Our researchers are developing new tools to model the dynamics of belief on a massive network scale—resources that could advance the science of social diffusion and help policymakers predict how the spread of information through digital media will affect public attitudes and health behaviors.


According to recent federal studies, e-cigarette use tripled among U.S. teens between 2013 and 2014. These figures suggest a significant change in public attitudes, but the social forces behind this shift remain difficult for public health officials and policymakers to assess. Though big data analytics have enhanced our ability to map how information spreads through social networks, we still lack a reliable means of modeling how millions of online interactions drive the diffusion of beliefs.

simulation platform will provide insight into public health behaviors such as e-cigarette use

The capacity to quickly gauge public attitudes could revolutionize the way policymakers forecast long-term health trends and respond to immediate crises. To develop a toolset capable of delivering these critical insights, researchers in the Social and Decision Analytics Laboratory (SDAL) have partnered with scientists from Sandia National Laboratories, Carnegie Mellon, and the Biocomplexity Institute’s Network Dynamics and Simulation Science Laboratory.


This project will build on our research team’s previous successes in computationally modeling interactions that produce lasting changes in belief. Our refined toolset will leverage the broad-scale analytical power of network science, which helps to identify the drivers of complex systems, and the psychological insight of cognitive science, which measures the mental processes that inform decision-making.

The development process will begin with a preliminary study into the spread of attitudes toward e-cigarettes. This project will employ a combination of crowd-sourcing, big data analytics, and simulated social networking platforms to create a controlled environment for online social experimentation and large-scale simulations.


The group’s findings may be applied to analyze a variety of situations where the diffusion of information across social networks impacts public health decision-making, including instances of cyberterrorism, natural disasters, and epidemics.

Graduate student researchers receive advanced data analytics training

This three-year development process will also provide valuable training opportunities for students and post-doctoral fellows in network science and computational psychology.


  1. M. Orr, K. Zeimer, and D. Chen (Forthcoming, Fall 2016). Systems of Behavior and Human Health. In S. Galea & A. El-Sayed (Eds.), Systems Science and Population Health. Oxford University Press: Oxford, UK.
  2. M. Orr and D. Chen (Forthcoming, Fall 2016). Computational Models of Health Behavior. In R. Vallacher, A. Nowak, and S. Read (Eds.), Computational Models in Social Psychology. Psychology Press/Routledge: New York.
  3. M. Orr and D. Plaut. (2014). Complex Systems and Health Behavior Change: Insights from Cognitive Science. American Journal of Health Behavior, 38(3): pp. 404-413.
  4. M. Orr, R. Thrush, and D. Plaut. (2013). The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior.  PloS ONE, 8(5), e62490.

Back to top