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Workshop on Agent-Based Modeling of Complex Spatial Systems 14-16 th April 2007

Agent-Based GeoComputation is not about building models. It is, ultimately, about the art and science of addressing complex spatial problems with computers; for sound inference on matters of importance. An appropriate model is necessary, but not sufficient. Consider the complementary laboratory tools and practices sufficient for inference with agent-based computational laboratory research for effective responses to complex geographic emergencies. Our methodological advances in computational laboratory tools and practices have been driven by a potential pandemic influenza emergency due to an unusually deadly (H5N1) influenza pandemic spreading worldwide among migrating, wild, and domestic birds. H5N1 influenza currently kills more than half of its human victims. If it adapts to spread easily among humans, it could lead to a global influenza pandemic far more severe than the flu pandemic of 1918-1919. Our research uses complementary computational laboratory tools to evaluate relative geographical risks via patterns of inter-city hierarchical diffusion of pandemic influenza, and to optimize geographic deployment of limited resources to inhibit the inter-city spread of pandemic influenza. The crucial substantive challenge harnesses advantages of geographic structure to clarify relative risks and to amplify the protective leverage of available resources, in order to target advanced preparation, minimize mortality, and gain time for vaccine production and administration. The crucial methodological challenge is to conduct sufficient sensitivity and risk analyses of the model and of recommended interventions to provide as much information as possible for decision-makers, in the event that model results must be used to recommend deployments of interventions during a pandemic emergency. The epistemological insights and methodological extensions driven by responding to this potential emergency provide helpful guidelines for effective calibrations, experimental designs, optimizations, and risk analyses of spatial agent-based computational laboratory research.