Spatially Explicit Agent-based Modeling

Spatially explicit agent-based modeling (SEABM) is a computational approach that employs agents, environments, and interactions among them to represent dynamics in complex adaptive spatial systems (CASS), exhibiting nonlinear, emergent, self-organized, and adaptive characteristics. SEABM provides a flexible framework that allows integration of domain-specific data, models, and knowledge (e.g., GIS, statistical models, and land-use models) and, thus, serves as an integrative platform for the modeling of CASS.

A generic SEABM framework, GAIA (Geographically Aware Intelligent Agents) has been developed, associated with a software package (GAIASP, GAIA Simulation Package). The GAIA framework is designed to represent individuals that are contextually aware, knowledge-driven, and adaptive within spatially explicit environments. Further, the software package GAIASP has been extended to the cyberinfrastructure environment to better leverage increasingly available computing resources for SEABM, extremely computationally intensive particularly for large-scale spatial problems. Parallel and service-oriented computing have been used to enable SEABM within the cyberinfrastructure.

Project Team: Eric Shook, Wenwu Tang, Shaowen Wang