Embracing Complexity and Uncertainty: The Potential of Agent-Based Modeling for Environmental Planning and Policy

Abstract

Environmental degradation is often defined as a public goods problem, emerging when property rights are not clearly defined and costs are externalized to other parties. Proposing corrective regulation that enforces technological fixes or market-based approaches is often met with political resistance and doubts about its effectiveness. This is partly due to the complexity of interacting physical and socio-economic components that obscure the impacts of human decision-making on environmental functions. Yet, understanding the complexity of integrated human-environmental systems can help planners and stakeholders frame environmental problems, view their role in them and design effective policies to address them. This article examines the potential and limitations of agent-based models as metaphors that can contribute to the understanding of such complex systems, illustrating the argument with a hypothetical application in groundwater management.

Keywords:

• Agent-based modeling
• complexity
• uncertainty
• environmental planning
• adaptive management
• participatory modeling
• groundwater management

Acknowledgements

I am very grateful to Scott Campbell, Daniel Brown, Rick Riolo, and John Nystuen from the University of Michigan, to Charlie Hoch and Marty Jaffe from the University of Illinois at Chicago, to Asli Gocmen from the University of Wisconsin-Madison and to Paola Zellner Bassett for their guidance, encouragement and feedback on earlier versions of this article. Many thanks go to Royce Maniko and Robert Peven from the Monroe County Planning Department, Howard Reeves from US Geological Survey and Anna Michalak from the University of Michigan, who kindly provided data and their expert knowledge, and to three anonymous referees for their constructive criticism of this manuscript.

Notes

1. Popular platforms for the development of agent-based models include SWARM (http://www.swarm.org/wiki/Main_Page), REPAST (http://repast.sourceforge.net/), ASCAPE (http://www.brook.edu/es/dynamics/models/ascape/), MASON (http://cs.gmu.edu/∼eclab/projects/mason/), CORMAS (http://cormas.cirad.fr/indexeng.htm) and NetLogo (http://ccl.northwestern.edu/netlogo/) which are supported by various object-oriented programming languages (e.g., Java, Objective C). These are sets of libraries that define common classes, interfaces and primitives specifically designed for the programming of agent-based models.

2. Gregory Bateson (1988) wrote an inspiring essay around the “syllogism in grass” to illustrate the insights that can be derived from metaphors, to appreciate the beauty of nature and to understand its complexity.

3. Raster images represent a grid of picture elements (pixels) whose color reflects a value for that element, and which can be viewed in a monitor or printed in paper. Rasterization involves converting a vector graphic (a polygonal representation defined by mathematical equations) into a raster image.