Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for Multidisciplinary and Multiscale Integration

Traditional approaches to studying human–environment interactions often ignore individual-level information, do not account for complexities, or fail to integrate cross-scale or cross-discipline data and methods, thus, in many situations, resulting in a great loss in predictive or explanatory power. This article reports on the development, implementation, validation, and results of an agent-based spatial model that addresses such issues. Using data from Wolong Nature Reserve for giant pandas (China), the model simulates the impact of the growing rural population on the forests and panda habitat. The households in Wolong follow a traditional rural lifestyle, in which fuelwood consumption has been shown to cause panda habitat degradation. By tracking the life history of individual persons and the dynamics of households, this model equips household agents with ‘‘knowledge’’ about themselves, other agents, and the environment and allows individual agents to interact with each other and the environment through their activities in accordance with a set of artificial-intelligence rules. The households and environment coevolve over time and space, resulting in macroscopic human and habitat dynamics. The results from the model may have value for understanding the roles of socioeconomic and demographic factors, for identifying particular areas of special concern, and for conservation policy making. In addition to the specific results of the study, the general approach described here may provide researchers with a useful general framework to capture complex human–environment interactions, to incorporate individual-level information, and to help integrate multidisciplinary research efforts, theories, data, and methods across varying spatial and temporal scales.