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What is an agent-based model?
What is an agent-based model?
An agent-based model (ABM) is a computer simulation in which a collection of individual objects, called agents, interact with each other and result in emergent phenomena over time. ABM is a powerful tool for understanding complex systems, as it allows users to explore and represent the relationships among the interconnected system elements. At present, ABMs have been widely used in various STEM and social science fields to study complex systems, such as ecosystems, weather systems, and human societies.
Many software packets have been developed to create ABMs. The ABMs on this website are developed using NetLogo (Wilenski, 1999).
How do agent-based computer models support student learning?
How do agent-based computer models support student learning?
Our studies (Xiang & Passmore, 2010, 2015) have found that integrating ABM in science classrooms may
Effectively presenting target phenomena related to population dynamics to young students.
Exposing students’ personal models and prior understandings of the targeted natural phenomenon
Provoking and supporting students in 1) elaborating the target natural phenomenon, 2) abstracting Patterns, and 3) revising conceptual models
Supporting tangible and productive conversations among students, as well as between instructors and students.
We have also found that integrating ABM in science classrooms may successfully support three-dimensional learning (Xiang et al., 2022).
References and more resources relating to our work
Gelfand, A. (2013). The biology of interacting things: The intuitive power of agent-based models. Biomedical Computation Review, 1, 20-27.
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171-209.
Xiang, L., & Passmore, C. (2010). The Use of Agent-based Programmable Modeling Tool in 8th Grade Students’ Model-Based Inquiry. The Journal of the Research Center for Educational Technology, V6 (2), 130-147.
Xiang, L., & Passmore, C. (2015). A Framework for Model-Based Inquiry Through Agent-Based Programming. Journal of Science Education and Technology. V24 (2), 311-329. DOI: 10.1007/s10956-014-9534-4
Xiang, L. & Mitchell, A. (2019). Investigating Bark Beetle Outbreaks in Forest Ecosystems Using Computer Models. Science Scope, 46(6), 65-67. DOI: 10.2505/4/ss19_042_06_65
Pilny, A., Xiang, L. (Co-first author), Huber, C., Silberman, W., & Goatley-Soan, S. (2021). The Impact of Contact Tracing on the Spread of COVID-19: An Egocentric Agent-Based Model. Connections, 41(1), 25-46. https://doi.org/10.21307/connections-2021.022
Xiang, L., Goodpaster, S. & Mitchell, A. (2022). Supporting Three-dimensional Learning on Ecosystems Using an Agent-based Computer Model. Journal of Science Education and Technology. 31 (4), 473-489. https://doi.org/10.1007/s10956-022-09968-x
Xiang, L. & Diamond, S. (2022). Developing and Using Computer Models to Understand Epidemics. The Science Teacher, 89(3), 70-78. https://doi.org/10.1080/00368555.2022.12293672
Xiang, L., Keck, J. W., Gallimore, J., Sakhaei, A., Loh, E., & Berry, S. M. (2025). Wastewater Infrastructure as a Public Health Tool: Agent-Based Modeling of Surveillance Strategies in a COVID-19 Context. Systems, 13(12), 1093. https://doi.org/10.3390/systems13121093
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