Agent-Based Modeling









Agent-based models are computer simulations used to study the interactions between people, things, places, and time. They are stochastic models built from the bottom up meaning individual agents (often people in epidemiology) are assigned certain attributes. The agents are programmed to behave and interact with other agents and the environment in certain ways. These interactions produce emergent effects that may differ from effects of individual agents. Agent-based modeling differs from traditional, regression-based methods in that, like systems dynamics modeling, it allows for the exploration of complex systems that display non-independence of individuals and feedback loops in causal mechanisms. It is not limited to observed data and can be used to model the counterfactual or experiments that may be impossible or unethical to conduct in the real world. However, agent-based modeling is not without its limitations. The data parameters (such as the reproductive rate for infectious diseases) are often difficult to find in the literature. In addition, the validity of the model can be difficult to assess, particularly when modeling unobserved associations. Overall, agent-based models provide an additional tool for assessing the impacts of exposures on outcomes. It is particularly useful when interrelatedness, reciprocity, and feedback loops are known or suspected to exist or when real world experiments are not possible.


Textbooks & Chapters

Think Complexity, by Allen Downy. Not written with Epidemiologists or health care professionals in mind, but this excellent, readable book by Allen Downey explains and provides examples of many of the originating theories and tenets of complex adaptive systems and agent based modeling, such as Thomas Schelling’s “Dynamic Models of Segregation,” Stephen Wolfram’s work in cellular automata, as well as fractals, and game theory. Chapters 1,2 6,7 and 10 are particularly relevant and interesting. Book is free online.

Where Medicine Went Wrong: Rediscovering the Path to Complexity, by Bruce J West. World Scientific Publishing Company; 1st edition (October 9, 2006).
Although this text does not deal specifically with agent based models, this interesting book addresses the potential faults in our traditional way of modeling many physiologic processes and disease states; i.e., in trying to force a Gaussian normality on what are inherently more complex systems. Written for clinicians, but a background knowledge of physics is very helpful.

Methodological Articles


Systems science methods in public health: dynamics, networks, and agents
Author(s): DA Luke and KA Stamatakis
Journal: Annual review of public health
Year published: 2012

Social network analysis and agent-based modeling in social epidemiology
Author(s): AM El-Sayed, P Scarborough, L Seeman, and S Galea
Journal: Epidemiologic Perspectives & Innovations
Year published: 2012

Causal thinking and complex system approaches in epidemiology

Author(s): S Galea, M Riddle, and GA Kaplan.
Journal: International journal of epidemiology 39.1 (2010): 97-106.
Year published: 2010

Application Articles

Agent-based simulation platforms: review and development recommendations
Author(s): SF Railsback, SL Lytinen, SK Jackson
Journal: Simulation
Year published: 2006

Modeling and analysis of global epidemiology of avian influenza

Author(s): DM Rao, A Chernyakhovsky and V Rao
Journal: Environmental Modelling & Software
Year published: 2009

Modeling targeted layered containment of an influenza pandemic in the United States

Author(s): ME Halloran, N Ferguson, S Eubank, et al.
Journal: Proceedings of the National Academy of Sciences
Year published: 2008

Conceptual approaches to the study of health disparities

Author(s): MG Orr and CR Evans
Journal: Research in Human Development
Year published: 2011

The role of subway travel in an influenza epidemic: a New York City simulation

Author(s): P Cooley, S Brown, J Cajka, et al.
Journal: Journal of Urban Health
Year published: 2011

A spatial agent-based model for the simulation of adults’ daily walking within a city

Author(s): Y Yang, AV Diez Roux, AH Auchincloss, DA Rodriguez and DG Brown
Journal: American journal of preventive medicine
Year published: 2011

Using simple agent-based modeling to inform and enhance neighborhood walkability

Author(s): H Badland, M White, G Macaulay, et al.
Journal: International Journal of Health Geographics
Year published: 2013



ABM blog written by Jeff Schank (UC Davis psychology professor). It is a clearinghouse of information, including lists of researchers using ABM, conferences, and additional websites.

On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences, a website created by Robert Axelrod and Leigh Tesfatsion (Iowa State University).
A comprehensive resource for agent based models: links to original articles and book chapters that inspired agent based thinking, as well links to articles encompassing both methodology and example applications. Links to modeling software as well.

A presentation by Bruce West to a group of clinicians, summarizing some of the concepts in his book Where Medicine Went Wrong: Rediscovering the Path to Complexity.


There is a course on Agent-Based Modeling offered as part of the Epidemiology and Population Based Health Summer Institute at Columbia (EPIC) beginning June 1, 2022. 

Open ABM – forms part of Computational Modeling for SocioEcological Science (CoMSES Net), a network dedicated to support and expand the development and use of computational modeling in the social and life sciences. Not specifically health related.

Host/program: Massachusetts Institute of Technology
Course format: Both
Software used: AnyLogic

Model Thinking

Host/program: Coursera/University of Michigan
Next offering: Self-paced
Course format: Online
Software used: NetLogo

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