Another complexity tool that provides a rich understanding of the world around us is Geographic Information Systems (GIS). GIS gives us the ability to examine large amounts of spatial patterns simultaneously. In addition, by combining GIS with agent-based modeling we can begin to not only understand what patterns exist in the world, but how they were created.
For instance, the Sluce project at the University of Michigan has built computational models of how residents decide where to live in Southeastern Michigan, and how these decisions affect the patterns of land-use and land-change in this area. But this combination of geography and computational modeling is not limited to just residential housing preferences, but instead is useful for any geographically sensitive product. For example, there has also been working combining models of commuting behavior with geographic information to choose good locations for future gas stations.
- Brown, Daniel G., Page, Scott E., Riolo, Rick, Zellner, Moira, and William Rand (2005) "Path dependence and the validation of agent-based spatial models of land use." International Journal of Geographical Information Science, Special Issue on Land Use Dynamics 19(2): 153-174
- Heppenstall, Alison, Evans, Andrew and Birkin, Mark (2006). 'Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets'. Journal of Artificial Societies and Social Simulation 9(3)2