Creating agents and landscapes for multiagent systems from random samples

Abstract

An important goal of modeling human-environment interactions is to provide scientific information to policymakers and stakeholders in order to better support their planning and decision-making processes. Modern technologies in the fields of GIS and data processing, together with an increasing amount of accessible information, have the potential to meet the varying information needs of policymakers and stakeholders. Multiagent modeling holds the promise of providing an enhanced collaborative framework in which planners, modelers, and stakeholders may learn and interact. The fulfillment of this promise, however, depends on the empirical parameterization of multiagent models. Although multiagent models have been widely applied in experimental and hypothetical settings, only few studies have strong linkages to empirical data and the literature on methods of empirical parameterization is still limited. This paper presents a straightforward approach to parameterize multiagent models in applied development research. The parameterization uses a common sampling frame to randomly select observation units for both biophysical measurements and socioeconomic surveys. The biophysical measurements, i.e., soil properties in this study, are then extrapolated over the landscape using multiple regressions and a digital elevation model. The socioeconomic surveys are used to estimate probability functions for key characteristics of human actors, which are then assigned to the model agents with Monte Carlo techniques. This approach generates a landscape and agent populations that are robust and statistically consistent with empirical observations. Copyright © 2006 by the author(s). Published here under license by the Resilience Alliance.