The Emergence of Parmenidean Knowledge Synthesis in Spatiotemporal Modelling

• Activity Number: 128
• Type: Invited
• Date/Time: Monday, August 12, 2002 : 2:00 PM to 3:50 PM
• Sponsor: International Association of Mathematicial Geology (IAMG)
• Author(s): George Christakos*+
• Affiliation(s): University of North Carolina, Chapel Hill
• Keywords: Synthesis ; Statistics ; Nature ; Spatiotemporal ; Geostatistics ; Epistemic

Abstract

Knowledge synthesis (KS) refers to integration/processing of knowledge bases to obtain a realistic map of the natural phenomenon across space-time, assess important uncertainty sources, evaluate relevant risks, and make science-based decisions. KS introduces challenging theoretical and interpretive questions. A Parmenidean framework throws light on these questions by combining epistemic ideals with random field theory. Theoretical models developed for well-defined conceptual environment (e.g., mathematical equations of natural laws) are integrated with site-specific details of real environment (e.g., uncertain information sources). Parmenidean KS generates space-time maps and multi-point distributions using conditionalization and information principles, the choice of which depends on epistemic and physical features of the problem (Bayesian vs. non-Bayesian and entropic vs. non-entropic formulations are considered). No restriction is imposed on the shape of the distribution or the form of the predictor (non-Gaussian distributions, multiple-point statistics, and non-linear models are automatically incorporated).