William Becker

BlueFox Data

An overview of the "COINr" R package, which is for building and analysing composite indicators.

Abstract submission is now open for the 10th International Conference on Sensitivity Analysis of Model Output (SAMO). The conference will be held at Florida State University, Tallahassee, Florida. The dates of the conference are March 14 -16, 2022, at the Florida State Conference Center. See: https://samo2022.math.fsu.edu/

The call for measuring synergies and trade-offs between water, energy, and food is increasing worldwide. This article presents the development and application of a country-level index that has been calculated for 181 nations using open databases. Following an assessment of 87 water-, energy-, and food-related indicators, 21 were selected to constit...

There are over 650 million people in Africa who have no access to electricity; this is in sharp contrast to the continent's vast untapped renewable energy potential and due largely to the historical lack of investments in energy infrastructure. New investments in decentralised power generation within Sub-Saharan Africa play a progressively importan...

Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information...

In this data article, we present datasets from the construction of a composite indicator, the Photovoltaic Decentralised Energy Investment (PV-DEI) index, presented in detail in [1]. This article consists of the comprehensive energy-related data collected in practice from several sources, and from the outputs of the methodology described in [1]. Th...

In this paper, we discuss the sensitivity analysis of model response when the uncertain model inputs are not independent of one other. In this case, two different kinds of sensitivity indices can be evaluated: (i) the sensitivity indices that account for the dependence/correlation of an input or group of inputs with the remainder and (ii) the sensi...

Asia and Europe have made connectivity between people, businesses and institutions a top political priority in the frame of the Asia-Europe Meeting (ASEM) intergovernmental cooperation forum. In the ASEM context, policy leaders agreed that improving connectivity between countries should contribute to achieve the Sustainable Development Goals. Conne...

Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion o...

The Composite Indicator Analysis and Optimization (CIAO) tool v.2 is an expansion of the automated Matlab menu-version of the approach presented by Becker et al. (2017)* for the advanced assessment of Composite Indicators (CIs). The CIAO tool allows users to: 1. Perform a detailed examination of the linear and nonlinear relationships among (i) the...

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of...

The Europe 2020 Strategy was launched by the European Commission in 2010 to promote smart, sustainable, and inclusive growth across EU member states. As the strategy draws to a close in 2020 and is superseded by the Sustainable Development Goals and the Green Deal, this work aims to assess the progress made over the last decade, and to carry forwar...

The Water-Energy-Food (WEF) nexus has, in the past decade, gained prominence as an approach for assessing integrated resource management. One challenge related to the WEF nexus approach is how to represent and monitor it since a system that includes water-, energy- and food-related parameters is complex. Not only are these resources quantified util...

Sensitivity analysis helps decision-makers to understand how a given model output responds when there is variation in the model inputs. One of the most authoritative measures in global sensitivity analysis is the Sobol' total-order index ($T_i$), which can be computed with several different estimators. Although previous comparisons exist, it is har...

These are demo scripts to help reproduce the work in my paper: "Metafunctions for benchmarking in sensitivity analysis", by William Becker (Reliability Engineering and System Safety) This code is available in the interests of transparency and reproducability. It was written for specific experiments, rather than for general public release, and does...

Comparison studies of global sensitivity analysis (GSA) approaches are limited in that they are performed on a single model or a small set of test functions, with a limited set of sample sizes and dimensionalities. This work introduces a flexible ‘metafunction’ framework to benchmarking which randomly generates test problems of varying dimensionali...

The EU Bioeconomy Strategy, updated in 2018, in its Action Plan pledges an EU-wide, internationally coherent monitoring system to track economic, environmental and social progress towards a sustainable bioeconomy. This paper presents the approach taken by the European Commission’s (EC) Joint Research Centre (JRC) to develop such a system. To accomp...

This Chapter presents a set of quantitative modelling approaches, connected to various steps of the policy cycle, that aim at helping policy-makers and all social actors involved, by providing a scientific sound framework for a systematic, coherent and transparent analysis. Practical guidelines for structuring policy problems by using uncertainty a...

The COIN Tool is a free Microsoft Excel-based tool designed to help users from research institutions, international organisations, European Union institutions, national and local governments, among others, in the process of building and analysing composite indicators. It was developed by the European Commission's Competence Centre on Composite Indi...

Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question ‘How uncertain is the prediction?’ Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their...

This study employs statistical methods to further develop the Macroeconomic Imbalance Procedure (MIP) Scoreboard in line with the European Parliaments regulation of 2011, so as to make it an even better information aggregation, decision making and communication tool. The contribution to the literature and ongoing policy debate is threefold. First,...

The Composite Indicator Analysis and Optimization (CIAO) tool v.1 is an automated Matlab menu-version of the approach presented by Becker et al. (2017)* for the advanced assessment of Composite Indicators (CIs). The CIAO tool allows users to: 1. Perform a detailed examination of the linear and nonlinear relationships among (i) the set of indicator...

The present report was produced on the initiative of the European Union with the aim of providing a scientific-based contribution to the policy discussions on connectivity in the framework of the Asia-Europe Meeting (ASEM). The key findings are: i) connectivity can help to achieve the Sustainable Development Goals, but challenges lie ahead; ii) pol...

Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve...

One of the most powerful and versatile system identification frameworks of the last three decades is the NARMAX/NARX 1 approach, which is based on a nonlinear discrete-time representation. Recent advances in machine learning have motivated new functional forms for the NARX model, including one based on Gaussian processes (GPs), which is the focus o...

Sensitivity analysis is an essential tool in the development of robust models for engineering, physical sciences, economics and policy-making, but typically requires running the model a large number of times in order to estimate sensitivity measures. While statistical emulators allow sensitivity analysis even on complex models, they only perform we...

Appropriate land management can be an effective approach to improving water quantity regulation. There is, however, a need to identify both where measures are most needed and where they may be most effective. The water retention index (WRI) was developed with this goal in mind. The WRI is a composite indicator which takes into account parameters re...

One of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in...

One of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in...

Composite indicators are very popular tools for assessing and ranking countries and institutions in terms of environmental performance, sustainability, and other complex concepts that are not directly measurable. Because of the stakes that come with the media attention of these tools, a word of caution is warranted. One common misconception relates...

Multidimensional measures (often termed composite indicators) are popular tools in the public discourse for assessing the performance of countries on human development, perceived corruption, innovation, competitiveness, or other complex phenomena. These measures combine a set of variables using an aggregation formula, which is often a weighted arit...

Estimation of correlation ratios, decomposition into correlated and uncorrelated parts, and optimisation of weights. REQUIRES ML/STATS TOOLBOX AND OPTIMISATION TOOLBOX FOR SOME OPERATIONS.

This was a keynote presentation given at the Sensitivity Analysis of Model Output (SAMO) conference in 2016. It shows the exploration and optimisation of composite indicator weights using nonlinear regression. The examples used are university rankings.

Sensitivity analysis typically requires many thousands of model runs in order to estimate sensitivity measures via Monte Carlo methods. Emulators offer a solution via modelling the response surface of the model using approaches taken from statistics and machine learning. Gaussian processes in particular represent a state-of-the-art probabilistic em...

Water quantity management remains an issue in Europe, resulting in numerous periods of regional flooding and drought annually. Land management can be a powerful natural approach to reduce the impacts of such fluctuations in water flow through the landscape, especially in upstream source areas. The Water Retention Index has been developed as a tool...

A flight parameter sensor simulation model was developed to assess the conservatism of the landing gear component loads calculated using a typical hard-landing analysis process. Conservatism exists due to factors of safety that are incorporated into any hard-landing analysis process to account for uncertainty in the measurement of certain flight pa...

SIMLAB 4.0 is a comprehensive standalone software package for performing global sensitivity analysis. Several sampling strategies and sensitivity measures are available. SIMLAB includes the most recent variance-based formulas for first order and totalorder sensitivity indices, graphical methods as well as more classical methods. The peculiarity of...

Multi-dimensional measures (often termed composite indicators) are popular tools in the public discourse for assessing the performance of countries on human development, perceived corruption, innovation, competitiveness, or other complex phenomena. These measures combine a set of variables using an aggregation formula, which is often a weighted ari...

This document presents several alternatives for an exploratory analysis of price convergence (and disparity) within the EU28. Both cross-sectional (across countries) and dynamic (over time) analyses of the evolution of price levels have been undertaken. The results show that prices are still very different across Member States, particularly in serv...

Composite indicators are aggregations of measurable variables (indicators) that aim to quantify underlying concepts that are not directly observable, such as competitiveness, freedom of press or climate hazards. Composite indicators, otherwise referred to as performance indices, are employed for many purposes, including policy monitoring. Sensitivi...

A recent approach to surrogate modelling, called dynamic trees, uses regression trees to partition the input space, and fits simple constant or linear models in each " leaf " (region of the input space). This article aims to investigate the applicability of dynamic trees in sensitivity analysis, in particular on high dimensional problems at low sam...

A robust composite indicator was developed to assess the capacity of the landscape to regulate and retain water passing through it at Pan-European scale. The “Water Retention Index” (WRI) takes into account the role of interception by vegetation, the water-holding capacity of the soil, and the relative capacity of the bedrock to allow percolation o...

This paper investigates variable-selection procedures in regression that make use of global sensitivity analysis. The approach is combined with existing algorithms and it is applied to the time series regression designs proposed by Hoover and Perez. A comparison of an algorithm employing global sensitivity analysis and the (optimized) algorithm of...

Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional...

Sensitivity analysis allows one to investigate how changes in input parameters to a system affect the output. When computational expense is a concern, metamodels such as Gaussian processes can offer considerable computational savings over Monte Carlo methods, albeit at the expense of introducing a data modelling problem. In particular, Gaussian pro...

A major problem in uncertainty and sensitivity analysis is that the computational cost of propagating probabilistic uncertainty through large nonlinear models can be prohibitive when using conventional methods (such as Monte Carlo methods). A powerful solution to this problem is to use an emulator, which is a mathematical representation of the mode...

We compare the convergence properties of two different quasi-random sampling designs – Sobolʼs quasi-Monte Carlo, and Latin supercube sampling in variance-based global sensitivity analysis. We use the non-monotonic V-function of Sobolʼ as base case-study, and compare the performance of both sampling strategies at increasing sample size and dimensio...

This paper forms the second in a short sequence considering the system identification problem for hysteretic systems. The basic model for parameter estimation is assumed to be the Bouc–Wen model as this has proved particularly versatile in the past. Previous work on the Bouc–Wen system has shown that the system response is more sensitive to some pa...

Non-accidental head injury in infants, or shaken baby syndrome, is a highly controversial and disputed topic. Biomechanical studies often suggest that shaking alone cannot cause the classical symptoms, yet many medical experts believe the contrary. Researchers have turned to finite element modelling for a more detailed understanding of the interact...

We compare the convergence properties of two different quasi-random sampling designs-Sobol's quasi-Monte Carlo, and Latin super-cube sampling in variance-based global sensitivity analysis. We use the non-monotonic V-function of Sobol' as base case-study, and compare the performance of both sampling strategies at increasing sample size and dimension...

Understanding the mechanics of the aortic valve has been a focus of attention for many years in the biomechanics literature, with the aim of improving the longevity of prosthetic replacements. Finite element models have been extensively used to investigate stresses and deformations in the valve in considerable detail. However, the effect of uncerta...

As the sophistication of finite element models increases, the need to investigate the effects of uncertainties in model inputs becomes increasingly important in the interests of developing a robust model. A novel design of an unmanned airship is under development at the Politecnico di Torino, Italy. Structural and fluid–structure interaction models...

Uncertainty analysis in computer models has seen a rise in interest in recent years as a result of the increased complexity of (and dependence on) computer models in the design process. A major problem however, is that the computational cost of propagating uncertainty through large nonlinear models can be prohibitive using conventional methods (suc...

Sensitivity analysis allows one to investigate how changes in input parameters to a system affect the output. Many techniques exist to deal with this, though one method that has seen considerable interest in recent years is the use of Gaussian process-based metamodels. A Gaussian process is constructed from a number of model samples, and sensitivit...

A Flight Parameter Sensor Simulation (FPSS) model has been developed to assess the conservatism of the landing gear loads calculated using a hard landing analysis process. Conservatism exists due to factors of safety that are added to the hard landing analysis process to account for uncertainty in the measurement of certain flight parameters. The F...