Agent-based models

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 about a complex system, 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 sociaties, .

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?

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 instructor and students.

References and more resources:

  • 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. 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