* chiSIM v0.1. available at https://github.com/Repast/chiSIM
For more information on compiling and using the framework, see the Users Guide.
For more information on REPAST, see https://repast.github.io/repast_hpc.html and refer to Collier and North (2013).
Refer to Macal et al. (2018) for chiSIM description.
Refer to Kaligotla et al. (2018) for model description and documentation.
Research reported here was supported by the National Institute on Aging of the National Institutes of Health R01AG047869 (ST Lindau., PI). This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Institute on Aging.
This work was also supported by the U.S. Department of Energy under contract number DE-AC02-06CH11357. This work was completed in part with resources provided by the Research Computing Center at the University of Chicago (the Midway2 cluster), the Laboratory Computing Resource Center at Argonne National Laboratory (the Bebop cluster), and the University of Chicago (the Beagle supercomputer).
Collier, N., & North, M. (2013). Parallel agent-based simulation with Repast for High Performance Computing. SIMULATION, 89(10), 1215–1235. doi: 10.1177/0037549712462620.
Abstract: In the last decade, agent-based modeling and simulation (ABMS) has been applied to a variety of domains, demonstrating the potential of this technique to advance science, engineering, and policy analysis. However, realizing the full potential of ABMS to find breakthrough research results requires far greater computing capability than is available through current ABMS tools. The Repast for High Performance Computing (Repast HPC) project addresses this need by developing a useful and useable next-generation ABMS system explicitly focusing on larger-scale distributed computing platforms. Repast HPC is intended to smooth the path from small-scale simulations to large-scale distributed simulations through the use of a Logo-like system. This article’s contribution is its detailed presentation of the implementation of Repast HPC as a useful and usable framework, a complete ABMS platform developed explicitly for larger-scale distributed computing systems that leverages modern C++ techniques and the ReLogo language.
Macal, Charles M., Nicholson T. Collier, Jonathan Ozik, Eric R. Tatara, and John T. Murphy. "chiSIM: an agent-based simulation model of social interactions in a large urban area." In 2018 Winter Simulation Conference (WSC), pp. 810-820. IEEE, 2018, doi: 10.1109/WSC.2018.8632409.
Abstract: Cities are complex, dynamic, evolving adaptive systems comprised of people as well as interconnected physical infrastructure. Simulation modeling can help us understand and shape the evolution of our cities. In this paper, we describe an agent-based simulation modeling framework applied to Chicago, called chiSIM (for the Chicago Social Interaction Model). Each person residing in Chicago is represented as an agent in chiSIM; all places where people can be located in Chicago also are represented. The model simulates the movements of people between locations on an hourly basis during the course of a typical day. Co-located agents engage in various kinds of social interactions, such as exchanging information, engaging in business transactions, or simply sharing physical proximity. We discuss technical approaches to large-scale urban modeling including development of synthetic populations, efficiency gains through distributed processing, logging and analysis of simulation results, and visualization.
Kaligotla, Chaitanya, Jonathan Ozik, Nicholson Collier, Charles M. Macal, Stacy Lindau, Emily Abramsohn, and Elbert Huang. "Modeling an information-based community health intervention on the south side of Chicago." In 2018 Winter Simulation Conference (WSC), pp. 2600-2611. IEEE, 2018, doi: 10.1109/WSC.2018.8632525.
Abstract: We describe the development and application of a model that simulates the impact of CommunityRx, an information-based health intervention, on the utilization of community-based resources. The model includes a synthetic population of agents matching the sociodemographic characteristics of the South Side of Chicago, along with their activities and behaviors. We simulate the information-based intervention and model agent decision-making about using community resources to maintain health, based on a dynamic dosing of information about community resources, gained through interactions and experience. Through in silico experiments, our model aims to demonstrate the flow and spread of information from primary agents to others in the community, and through these dynamic interactions, the impact of an individual-level information intervention on resource utilization.