Social, Cognitive, and Behavioral Sciences

The simulation of systems that implicate humans in any way requires some consideration of the core foundational disciplines that study human behavior (e.g., cognitive science, psychology, sociology, communications, economics). Naturally, because we deal in simulations, a central question arises: What kind of formal abstractions are both feasible and useful for the problem at hand, and implementable in a simulation environment? Social abstractions might use graph dynamical systems theory; abstractions in respect to individual- or team-level behavior might leverage cognitive architectures or precise psychological measurements. Our team employs a set of methodological perspectives that are designed to ground our simulation approaches in cognitive, behavioral, and social theory. For example, we conduct human experiments in controlled social settings to understand decision-making and resulting actions. We develop sophisticated measures of attitude formation (the automaticity perspective from Rich Fazio’s groundbreaking work) that can be deployed on Amazon Mechanical Turk. We’ve developed a technical simulation platform, The Matrix, that is designed to integrate cognitive models of human behavior (from cognitive science and cognitive psychology) with theory from the social sciences (e.g., social networks, behavioral economics) to drive the simulation of at-scale human systems.




Project Contact: Bryan Lewis
Funding Agency: National Science Foundation NSF

This project seeks to tie field-collected data and structural ecological models to better understand the complex interactions between the environment and humans in a riparian zone. We provide the agent-based modeling framework to synthesize the field-gathered environmental data with weather and human interactions.


DIBBS CIF21: Middleware and High-Performance Analytics Libraries for Scalable Data Science

Project Contact: Anil Vullikanti / Madhav Marathe
Funding Agency: National Science Foundation National Science Foundation

This collaborative project is supported by scientists from a variety of domains, including network science, epidemiology, spatial GIS, biomolecular simulations, pathology, computer vision, and remote sensing. This project addresses the need for high-performance data analytics with efficient parallel algorithms using a novel approach, centered on combining the breadth and productivity of best practice commodity Apache Big Data Stack (HPC-ABDS) and high-performance computing. The project will produce two types of key building blocks: Middleware for Data-Intensive Analytics and Science (MIDAS) and the Scalable Parallel Interoperable Data Analytics Library (SPIDAL). MIDAS and SPIDAL are motivated and tested by the applications in different domains.




Project Contact: Bryan Lewis
Funding Agency: National Science Foundation NSF

This project seeks to conduct both field and computational research on the impact of disease on the social group dynamics of social animals, like the banded mongoose. This research involves the development of both compartmental and agent-based models of social group dynamics and disease spread.



Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes

Project Contact: Chris Kuhlman / Anil Vullikanti
Funding Agency: National Science Foundation National Science Foundation

The goal of the ORDER-CRISP (Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes) project is to study damage-inducing mechanisms such as wind, rain, storm surge, and flooding. We model their effects on damage to both infrastructure (e.g., transportation, communications, electric power, potable water) and human (social/societal) networks. In particular, our focus is on understanding these interacting utility infrastructures and devising and assessing methods that make human populations more resilient before, during, and after natural disasters. Topics being studied include social isolation, social support structures, social vulnerability indices, and various well-being indices.



Systems analysis of social pathways of epidemics to reduce health disparities

Project Contact: Achla Marathe
Funding agency: National Institutes of General Medical Sciences (NIGMS), National Institutes of Health (NIH)   National Institutes of Health National Institute of General Medical Sciences logo

The objective of this R01 is to incorporate social behavior into mathematical models of infectious disease transmission dynamics, with a focus on influenza like illness. The inferences of this project will improve our understanding of the impact of different control and prevention strategies for infectious disease epidemics in general and Influenza epidemics in particular. Our hypothesis is that individual behavior, disease dynamics, and interventions coevolve across multiple scales to create statistically and epidemiologically significant differences in the efficacy and social equity of public health policies such as infectious disease control strategies.

This project extends well studied computational simulations to include people's behaviors relevant to infectious disease epidemics and will be used to determine the consequences of feedback between population-level effects and individual-level behavior. In particular, we will determine the sensitivity of outcomes to particular behaviors. A survey designed to focus on those particular behaviors will be used to estimate variability across communities and to calibrate the simulations. Published models and results on Influenza transmissibility and intervention efficacy will be revisited with the improved simulations. Initially, our analysis will describe the mean performance of interventions over the whole population. The analyses will then extend to scenarios reflecting the observed variability in behavior to reveal how health disparities could arise from behavioral differences at the community level. Altogether, the results of the new and comparative analyses will inform the design of optimal epidemic interventions with fewer unintended consequences.

Project Team

Achla Marathe (Contact PI), Professor, Department of Public Health Sciences, Biocomplexity Institute & Initiative, UVA.
Kaja Abbas (mPI), Assistant Professor, London School of Hygiene and Tropical Medicine, UK
Samarth Swarup, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Jiangzhuo Chen, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Stephen Eubank, Professor, Department of Public Health Sciences, Biocomplexity Institute & Initiative, UVA.
Bryan Lewis, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Pamela Murray-Tuite, Associate Professor, Civil and Environmental Engineering, Clemson University

Past and Current Students Supported on the Grant

Lijing Wang, PhD Computer Science, University of Virginia
Gloria Kang, MPH/PhD, Biomedical and Veterinary Sciences, Virginia Tech
Narges Dorratoltaj, MPH (Infectious Disease) / PhD (Biomedical and Veterinary Sciences), Virginia Tech
Meghendra Singh, MS Computer Science, Virginia Tech
Kunal Singh, MS Civil and Environmental Engineering, Virginia Tech
Bowen Shi, PhD Economics, Virginia Tech
Arminder Deol, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine

Selected Publications

Please see this document for selected publications supported by this project.