Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance.

ABSTRACT: Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservati

SUBMITTER: Ligmann-Zielinska A 

PROVIDER: S-EPMC4207681 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature