Within the study of computational security policies arise diverse complex challenges for which solutions are still pursued.
Here we propose a conceptual and computational framework where policies are formulated as binary trees, using a simulation-optimization approach.
We examine continual computation, reasoning policies that capture a broader conception of problem by considering the proactive allocation of computational resources to potential future challenges.
We use Largest Lyapunov exponents to characterize stability and use Gaussian process model as an inexpensive surrogate, facilitating computational efficiency in policy optimization under uncertainty.
The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.
While the approach of using a detailed chemistry-transport model is feasible for specific policy assessments, computational requirements preclude the assessment of a wide range of policy options, quantification of uncertainty, sensitivity studies, or a timely response to policy questions.
Some are cross-disciplinary or specializations, like biosystems engineering, clinical nurse leadership, computational biology and international policy analysis.
In order to balance requirements for objectivity with the need for nuanced readings of policy texts, computational linguistics software [ 62] is used here to offer objective, machine based findings about the presence of explicit references to vulnerable groups.
The introduction of a systematic approach for exploring alternative policies using a computational methodology has the potential to help in the decomposition of complex problems into subproblems with more manageable size, and can accelerate and improve the effectiveness of the policy-making process.
An efficient computational architecture to compute the resource allocation corresponding to a subset of these policies is presented in the following subsection.
