Agent-Based Modeling

Overview

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.

Readings

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.
http://greenteapress.com/complexity/thinkcomplexity.pdf(link is external and opens in a new window)

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