Complex Systems Analysis using Space-Time Information Systems and Model Transition Sensitivity Analysis

Real-world systems are dynamic, complex and geographic, yet many mathematical modeling tools do not evaluate sensitivity of results to underlying assumptions, and GIS do not adequately represent time. This presentation describes two new approaches: Space-Time Information Systems (STIS), and Model Transition Sensitivity Analysis (MTSA). Current GIS are based on spatial data models that inadequately characterize the temporal dimension needed for effective representation of complex systems. They do not deal readily with spacetime georeferencing nor space-time queries, and are best suited to “snapshots” of static systems. These deficiencies prompted many geographers to call for a “higher-dimensional GIS” (a STIS) to better represent space-time dynamics. When formulating models of complex systems, critical choices are made regarding model type and complexity. Model type is the mathematical approach employed, for example, a deterministic model versus a stochastic model. Model complexity is determined by the amount of abstraction and simplification employed during model construction. A growing body of work demonstrates that choice of model type and complexity has substantial impacts on simulation results and on model-based decisions. This paper briefly describes STIS and MTSA approaches that allow researchers to more effectively represent complex systems and to evaluate the sensitivity of their results to underlying assumptions.