Time Dependent Variance-based Sensitivity Analysis of Development Aggregation Generated by Heterogeneous Land Use Agents

Arika Ligmann-Zielinska1 and Libo Sun2
1.Department of Geography Environmental Science and Policy Program,Michigan State University, East Lasing, USA
2.Department of Statistics and Probability, Michigan State University, East Lasing, USA
1.ligmannz@msu.edu; 2.sunlibo@msu.edu

Abstract: Agent-based models have been recognized as computational laboratories furnishing spatial scientists with a plausible exploratory apparatus for learning about land use dynamics through an explicit representation of human behavior. At the same time research suggests that the utility of agent-based modeling has been hampered by a limited understanding of the decision processes involving a wide array of stakeholders with different perceptions and preferences. Therefore, it is critically important to offer new tools for a more comprehensive inspection of uncertainties related to the interrelationships between individual choices and land development patterns. In this paper, we propose a new approach to evaluating agent behavioral uncertainty using time dependent variance-based global sensitivity analysis. The method produces time series of first order sensitivity indices that allocate the variance of development patterning to two heterogeneous behavioral features: risk perceptions, quantified through attitude utility functions, and land preferences, in the form of weights assigned to different decision criteria. We experiment with three ABM scenarios that emphasize the various decision components. The scenarios utilize a fixed number of parameters with changing distributions reflecting the behavioral characteristic under consideration. Outcome maps for each time step are summarized using the aggregation index, which is further employed in sensitivity computation. The resulting sensitivity indices are plotted against time to track the impact of input conditions on land use compactness. The comparisons of the plots reveal varying sensitivity trajectories that depend on the modified decision rule.

Keywords: sensitivity analysis; agent-based model; behavioral heterogeneity

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