parameters too. “Whether such laws better protect the public or increase deaths needs to be determined statistically,” Wodarz said. “Do you have a greater chance of dying if you run or if you face your attacker with a weapon?”
The authors say key parts of their equations should be studied more closely: the fraction of offenders who illegally possess a gun, the statistical degree of protection provided by legal gun ownership, and the number of people who are legally carrying a gun when attacked. Comprehensive data in those areas, they say, could further aid the development and implementation of effective policies.
Federal funding for gun control research was essentially nonexistent for nearly two decades, but President Barack Obama in January labeled firearm deaths a public health crisis and ended the longstanding freeze. About 11,000 Americans die each year from gunshot homicides.
A large number of peer reviews – 11 in total – were solicited by journal editors; two or three are the norm. A wide array of opinions were expressed, ranging from enthusiastically positive and constructive to a critic who stated that scientific methods would never be useful in this area.
The authors were warned to be prepared for heated responses to their paper but believe it’s critical to bring the best tools of research to the issue.
“If the current discussion could be steered toward science, rather than having a heated debate without much of a logical foundation, a big step forward toward saving lives would be achieved,” they said.
The paper, delivered on Friday July 26, 2013, is available here, but leaves a lot of room for vigorous discussion:
We analyzed mathematical models in order to calculate the gun-induced homicide rate of people depending on different gun control strategies. In particular, we examined the tradeoff that legal gun availability could either increase the firearm-induced death rates by increasing the number of gun-mediated attack, or reduce the death rate due to protection offered by gun ownership. Such a mathematical framework has so far not been constructed and analyzed, although our work falls into the larger area of shooting and crime modeling, which has been briefly reviewed above.
The gun control strategies in our model were expressed by a parameter that describes the fraction of the population that can legally own firearms. The strategies can range from a ban of private firearm possession to a “gun availability to all” strategy. We first investigated a situation in which one shooter is faced by only a single person that could potentially own a gun and that could fight back against the shooter. This can correspond to a one-on-one attack, such as a robbery, or a school shooting where the only person in the classroom that could carry a gun is the teacher. Subsequently, we examined a different scenario where a shooter faces a crowd of people, all of which could potentially own a gun and fight back against the attacker. This corresponds to shootings in public places such as movie theaters and malls.
In order to understand the implications of these modeling approaches, two aspects need to be considered. First, we discuss to what degree the model formulation is rooted in epidemiological data and how the validity of the model can be tested. Subsequently, we discuss how available statistical data can be used to parameterize the model and to derive specific predictions, based on the model’s assumptions.