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|A6 - Agent-Based Modelling of Land-use Effects I|
|Time:||8:00 AM - 10:00 AM|
|Room:||Ball Room 2|
|Session Chair(s):||J. Gary Polhill, Alessandro Gimona, and Richard Aspinall|
|Session Abstract: One of the main themes of the Global Land Project concerns the understanding of the effects of human land use activities in altering the structure and functioning of terrestrial landscapes and ecosystems. Improved understanding of the decision making processes related to land use management provides the foundation for evaluating the interactions between factors influencing human activities and feedbacks within the coupled human-environment system. Modeling can contribute to better understanding of these systems. It is now generally accepted that to adequately understand the complex dynamics of landscapes, it is often necessary for models thereof to integrate the human social processes embedded within them. In so doing, a spectrum of approaches can be applied, from analytical through to narrative; quantitative to qualitative. In the social sciences, agent-based (akin to individual-based) modeling has been proposed as a ‘third way’: formal and yet descriptive in its representations. Agent-based modeling has been applied for some time now to the study of land use and cover change by various researchers. This symposium will consider developments in coupled human-natural system modeling using agent-based simulation, from the perspective of land use effects on population dynamics and ecosystems processes and/or services at the landscape scale. It is thus directly relevant to the broader US-IALE conference theme of Coupling Humans and Complex Ecological Landscapes. We will be inviting papers on the following topics, among others, with a view to showcasing the various ways in which agent-based modeling can contribute to the an integrated understanding of the social and the ecological: Case studies of agent-based modeling in natural resource management and policy; Calibrating and validating coupled agent-based/ecosystem models; Developing theory using reduced form/conceptual agent-based models; Disseminating agent-based models to the scientific community and beyond; Lessons for and critiques of ABM from other efforts in coupled SES modeling.|
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