Geovisualizing, representing, analyzing, modeling, and simulating

Complex Human-Environment Systems (CHES)

for improved envisioning, understanding, and planning



(4-D) Agent-Based Modeling (ABM)


What is an agent-based model (ABM)? An agent-based model is a computerized, digital model that aims to simulate a number of decision-makers (agents) and institutions, which change status and/or interact with one another over time according to a set of prescribed rules. By and large, all agents are often embedded in a hierarchical structure, dwell on and interact with an (often) dynamic landscape, and learn and adapt in response to changes in other agents and the environment. Building on a fundamental philosophy of methodological individualism, agent-based modelers advocate a focus on the uniqueness of individual agents, local environment, and multiple dynamical agent-agent or agent-environment interactions, which may account for many complexity features. Therefore, agent-based modelers warn against aggregating individual decisions or local-level characteristics so as to avoid potential misleading results.

Agent-based modeling is a major bottom-up tool powerful to understand complexity in many theoretical and empirical systems, to perform space-time analysis, and to represent or envision many complex landscape processes or human-environment interactions. Currently, the CHES group is pushing toward 4-D (x, y, z, and time) ABM (see this example), which is an extention of traditional 3-D (x, y, and time) ABM. We will soon be developing a participatory ABM as part of our NSF project, which involves stakeholders in an iterative process of model development for information sharing, collective learning and exchange of ideas on a given concrete issue among researchers and other stakeholders. Below is a list of exemplar articles that help illustrate the uniqueness and power of ABM.


Readings and References:


An, L., A. Zvoleff, J. Liu, and W. Axinn (2014). Agent based modeling in coupled human and natural systems (CHANS): Lessons from a comparative analysis. Annals of Association of American Geographers 104(4):723-745.

An, L. (2012). Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecological Modelling 229(24):25-36.

An, L., M. Linderman, J. Qi, A. Shortridge, and J. Liu (2005). Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multi-scale integration. Annals of Association of American Geographers 95(1):54-79.

Chin, A., L. An, J. Florsheim, L. Laurencio, R. Marston, A. Parker, G. Simon, and E. Wohl (2016). Feedbacks in human-landscape systems: lessons from the Waldo Canyon Fire of Colorado, USA. Geomorphology 252(2016): 40-50.

Epstein J.M., and R. Axtell (1996). Growing Artificial Societies: Social Science From the Bottom Up. Washington: Brookings Institute.

Epstein J.M. (2006). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, New Jersey: Princeton University Press.

Parker, D.C., S.M. Manson, M.A. Janssen, M.J. Hoffmann, and P. Deadman (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers 93: 314ĘC37.

Sullivan, A., A.M. York, and L. An (2018). Which perspective of institutional change best fits empirical data? An agent-based model comparison of rational choice and cultural diffusion in invasive plant management. Journal of Artificial Societies and Social Simulation 21(1):5.


Examples, Models, and/or Documents:


CHANS-ABM Submodels

Pseudo-code for CHANS ABM

Chitwan ABM

Introduction to Chitwan ABM- Presentation Sep. 27, 2013

River Geomorphology ABMs

GitHub - A Step-by-Step Guide