The science of diffusion and the spread of public policy

August 22, 2016, American Institute of Physics
Credit: George Hodan/Public Domain

Most of us think of diffusion in the context of its scientific definition as the process whereby particles of liquids, gases or solids intermingle and in dissolved substances move from a region of higher to one of lower concentration. However, it also applies to the spread of policies between and among cultural, social and governmental systems.

A research team at New York University (NYU) and University of California, Los Angeles (UCLA) collaborated on merging the domains of health policy with network science and dynamical systems to help understand the mechanisms of policy diffusion in the same way we understand the diffusion of one substance into another. Their findings are discussed in Chaos.

This line of research started when Maurizio Porfiri, an engineer at NYU, saw a documentary about the social factors influencing obesity, and he thought that perhaps his work in network science and dynamical systems could help understand the growth of obesity in the U.S. When he began looking for colleagues with the necessary expertise he identified James Macinko, at UCLA, and Diana Silver, at NYU, and a collaboration was born. However, their research began by exploring policy diffusion, not obesity, as Porfiri had originally thought.

With support from the National Institute on Alcohol Abuse and Alcoholism, the team set out to demonstrate the possibility of detecting causality in policy diffusion processes, encapsulated in networks of influence among states in the U.S.

"Loosely speaking, we seek to establish ways to pick up who are the leaders and followers among the 50 U.S states when it comes to creating new health policies," Pofiri said. While reconstructing network topologies in collective systems is a hot research area within chaos research, not much work has been done in the health policy domain. The team demonstrated two complementary approaches, grounded on information theory and time series analysis, using systematic analysis of surrogate datasets generated through a minimalistic model for policy diffusion and opening the door to new understandings of how policies are transmitted from state to state. To create a realistic model for policy diffusion on which to test algorithms for network reconstruction, the team looked into neuroscience research, specifically, into models of binary neurons used to explain observed spatiotemporal patterns, expertise contributed by Carsten Grabow, another NYU colleague.

Already the team is looking forward to further research in this area. From a public health point of view, they want to explore additional datasets, such as those related to tobacco regulations, to explore the generality of our approaches and unravel similarities in network structures across different health domains. From a theoretical point of view, they hope to explore alternative, data-driven means to describe the diffusion process as a low-dimensional manifold. From an engineering viewpoint, they hope to implement control techniques to enhance the diffusion of effective policies and inform the decisions of policymakers.

Their results could help public health experts to find connectivity structures in their datasets, and pinpoint the specific factors influencing policy diffusion.

"We envision researchers in the field tapping into our work to explain the role of ideology or geography, for example, on the diffusion of a specific policy across states," Porfiri said. "Ultimately, we hope policymakers will use / approaches to make decisions on health policies that could benefit our society at large."

Explore further: Researchers apply lessons of animal herd behavior to reduce alcohol-related traffic deaths

More information: Carsten Grabow et al, Detecting causality in policy diffusion processes, Chaos: An Interdisciplinary Journal of Nonlinear Science (2016). DOI: 10.1063/1.4961067

Related Stories

When diffusion depends on chronology

July 15, 2013

The Internet, motorways and other transport systems, and many social and biological systems are composed of nodes connected by edges. They can therefore be represented as networks. Scientists studying diffusion over such ...

Self-organized nanopatterns in multicomponent systems

June 18, 2012

European researchers have studied a new class of self-organized nanostructures formed by the complex interdependence of chemical reactions and diffusion in multicomponent reacting systems. Project results have potential applications ...

Software helps decrypt embryonic development

April 26, 2016

When new life develops, a tiny ball of initially identical cells has to form the different body parts of the mature organism. Sixty years ago, Alan Turing proposed that this body patterning is achieved by two types of signaling ...

Recommended for you

On the rebound

January 22, 2018

Our bodies have a remarkable ability to heal from broken ankles or dislocated wrists. Now, a new study has shown that some nanoparticles can also "self-heal" after experiencing intense strain, once that strain is removed.

Nanoparticle gel controls twisted light with magnetism

January 22, 2018

"Help me, Obi Wan Kenobi. You're my only hope." For many of those around at the release of Star Wars in 1977, that scene was a first introduction to holograms—a real technology that had been around for roughly 15 years.

1 comment

Adjust slider to filter visible comments by rank

Display comments: newest first

Steelwolf
not rated yet Aug 22, 2016
In other words they are finding who is obstructing or holding up the works for supposed Ideological reasons that always end up financial and then being able to bear the proper legal and financial strategies, and which authorities are 'clean' enough to be able to remove said obstruction. As always, follow the money.

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.