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DOE OSTI2014

A continuation multilevel Monte Carlo algorithm

Here, we propose a novel Continuation Multi Level Monte Carlo (CMLMC) algorithm for weak approximation of stochastic models. The CMLMC algorithm solves the given approximation problem for a sequence of decreasing tolerances, ending when the required error tolerance is satisfied. CMLMC assumes discretization hierarchies that are defined a priori for each level and are geometrically refined across levels. Moreover, the actual choice of computational work across levels is based on parametric models for the average cost per sample and the corresponding variance and weak error. These parameters are calibrated using Bayesian estimation, taking particular notice of the deepest levels of the discretization hierarchy, where only few realizations are available to produce the estimates. The resulting CMLMC estimator exhibits a non-trivial splitting between bias and statistical contributions. We also show the asymptotic normality of the statistical error in the MLMC estimator and justify in this way our error estimate that allows prescribing both required accuracy and confidence in the final result. Our numerical results substantiate the above results and illustrate the corresponding computational savings in examples that are described in terms of differential equations either driven by random measures or with random coefficients.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

An Empirical Quantile Estimation Approach for Chance-Constrained Nonlinear Optimization Problems

We investigate an empirical quantile estimation approach to solve chance-constrained nonlinear optimization problems. Our approach is based on the reformulation of the chance constraint as an equivalent quantile constraint to provide stronger signals on the gradient. In this approach, the value of the quantile function is estimated empirically from samples drawn from the random parameters, and the gradient of the quantile function is estimated via a finite-difference approximation on top of the quantile-function-value estimation. We establish a convergence theory of this approach within the framework of an augmented Lagrangian method for solving general nonlinear constrained optimization problems. The foundation of the convergence analysis is a concentration property of the empirical quantile process, and the analysis is divided based on whether or not the quantile function is differentiable. In contrast to the sampling-and-smoothing approach used in the literature, the method developed in this paper does not involve any smoothing function and hence the quantile-function gradient approximation is easier to implement and there are less accuracy-control parameters to tune. Furthermore, we demonstrate the effectiveness of this approach and compare it with a smoothing method for the quantile-gradient estimation. Numerical investigation shows that the two approaches are competitive for certain problem instances.

Applied Probability↗

DOE OSTI2013

The mediation of environmental assessment's influence: What role for power?

Considerable empirical research has been conducted on why policy tools such as environmental assessment (EA) often appear to have 'little effect' (after Weiss) on policy decisions. This article revisits this debate but looks at a mediating factor that has received limited attention to-date in the context of EA - political power. Using a tripartite analytical framework, a comparative analysis of the influence and significance of power in mediating environmental policy integration is undertaken. Power is analysed, albeit partially, through an exploration of institutions that underpin social order. Empirically, the research examines the case of a new approach to policy-level EA (essentially a form of Strategic Environmental Assessment) developed by the World Bank and its trial application to urban environmental governance and planning in Dhaka mega-city, Bangladesh. The research results demonstrate that power was intimately involved in mediating the influence of the policy EA approach, in both positive (enabling) and negative (constraining) ways. It is suggested that the policy EA approach was ultimately a manifestation of a corporate strategy to maintain the powerful position of the World Bank as a leading authority on international development which focuses on knowledge generation. Furthermore, as constitutive of an institution and reflecting the worldviews of its proponents, the development of a new approach to EA also represents a significant power play. This leads us to, firstly, emphasise the concepts of strategy and intentionality in theorising how and why EA tools are employed, succeed and fail; and secondly, reflect on the reasons why power has received such limited attention to-date in EA scholarship. - Highlights: Black-Right-Pointing-Pointer Conducts empirical research on the neglected issue of power. Black-Right-Pointing-Pointer Employs an interpretation of power in which it is viewed as a productive phenomenon. Black-Right-Pointing-Pointer Analyses the influence of power in the trial application of a new approach to policy environmental assessment. Black-Right-Pointing-Pointer Demonstrates the importance of power dynamics in understanding the successes and failures of environmental assessment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

A Research Agenda for Radiation Oncology: Results of the Radiation Oncology Institute's Comprehensive Research Needs Assessment

Purpose: To promote the rational use of scarce research funding, scholars have developed methods for the systematic identification and prioritization of health research needs. The Radiation Oncology Institute commissioned an independent, comprehensive assessment of research needs for the advancement of radiation oncology care. Methods and Materials: The research needs assessment used a mixed-method, qualitative and quantitative social scientific approach, including structured interviews with diverse stakeholders, focus groups, surveys of American Society for Radiation Oncology (ASTRO) members, and a prioritization exercise using a modified Delphi technique. Results: Six co-equal priorities were identified: (1) Identify and develop communication strategies to help patients and others better understand radiation therapy; (2) Establish a set of quality indicators for major radiation oncology procedures and evaluate their use in radiation oncology delivery; (3) Identify best practices for the management of radiation toxicity and issues in cancer survivorship; (4) Conduct comparative effectiveness studies related to radiation therapy that consider clinical benefit, toxicity (including quality of life), and other outcomes; (5) Assess the value of radiation therapy; and (6) Develop a radiation oncology registry. Conclusions: To our knowledge, this prioritization exercise is the only comprehensive and methodologically rigorous assessment of research needs in the field of radiation oncology. Broad dissemination of these findings is critical to maximally leverage the impact of this work, particularly because grant funding decisions are often made by committees on which highly specialized disciplines such as radiation oncology are not well represented.