Integrating Uncertainty Analysis into Complex-System Modeling for Effective Public Policy I: Preliminary Findings

Public policy seeks to influence complex natural, social, and engineered systems to achieve desired outcomes. Effective public policies are those which combine good outcomes with high reliability such that their choice is robust to a wide range of possible uncertainties. Modeling these complex systems and their potential response to proposed policies can provide decision-makers with an objective basis for policy design. Critical to this design process is the development of rigorous methods to evaluate and rank modeled policy effectiveness in context of model uncertainty. A peer-reviewed complex system model of pandemic influenza propagation is used in a test case to illustrate the power of uncertainty-based public policy ranking. The networked-agent model calculates the effects of both social network-based community mitigation practices such as school closure and social distancing, and individually-based treatment options such as antiviral treatment and vaccination. A proposed uncertainty-based methodology is described. The roles of experimental design, input factor representation, and sensitivity analysis methodology illustrate a succinct methodology to rank pandemic disease control options. A combination of space-filling experimental designs, modeling policy options as continuous rather than categorical variables, and treed Gaussian process and polynomial chaos expansion-based sensitivity analysis are projected to yield a straightforward ranking of policy SAND 2011-3422C 2 options that is robust to the identified aleatory and epistemic model uncertainties. These methods should have the additional advantage of requiring relatively few model runs to achieve a consistent and defensible ranking. Phase I of the experimental work to demonstrate the proposed uncertainty-based methodology is outlined. This early work indicates that analysis of published modeling results can generate a robust composite ranking of public policy options for mitigation of pandemic influenza.