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.

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Projects

CNH-L

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.

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Comprehensive National Incident Management System (CNIMS)

Project Contacts: Chris Barrett, Madhav Marathe
Funding Agency: Defense Threat Reduction Agency DTRA

This project seeks to develop fundamental science that can drive the response to, and planning for, significant national incidents, from infectious disease outbreaks to catastrophic natural and human-initiated disasters. Real-world problems identified and posed by stakeholders drive this fundamental and applied research program. Past applied work has included real-time epidemic response, a variety of infectious disease forecasting, tabletop planning support, and detailed policy analysis for the response to outbreaks.

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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.

 

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EEID

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.

 

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GORDIAN

Project Contact: Bryan Lewis / Samarth Swarup
Funding Agency: Defense Advanced Research Projects Agency (DARPA) DARPA

GORDIAN seeks to generate realistic synthetic information for researchers to perform graph alignment and signal detection to assist in the identification of adversaries. Our research has focused on generating realistic behaviors like household expenditures, mobility, criminal activity, use of social media, and more. The resulting in silico society is consistent across agents and time and provides the “haystack” of a society’s digital exhaust into which we can embed the “needle” of an adversaries’ activities, to challenge our colleagues on the detection and identification teams within the larger project.

 

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Heavy Load Ahead

Project Contact: Samarth Swarup
Funding Agency: National Science Foundation National Science Foundation

This planning NSF grant supports work with public transportation managers in two Washington, D.C. metro-area counties to develop a proposal for a multi-level, integrated investigation into:

  • Individual- and organizational-level factors influencing the provision and use of information from crowdsourcing apps and other digital technologies in transportation
  • Cognitive impacts of perceived information overload on drivers, public managers, and planners (consumers and producers of information)
  • Effects of overload on transportation incidents and patterns
  • Effects on transportation system performance of different types, levels, and quality of crowd-sourced transport information

 

 

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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.

 

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Social Pathways

Project Contact: Achla Marathe / Samarth Swarup
Funding agency: National Institutes of Health (NIH)   National Institutes of Health National Institute of General Medical Sciences logo

The objective of this project is to incorporate social behavior into mathematical models of infectious disease transmission dynamics. 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.

 

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SocioNeticus

Project Contact: Mark Orr / Parantapa Bhattacharya
Funding Agency: Defense Advanced Research Projects Agency (DARPA) DARPA

This program develops simulation methods that integrate sociological, cognitive, and neural theory into at-scale, data-heavy social simulations using a newly developed agent-based modeling platform (the MATRIX). By virtue of the platform, this work also has implications for the foundations of sociological, cognitive, and neural theory.

 

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