Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States

Authored by Tom P Evans, Steven M. Manson

Date Published: 2007-12-26

DOI: 10.1073/pnas.0705802104

Sponsors: United States National Aeronautics and Space Administration (NASA) Carnegie Mellon University United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatan of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general.

Tags

Agent-based model Decision Making Bounded rationality landscape management land change