Abstract

The talk outlines a high resolution interaction-based approach to support policy informatics for large co-evolving socio-technical networks. Such systems consist of a large number of interacting physical, technological, and human/societal components. Quantitative changes in HPC including faster machines and service-oriented software have created qualitative changes in the way information can be integrated in analysis of these large heterogeneous systems and supporting policy makers as they consider the pros and cons of various decision choices.

Agent-oriented simulation is an example of an interaction based computational technique useful for reasoning about biological, information and social networks. Developing scalable models raises important computational and conceptual issues, including computational efficiency, necessity of detailed representation and uncertainty quantification.

The talk will describe the development of high performance computing based crises management system called Comprehensive National Incident Management System (CNIMS). As an illustrative case study we will describe how CNIMS can be used for developing a scalable computer assisted decision support system for pandemic planning and response. We will conclude by discussing challenging validation and verification issues that arise when developing such models.