Modeling Population-Environment Interactions in a World Heritage Site

The work proposed here aims to accomplish two goals. First, using available data supplemented with expert opinion and qualitative data to be collected as part of this project, we will develop an ABM model that has sub-models that incorporate complexity from global tourism, climate change, migration streams, urbanization, invasive terrestrial species, land use/land cover change (LULCC), and the human-environment interactions of a fragile, and increasingly developed, coastal environment. This modeling will not only be a "proof of concept" approach, but will also have results of interest in their own right. Second, we will tackle head-on the skepticism of statistically sophisticated social scientists toward ABM-type models. David Guilkey, a well-known econometrician at the University of North Carolina and a skeptic about the utility of ABMs in causal analysis, will be part of the team designing the ABM. The idea is to highlight all the points at which the ABM we develop is using, data, rules & tools, that would be considered questionable from an econometric perspective, and then, working as a team, design a data collection and analysis approach that could be analyzed both by ABM and econometric experts to see the extent to which the ABM and statistical approaches give similar results, and if not, why not. In short, this is the statistical and modeling equivalent of a "cook off." Funding for the data collection and analyses for this "cook off" is not part of the present project; rather we would apply to NIH, and possibly NSF for the necessary funds toward the end of this project. The significance of both the proof of concept and being able to bridge the chasm between those using ABM-type models and more conventional statistical models is that serious progress in understanding complex systems will require a better understanding of the strengths and weakness of both approaches, and preferably a functional combination of the two.

Principal Investigator: Stephen J. Walsh

CPC Fellow Investigator: David K. Guilkey , Ronald R. Rindfuss

Funding Source: James S. McDonnell Foundation

Grant Number: 220020269

Funding Period: 9/1/2011 - 9/1/2015