text:
20RespyFrameworkSimulation,
	title = {respy - {A} {Framework} for the {Simulation} and {Estimation} of {Eckstein}-{Keane}-{Wolpin} {Models}},
	url = {https://github.com/OpenSourceEconomics/respy},
	author = {{Janos Gabler} and {Tobias Raabe}},
	year = {2020},
}

@misc{EngineeringDesignSurrogate,
	title = {Engineering {Design} via {Surrogate} {Modelling}: {A} {Practical} {Guide} {\textbar} {Wiley}},
	shorttitle = {Engineering {Design} via {Surrogate} {Modelling}},
	url = {https://www.wiley.com/en-ao/Engineering+Design+via+Surrogate+Modelling%3A+A+Practical+Guide-p-9780470060681},
	abstract = {Surrogate models expedite the search for promising designs by standing in for expensive design evaluations or simulations. They provide a global model of some metric of a design (such as weight, aerodynamic drag, cost, etc.), which can then be optimized efficiently. Engineering Design via Surrogate Modelling is a self-contained guide to surrogate models and their use in engineering design. The fundamentals of building, selecting, validating, searching and refining a surrogate are presented in a manner accessible to novices in the field. Figures are used liberally to explain the key concepts and clearly show the differences between the various techniques, as well as to emphasize the intuitive nature of the conceptual and mathematical reasoning behind them. More advanced and recent concepts are each presented in stand-alone chapters, allowing the reader to concentrate on material pertinent to their current design problem, and concepts are clearly demonstrated using simple design problems. This collection of advanced concepts (visualization, constraint handling, coping with noisy data, gradient-enhanced modelling, multi-fidelity analysis and multiple objectives) represents an invaluable reference manual for engineers and researchers active in the area. Engineering Design via Surrogate Modelling is complemented by a suite of Matlab codes, allowing the reader to apply all the techniques presented to their own design problems. By applying statistical modelling to engineering design, this book bridges the wide gap between the engineering and statistics communities. It will appeal to postgraduates and researchers across the academic engineering design community as well as practising design engineers. Provides an inclusive and practical guide to using surrogates in engineering design. Presents the fundamentals of building, selecting, validating, searching and refining a surrogate model. Guides the reader through the practical implementation of a surrogate-based design process using a set of case studies from real engineering design challenges. Accompanied by a companion website featuring Matlab software at http://www.wiley.com/go/forrester},
	language = {en-ao},
	urldate = {2021-09-06},
	journal = {Wiley.com},
	file = {Snapshot:/Users/yulei/Zotero/storage/7EPLYPH7/Engineering+Design+via+Surrogate+Modelling+A+Practical+Guide-p-9780470060681.html:text/html},
}

@article{leamer1985SensitivityAnalysesWould,
	title = {Sensitivity {Analyses} {Would} {Help}},
	volume = {75},
	issn = {0002-8282},
	url = {https://www.jstor.org/stable/1814801},
	number = {3},
	urldate = {2021-09-06},
	journal = {The American Economic Review},
	author = {Leamer, Edward E.},
	year = {1985},
	note = {Publisher: American Economic Association},
	keywords = {⛔ No DOI found},
	pages = {308--313},
}

@article{sudret2007UncertaintyPropagationSensitivity,
	title = {Uncertainty propagation and sensitivity analysis in mechanical models},
	language = {en},
	author = {Sudret, Bruno},
	year = {2007},
	keywords = {⛔ No DOI found},
	pages = {252},
	file = {Sudret_2007_Uncertainty propagation and sensitivity analysis in mechanical models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sudret_2007_Uncertainty propagation and sensitivity analysis in mechanical models.pdf:application/pdf},
}

@article{sobol1993SensitivityEstimatesNonlinear,
	title = {Sensitivity {Estimates} for {Nonlinear} {Mathematical} {Models}},
	url = {https://www.semanticscholar.org/paper/Sensitivity-Estimates-for-Nonlinear-Mathematical-Sobol/3e0b415213a580254226fdbcbfc9980b70dd0468},
	abstract = {Semantic Scholar extracted view of \"Sensitivity Estimates for Nonlinear Mathematical Models\" by I. Sobol},
	language = {en},
	number = {4},
	urldate = {2021-09-06},
	journal = {Mathematical Modelling and Computational Experiments},
	author = {Sobol, I.},
	year = {1993},
	keywords = {⛔ No DOI found},
	pages = {407--414},
	file = {Snapshot:/Users/yulei/Zotero/storage/CS8NAECT/3e0b415213a580254226fdbcbfc9980b70dd0468.html:text/html;Sobol_1993_Sensitivity Estimates for Nonlinear Mathematical Models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sobol_1993_Sensitivity Estimates for Nonlinear Mathematical Models.pdf:application/pdf},
}

@article{harrison1992SensitivityAnalysisApplied,
	title = {The {Sensitivity} {Analysis} of {Applied} {General} {Equilibrium} {Models}: {Completely} {Randomized} {Factorial} {Sampling} {Designs}},
	volume = {74},
	issn = {0034-6535},
	shorttitle = {The {Sensitivity} {Analysis} of {Applied} {General} {Equilibrium} {Models}},
	url = {https://www.jstor.org/stable/2109672},
	doi = {10.2307/2109672},
	abstract = {We propose a method for estimating the population mean of a distribution of solution values from applied general equilibrium models subject to parameter uncertainty. The method is illustrated by demonstrating that the "marginal excess burden" of the U.S. taxation system may be robustly bounded with a high confidence.},
	number = {2},
	urldate = {2021-09-06},
	journal = {The Review of Economics and Statistics},
	author = {Harrison, Glenn W. and Vinod, H. D.},
	year = {1992},
	note = {Publisher: The MIT Press},
	pages = {357--362},
	file = {Harrison_Vinod_1992_The Sensitivity Analysis of Applied General Equilibrium Models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Harrison_Vinod_1992_The Sensitivity Analysis of Applied General Equilibrium Models.pdf:application/pdf},
}

@article{anderson2014UncertaintyClimateChange,
	title = {Uncertainty in climate change modeling: can global sensitivity analysis be of help?},
	volume = {34},
	issn = {1539-6924},
	shorttitle = {Uncertainty in climate change modeling},
	doi = {10.1111/risa.12117},
	abstract = {Integrated assessment models offer a crucial support to decisionmakers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions, and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations. Our discussion is guided by the application to the well-known DICE model of William Nordhaus. The proposed methodology allows analysts to draw robust insights into the dependence of future atmospheric temperature, global emissions, and carbon costs and taxes on the model's exogenous variables.},
	language = {eng},
	number = {2},
	journal = {Risk Analysis: An Official Publication of the Society for Risk Analysis},
	author = {Anderson, Barry and Borgonovo, Emanuele and Galeotti, Marzio and Roson, Roberto},
	month = feb,
	year = {2014},
	pmid = {24111855},
	keywords = {risk analysis, Climate change, Climate Change, Computer Simulation, global sensitivity analysis, integrated assessment modeling, Models, Theoretical, Monte Carlo Method, Risk Assessment, Uncertainty},
	pages = {271--293},
	file = {Anderson et al_2014_Uncertainty in climate change modeling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Anderson et al_2014_Uncertainty in climate change modeling.pdf:application/pdf},
}

@article{saltelli2010VarianceBasedSensitivity,
	title = {Variance based sensitivity analysis of model output. {Design} and estimator for the total sensitivity index},
	volume = {181},
	issn = {0010-4655},
	url = {https://www.sciencedirect.com/science/article/pii/S0010465509003087},
	doi = {10.1016/j.cpc.2009.09.018},
	abstract = {Variance based methods have assessed themselves as versatile and effective among the various available techniques for sensitivity analysis of model output. Practitioners can in principle describe the sensitivity pattern of a model Y=f(X1,X2,…,Xk) with k uncertain input factors via a full decomposition of the variance V of Y into terms depending on the factors and their interactions. More often practitioners are satisfied with computing just k first order effects and k total effects, the latter describing synthetically interactions among input factors. In sensitivity analysis a key concern is the computational cost of the analysis, defined in terms of number of evaluations of f(X1,X2,…,Xk) needed to complete the analysis, as f(X1,X2,…,Xk) is often in the form of a numerical model which may take long processing time. While the computational cost is relatively cheap and weakly dependent on k for estimating first order effects, it remains expensive and strictly k-dependent for total effect indices. In the present note we compare existing and new practices for this index and offer recommendations on which to use.},
	language = {en},
	number = {2},
	urldate = {2021-09-06},
	journal = {Computer Physics Communications},
	author = {Saltelli, Andrea and Annoni, Paola and Azzini, Ivano and Campolongo, Francesca and Ratto, Marco and Tarantola, Stefano},
	month = feb,
	year = {2010},
	pages = {259--270},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/ZDFH28ZJ/S0010465509003087.html:text/html;Saltelli et al_2010_Variance based sensitivity analysis of model output.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli et al_2010_Variance based sensitivity analysis of model output.pdf:application/pdf},
}

@article{ratto2008AnalysingDSGEModels,
	title = {Analysing {DSGE} {Models} with {Global} {Sensitivity} {Analysis}},
	volume = {31},
	issn = {1572-9974},
	url = {https://doi.org/10.1007/s10614-007-9110-6},
	doi = {10.1007/s10614-007-9110-6},
	abstract = {We present computational tools to analyse some key properties of DSGE models and address the following questions: (i) Which is the domain of structural coefficients assuring the stability and determinacy of a DSGE model? (ii) Which parameters mostly drive the fit of, e.g., GDP and which the fit of inflation? Is there any conflict between the optimal fit of one observed series versus another one? (iii) How to represent in a direct, albeit approximated, form the relationship between structural parameters and the reduced form of a rational expectations model? Global sensitivity analysis (GSA) techniques are used to answer these questions. We will discuss two classes of methods: Monte Carlo filtering (MCF) techniques and functional/variance decomposition techniques. These tools can make the model properties more transparent; helping the analyst to identify critical elements in the specification and, if necessary, guiding her to revise the model; supporting calibration and estimation procedures and interpreting estimation results. Applications to small DSGE models will complete the description of the methodologies.},
	language = {en},
	number = {2},
	urldate = {2021-09-06},
	journal = {Computational Economics},
	author = {Ratto, Marco},
	month = mar,
	year = {2008},
	pages = {115--139},
	file = {Ratto_2008_Analysing DSGE Models with Global Sensitivity Analysis.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Ratto_2008_Analysing DSGE Models with Global Sensitivity Analysis.pdf:application/pdf},
}

@techreport{scheidegger2017MachineLearningHighDimensional,
	address = {Rochester, NY},
	type = {{SSRN} {Scholarly} {Paper}},
	title = {Machine {Learning} for {High}-{Dimensional} {Dynamic} {Stochastic} {Economies}},
	url = {https://papers.ssrn.com/abstract=2927400},
	abstract = {We present the first computational framework that can compute global solutions to very-high-dimensional dynamic stochastic economic models on arbitrary state space geometries.},
	language = {en},
	number = {ID 2927400},
	urldate = {2021-09-06},
	institution = {Social Science Research Network},
	author = {Scheidegger, Simon and Bilionis, Ilias},
	month = jun,
	year = {2017},
	doi = {10.2139/ssrn.2927400},
	keywords = {Active Subspaces, Dynamic Programming, Gaussian Process, High-Performance Computing, Machine Learning, Neoclassical Growth},
	file = {Snapshot:/Users/yulei/Zotero/storage/V7WNKSLI/papers.html:text/html;Scheidegger_Bilionis_2017_Machine Learning for High-Dimensional Dynamic Stochastic Economies.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Scheidegger_Bilionis_2017_Machine Learning for High-Dimensional Dynamic Stochastic Economies.pdf:application/pdf},
}

@book{saltelli2008SensitivityAnalysis,
	address = {Chichester Weinheim},
	edition = {Paperback ed},
	series = {Wiley paperback series},
	title = {Sensitivity analysis},
	isbn = {978-0-470-74382-9},
	language = {eng},
	publisher = {Wiley},
	editor = {Saltelli, Andrea},
	year = {2008},
	file = {Saltelli_2008_Sensitivity analysis.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli_2008_Sensitivity analysis.pdf:application/pdf},
}

@book{hansen2008Robustness,
	edition = {STU - Student edition},
	title = {Robustness},
	url = {https://www.jstor.org/stable/j.ctt1dr35gx},
	abstract = {The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted?  Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics.  Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.},
	urldate = {2021-09-07},
	publisher = {Princeton University Press},
	author = {Hansen, Lars Peter and Sargent, Thomas J.},
	year = {2008},
}

@article{brock2003PolicyEvaluationUncertain,
	title = {Policy {Evaluation} in {Uncertain} {Economic} {Environments}},
	volume = {2003},
	issn = {0007-2303},
	url = {https://www.jstor.org/stable/1209150},
	doi = {10.1353/eca.2003.0013},
	number = {1},
	urldate = {2021-09-07},
	journal = {Brookings Papers on Economic Activity},
	author = {Brock, William A. and Durlauf, Steven N. and West, Kenneth D.},
	year = {2003},
	note = {Publisher: Brookings Institution Press},
	pages = {235--301},
}

@article{gupta2018RevisitingBasisSensitivity,
	title = {Revisiting the {Basis} of {Sensitivity} {Analysis} for {Dynamical} {Earth} {System} {Models}},
	volume = {54},
	issn = {1944-7973},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR022668},
	doi = {10.1029/2018WR022668},
	abstract = {This paper investigates the problem of global sensitivity analysis (GSA) of Dynamical Earth System Models and proposes a basis for how such analyses should be performed. We argue that (a) performance metric-based approaches to parameter GSA are actually identifiability analyses, (b) the use of a performance metric to assess sensitivity unavoidably distorts the information provided by the model about relative parameter importance, and (c) it is a serious conceptual flaw to interpret the results of such an analysis as being consistent and accurate indications of the sensitivity of the model response to parameter perturbations. Further, because such approaches depend on availability of system state/output observational data, the analysis they provide is necessarily incomplete. Here we frame the GSA problem from first principles, using trajectories of the partial derivatives of model outputs with respect to controlling factors as the theoretical basis for sensitivity, and construct a global sensitivity matrix from which statistical indices of total period time-aggregate parameter importance, and time series of time-varying parameter importance, can be inferred. We demonstrate this framework using the HBV-SASK conceptual hydrologic model applied to the Oldman basin in Canada and show that it disagrees with performance metric-based methods regarding which parameters exert the strongest controls on model behavior. Further, it is highly efficient, requiring less than 1,000 base samples to obtain stable and robust parameter importance assessments for our 10-parameter example.},
	language = {en},
	number = {11},
	urldate = {2021-09-07},
	journal = {Water Resources Research},
	author = {Gupta, Hoshin V. and Razavi, Saman},
	year = {2018},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR022668},
	keywords = {global sensitivity analysis, dynamical systems, efficiency and robustness, global sensitivity matrix, Parameter importance analysis, time-varying sensitivity},
	pages = {8692--8717},
	file = {Snapshot:/Users/yulei/Zotero/storage/2K7T7NHE/2018WR022668.html:text/html;Gupta_Razavi_2018_Revisiting the Basis of Sensitivity Analysis for Dynamical Earth System Models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Gupta_Razavi_2018_Revisiting the Basis of Sensitivity Analysis for Dynamical Earth System Models.pdf:application/pdf},
}

@article{sobol2007EstimatingApproximationError,
	title = {Estimating the approximation error when fixing unessential factors in global sensitivity analysis},
	volume = {92},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832006001499},
	doi = {10.1016/j.ress.2006.07.001},
	abstract = {One of the major settings of global sensitivity analysis is that of fixing non-influential factors, in order to reduce the dimensionality of a model. However, this is often done without knowing the magnitude of the approximation error being produced. This paper presents a new theorem for the estimation of the average approximation error generated when fixing a group of non-influential factors. A simple function where analytical solutions are available is used to illustrate the theorem. The numerical estimation of small sensitivity indices is discussed.},
	language = {en},
	number = {7},
	urldate = {2021-09-07},
	journal = {Reliability Engineering \& System Safety},
	author = {Sobol’, I. M. and Tarantola, S. and Gatelli, D. and Kucherenko, S. S. and Mauntz, W.},
	month = jul,
	year = {2007},
	keywords = {Approximation error, Sensitivity analysis by groups, Sensitivity indices, Sobol’ method},
	pages = {957--960},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/7DKEMVHW/S0951832006001499.html:text/html;Sobol’ et al_2007_Estimating the approximation error when fixing unessential factors in global.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sobol’ et al_2007_Estimating the approximation error when fixing unessential factors in global.pdf:application/pdf},
}

@article{guillaume2019IntroductoryOverviewIdentifiability,
	title = {Introductory overview of identifiability analysis: {A} guide to evaluating whether you have the right type of data for your modeling purpose},
	volume = {119},
	issn = {1364-8152},
	shorttitle = {Introductory overview of identifiability analysis},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815218307278},
	doi = {10.1016/j.envsoft.2019.07.007},
	abstract = {Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.},
	language = {en},
	urldate = {2021-09-07},
	journal = {Environmental Modelling \& Software},
	author = {Guillaume, Joseph H. A. and Jakeman, John D. and Marsili-Libelli, Stefano and Asher, Michael and Brunner, Philip and Croke, Barry and Hill, Mary C. and Jakeman, Anthony J. and Keesman, Karel J. and Razavi, Saman and Stigter, Johannes D.},
	month = sep,
	year = {2019},
	keywords = {Uncertainty, Derivative based methods, Emulation, Hessian, Identifiability, Non-uniqueness, Response surface},
	pages = {418--432},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/KPPDDZ7G/S1364815218307278.html:text/html;Guillaume et al_2019_Introductory overview of identifiability analysis.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Guillaume et al_2019_Introductory overview of identifiability analysis.pdf:application/pdf},
}

@article{tarantola2002CanGlobalSensitivity,
	title = {Can global sensitivity analysis steer the implementation of models for environmental assessments and decision-making?},
	volume = {16},
	issn = {1436-3259},
	url = {https://doi.org/10.1007/s00477-001-0085-x},
	doi = {10.1007/s00477-001-0085-x},
	abstract = {We illustrate a method of global sensitivity analysis and we test it on a preliminary case study in the field of environmental assessment to quantify uncertainty importance in poorly-known model parameters and spatially referenced input data. The focus of the paper is to show how the methodology provides guidance to improve the quality of environmental assessment practices and decision support systems employed in environmental policy. Global sensitivity analysis, coupled with uncertainty analysis, is a tool to assess the robustness of decisions, to understand whether the current state of knowledge on input data and parametric uncertainties is sufficient to enable a decision to be taken. The methodology is applied to a preliminary case study, which is based on a numerical model that employs GIS-based soil data and expert consultation to evaluate an index that joins environmental and economic aspects of land depletion. The index is used as a yardstick by decision-makers involved in the planning of highways to identify the route that minimises the overall impact.},
	language = {en},
	number = {1},
	urldate = {2021-09-07},
	journal = {Stochastic Environmental Research and Risk Assessment},
	author = {Tarantola, S. and Giglioli, N. and Jesinghaus, J. and Saltelli, A.},
	month = feb,
	year = {2002},
	pages = {63--76},
	file = {Tarantola et al_2002_Can global sensitivity analysis steer the implementation of models for.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Tarantola et al_2002_Can global sensitivity analysis steer the implementation of models for.pdf:application/pdf},
}

@misc{BetterRegulationToolbox,
	type = {Text},
	title = {Better regulation toolbox},
	url = {https://ec.europa.eu/info/law/law-making-process/planning-and-proposing-law/better-regulation-why-and-how/better-regulation-guidelines-and-toolbox/better-regulation-toolbox_en},
	abstract = {Better regulation toolbox},
	language = {en},
	urldate = {2021-09-07},
	journal = {European Commission - European Commission},
	file = {Snapshot:/Users/yulei/Zotero/storage/L94CX48Q/better-regulation-toolbox_en.html:text/html},
}

@article{box1986AnalysisUnreplicatedFractional,
	title = {An {Analysis} for {Unreplicated} {Fractional} {Factorials}},
	volume = {28},
	issn = {0040-1706},
	url = {https://www.tandfonline.com/doi/abs/10.1080/00401706.1986.10488093},
	doi = {10.1080/00401706.1986.10488093},
	abstract = {Loss of markets to Japan has recently caused attention to return to the enormous potential that experimental design possesses for the improvement of product design, for the improvement of the manufacturing process, and hence for improvement of overall product quality. In the screening stage of industrial experimentation it is frequently true that the “Pareto Principle” applies; that is, a large proportion of process variation is associated with a small proportion of the process variables. In such circumstances of “factor sparsity,” unreplicated fractional designs and other orthogonal arrays have frequently been effective when used as a screen for isolating preponderant factors. A useful graphical analysis due to Daniel (1959) employs normal probability plotting. A more formal analysis is presented here, which may be used to supplement such plots and hence to facilitate the use of these unreplicated experimental arrangements.},
	number = {1},
	urldate = {2021-09-07},
	journal = {Technometrics},
	author = {Box, George   E.P. and Meyer, R.   Daniel},
	month = feb,
	year = {1986},
	note = {Publisher: Taylor \& Francis
\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/00401706.1986.10488093},
	pages = {11--18},
	file = {Snapshot:/Users/yulei/Zotero/storage/32HD9HQJ/00401706.1986.html:text/html;Box_Meyer_1986_An Analysis for Unreplicated Fractional Factorials.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Box_Meyer_1986_An Analysis for Unreplicated Fractional Factorials.pdf:application/pdf},
}

@article{fisher1936DesignExperiments,
	title = {Design of {Experiments}},
	volume = {1},
	issn = {0007-1447, 1468-5833},
	url = {https://www.bmj.com/content/1/3923/554.2},
	doi = {10.1136/bmj.1.3923.554-a},
	language = {en},
	number = {3923},
	urldate = {2021-09-07},
	journal = {Br Med J},
	author = {Fisher, R. A.},
	month = mar,
	year = {1936},
	note = {Publisher: British Medical Journal Publishing Group
Section: Correspondence},
	pages = {554--554},
	file = {Snapshot:/Users/yulei/Zotero/storage/RNXEL7XT/554.2.html:text/html;Fisher_1936_Design of Experiments.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Fisher_1936_Design of Experiments.pdf:application/pdf},
}

@article{saltelli2010HowAvoidPerfunctory,
	title = {How to avoid a perfunctory sensitivity analysis},
	volume = {25},
	issn = {1364-8152},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815210001180},
	doi = {10.1016/j.envsoft.2010.04.012},
	abstract = {Mathematical modelers from different disciplines and regulatory agencies worldwide agree on the importance of a careful sensitivity analysis (SA) of model-based inference. The most popular SA practice seen in the literature is that of ’one-factor-at-a-time’ (OAT). This consists of analyzing the effect of varying one model input factor at a time while keeping all other fixed. While the shortcomings of OAT are known from the statistical literature, its widespread use among modelers raises concern on the quality of the associated sensitivity analyses. The present paper introduces a novel geometric proof of the inefficiency of OAT, with the purpose of providing the modeling community with a convincing and possibly definitive argument against OAT. Alternatives to OAT are indicated which are based on statistical theory, drawing from experimental design, regression analysis and sensitivity analysis proper.},
	language = {en},
	number = {12},
	urldate = {2021-09-07},
	journal = {Environmental Modelling \& Software},
	author = {Saltelli, Andrea and Annoni, Paola},
	month = dec,
	year = {2010},
	keywords = {Mathematical modeling, Sensitivity analysis, Uncertainty analysis, One-at-a-time, Robustness},
	pages = {1508--1517},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/KZZTNTNR/S1364815210001180.html:text/html;Saltelli_Annoni_2010_How to avoid a perfunctory sensitivity analysis.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli_Annoni_2010_How to avoid a perfunctory sensitivity analysis.pdf:application/pdf},
}

@article{campolongo2000SensitivityAnaysisIngredient,
	title = {Sensitivity {Anaysis} as an {Ingredient} of {Modeling}},
	volume = {15},
	issn = {0883-4237, 2168-8745},
	url = {https://projecteuclid.org/journals/statistical-science/volume-15/issue-4/Sensitivity-Anaysis-as-an-Ingredient-of-Modeling/10.1214/ss/1009213004.full},
	doi = {10.1214/ss/1009213004},
	abstract = {We explore the tasks where sensitivity analysis (SA) can be useful and try to assess the relevance of SA within the modeling process. We suggest that SA could considerably assist in the use of models, by providing objective criteria of judgement for different phases of the modelbuilding process: model identification and discrimination; model calibration; model corroboration. We review some new global quantitative SA methods and suggest that these might enlarge the scope for sensitivity analysis in computational and statistical modeling practice. Among the advantages of the new methods are their robustness, model independence and computational convenience. The discussion is based on worked examples.},
	number = {4},
	urldate = {2021-09-07},
	journal = {Statistical Science},
	author = {Campolongo, F. and Saltelli, A. and Tarantola, S.},
	month = nov,
	year = {2000},
	note = {Publisher: Institute of Mathematical Statistics},
	keywords = {Global sensitivity analysis, model transparency, numerical experiments, predictive uncertainty, quantitative sensitivity measure, reliability and dependability of models, Screening},
	pages = {377--395},
	file = {Snapshot:/Users/yulei/Zotero/storage/K2AXXWHJ/1009213004.html:text/html;Campolongo et al_2000_Sensitivity Anaysis as an Ingredient of Modeling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Campolongo et al_2000_Sensitivity Anaysis as an Ingredient of Modeling.pdf:application/pdf},
}

@article{homma1996ImportanceMeasuresGlobal,
	title = {Importance measures in global sensitivity analysis of nonlinear models},
	volume = {52},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/0951832096000026},
	doi = {10.1016/0951-8320(96)00002-6},
	abstract = {The present paper deals with a new method of global sensitivity analysis of nonlinear models. This is based on a measure of importance to calculate the fractional contribution of the input parameters to the variance of the model prediction. Measures of importance in sensitivity analysis have been suggested by several authors, whose work is reviewed in this article. More emphasis is given to the developments of sensitivity indices by the Russian mathematician I.M. Sobol'. Given that Sobol' treatment of the measure of importance is the most general, his formalism is employed throughout this paper where conceptual and computational improvements of the method are presented. The computational novelty of this study is the introduction of the ‘total effect’ parameter index. This index provides a measure of the total effect of a given parameter, including all the possible synergetic terms between that parameter and all the others. Rank transformation of the data is also introduced in order to increase the reproducibility of the method. These methods are tested on a few analytical and computer models. The main conclusion of this work is the identification of a sensitivity analysis methodology which is both flexible, accurate and informative, and which can be achieved at reasonable computational cost.},
	language = {en},
	number = {1},
	urldate = {2021-09-07},
	journal = {Reliability Engineering \& System Safety},
	author = {Homma, Toshimitsu and Saltelli, Andrea},
	month = apr,
	year = {1996},
	pages = {1--17},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/DPAT923N/0951832096000026.html:text/html;Homma_Saltelli_1996_Importance measures in global sensitivity analysis of nonlinear models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Homma_Saltelli_1996_Importance measures in global sensitivity analysis of nonlinear models.pdf:application/pdf},
}

@article{kleijnen1995SensitivityAnalysisOptimization,
	title = {Sensitivity analysis and optimization of system dynamics models: {Regression} analysis and statistical design of experiments},
	volume = {11},
	issn = {1099-1727},
	shorttitle = {Sensitivity analysis and optimization of system dynamics models},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sdr.4260110403},
	doi = {10.1002/sdr.4260110403},
	abstract = {This paper discusses what-if analysis and optimization of system dynamics models. These problems are solved, using the statistical techniques of regression analysis and Design of Experiments (DOE). These issues are illustrated by applying the statistical techniques to a system dynamics model for coal transportation, taken from Wolstenholme's book System Enquiry: a System Dynamics Approach (1990). The regression analysis uses the least-squares algorithm. DOE uses classic designs, namely, factorials and central composite designs. Compared with intuitive approaches, DOE is more efficient: DOE gives more accurate estimators of input effects. Moreover DOE is more effective: interactions among inputs are estimable too. The system dynamics model is also optimized, using a heuristic that is inspired by Response Surface Methodology (RSM) but that also accounts for constraints. Conclusions are presented for the case study, and general principles are derived. References are given for further study.},
	language = {en},
	number = {4},
	urldate = {2021-09-07},
	journal = {System Dynamics Review},
	author = {Kleijnen, Jack P. C.},
	year = {1995},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sdr.4260110403},
	pages = {275--288},
	file = {Snapshot:/Users/yulei/Zotero/storage/FRF4WUP7/sdr.html:text/html;Kleijnen_1995_Sensitivity analysis and optimization of system dynamics models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Kleijnen_1995_Sensitivity analysis and optimization of system dynamics models.pdf:application/pdf},
}

@article{razavi2015WhatWeMean,
	title = {What do we mean by sensitivity analysis? {The} need for comprehensive characterization of “global” sensitivity in {Earth} and {Environmental} systems models},
	volume = {51},
	issn = {1944-7973},
	shorttitle = {What do we mean by sensitivity analysis?},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014WR016527},
	doi = {10.1002/2014WR016527},
	abstract = {Sensitivity analysis is an essential paradigm in Earth and Environmental Systems modeling. However, the term “sensitivity” has a clear definition, based in partial derivatives, only when specified locally around a particular point (e.g., optimal solution) in the problem space. Accordingly, no unique definition exists for “global sensitivity” across the problem space, when considering one or more model responses to different factors such as model parameters or forcings. A variety of approaches have been proposed for global sensitivity analysis, based on different philosophies and theories, and each of these formally characterizes a different “intuitive” understanding of sensitivity. These approaches focus on different properties of the model response at a fundamental level and may therefore lead to different (even conflicting) conclusions about the underlying sensitivities. Here we revisit the theoretical basis for sensitivity analysis, summarize and critically evaluate existing approaches in the literature, and demonstrate their flaws and shortcomings through conceptual examples. We also demonstrate the difficulty involved in interpreting “global” interaction effects, which may undermine the value of existing interpretive approaches. With this background, we identify several important properties of response surfaces that are associated with the understanding and interpretation of sensitivities in the context of Earth and Environmental System models. Finally, we highlight the need for a new, comprehensive framework for sensitivity analysis that effectively characterizes all of the important sensitivity-related properties of model response surfaces.},
	language = {en},
	number = {5},
	urldate = {2021-09-07},
	journal = {Water Resources Research},
	author = {Razavi, Saman and Gupta, Hoshin V.},
	year = {2015},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014WR016527},
	keywords = {sensitivity analysis, Earth and Environmental system models, interaction effect, Morris, response surface, Sobol},
	pages = {3070--3092},
	file = {Snapshot:/Users/yulei/Zotero/storage/3MUBCG7M/2014WR016527.html:text/html;Razavi_Gupta_2015_What do we mean by sensitivity analysis.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Razavi_Gupta_2015_What do we mean by sensitivity analysis.pdf:application/pdf},
}

@article{do2020CorrelationEffectsMajor,
	title = {Correlation {Effects}? {A} {Major} but {Often} {Neglected} {Component} in {Sensitivity} and {Uncertainty} {Analysis}},
	volume = {56},
	issn = {1944-7973},
	shorttitle = {Correlation {Effects}?},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR025436},
	doi = {10.1029/2019WR025436},
	abstract = {Global sensitivity analysis (GSA) provides essential insights into the behavior of Earth and environmental systems models and identifies dominant controls of output uncertainty. Previous work on GSA, however, has typically been under the assumption that the controlling factors such as model inputs and parameters are independent, whereas, in many cases, they are correlated and their joint distribution follows a variety of forms. Although this assumption can limit the credibility of GSA and its results, very few studies in the field of water and environmental modeling address this issue. In this paper, we first discuss the significance of correlation effects in GSA and then propose a new GSA framework for properly accounting for correlations in input/parameter spaces. To this end, we extend the “variogram-based” theory of GSA, called variogram analysis of response surfaces (VARS), and develop a new generalized star sampling technique (called gSTAR) to accommodate correlated multivariate distributions. We test the new gSTAR-VARS method on two test functions, against a state-of-the-art GSA method that handles correlation effects. We then apply gSTAR-VARS to the HBV-SASK model, calibrated via a Bayesian, Markov chain Monte Carlo approach, for design flood estimation in the Oldman River Basin in Canada. Results demonstrate that accounting for correlation effects can be critically important in GSA, especially in the presence of nonlinearity and interaction effects in the underlying response surfaces. The proposed method can efficiently handle correlations and different distribution types and simultaneously generate a range of sensitivity indices, such as total-variogram effects, variance-based total-order effects, and derivative-based elementary effects.},
	language = {en},
	number = {3},
	urldate = {2021-09-07},
	journal = {Water Resources Research},
	author = {Do, Nhu Cuong and Razavi, Saman},
	year = {2020},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019WR025436},
	keywords = {Climate change, Global sensitivity analysis, Uncertainty, Correlation, Variogram analysis of response surfaces (VARS)},
	pages = {e2019WR025436},
	file = {Snapshot:/Users/yulei/Zotero/storage/RW5XTAZK/2019WR025436.html:text/html;Do_Razavi_2020_Correlation Effects.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Do_Razavi_2020_Correlation Effects.pdf:application/pdf},
}

@article{chastaing2012GeneralizedHoeffdingSobolDecomposition,
	title = {Generalized {Hoeffding}-{Sobol} decomposition for dependent variables - application to sensitivity analysis},
	volume = {6},
	issn = {1935-7524, 1935-7524},
	url = {https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-6/issue-none/Generalized-Hoeffding-Sobol-decomposition-for-dependent-variables---application/10.1214/12-EJS749.full},
	doi = {10.1214/12-EJS749},
	abstract = {In this paper, we consider a regression model built on dependent variables. This regression modelizes an input output relationship. Under boundedness type assumptions on the joint density function of the input variables, we show that a generalized Hoeffding-Sobol decomposition is available. This leads to new indices measuring the sensitivity of the output with respect to the input variables. We also study and discuss the estimation of these new indices.},
	number = {none},
	urldate = {2021-09-07},
	journal = {Electronic Journal of Statistics},
	author = {Chastaing, Gaelle and Gamboa, Fabrice and Prieur, Clémentine},
	month = jan,
	year = {2012},
	note = {Publisher: Institute of Mathematical Statistics and Bernoulli Society},
	keywords = {dependent variables, Hoeffding decomposition, Sensitivity index, Sobol decomposition},
	pages = {2420--2448},
	file = {Snapshot:/Users/yulei/Zotero/storage/FNGPHPFB/12-EJS749.html:text/html;Chastaing et al_2012_Generalized Hoeffding-Sobol decomposition for dependent variables - application.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Chastaing et al_2012_Generalized Hoeffding-Sobol decomposition for dependent variables - application.pdf:application/pdf},
}

@article{xu2008UncertaintySensitivityAnalysis,
	title = {Uncertainty and sensitivity analysis for models with correlated parameters},
	volume = {93},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832007001652},
	doi = {10.1016/j.ress.2007.06.003},
	abstract = {When conducting sensitivity and uncertainty analysis, most of the global sensitivity techniques assume parameter independence. However, it is common that the parameters are correlated with each other. For models with correlated inputs, we propose that the contribution of uncertainty to model output by an individual parameter be divided into two parts: the correlated contribution (by the correlated variations, i.e. variations of a parameter which are correlated with other parameters) and the uncorrelated contribution (by the uncorrelated variations, i.e. the unique variations of a parameter which cannot be explained by any other parameters). So far, only a few studies have been conducted to obtain the sensitivity index for a model with correlated input. But these studies do not distinguish between the correlated and uncorrelated contribution of a parameter. In this study, we propose a regression-based method to quantitatively decompose the total uncertainty in model output into partial variances contributed by the correlated variations and partial variances contributed by the uncorrelated variations. The proposed regression-based method is then applied in three test cases. Results show that the regression-based method can successfully measure the uncertainty contribution in the case where the relationship between response and parameters is approximately linear.},
	language = {en},
	number = {10},
	urldate = {2021-09-07},
	journal = {Reliability Engineering \& System Safety},
	author = {Xu, Chonggang and Gertner, George Zdzislaw},
	month = oct,
	year = {2008},
	keywords = {Sensitivity analysis, Uncertainty analysis, Correlated parameters, Latin hypercube sampling, Linear regression},
	pages = {1563--1573},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/UEYA2V89/S0951832007001652.html:text/html;Xu_Gertner_2008_Uncertainty and sensitivity analysis for models with correlated parameters.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Xu_Gertner_2008_Uncertainty and sensitivity analysis for models with correlated parameters.pdf:application/pdf},
}

@misc{sheikholeslami2020VISCOUSVarianceBasedSensitivity,
	type = {preprint},
	title = {{VISCOUS}: {A} {Variance}-{Based} {Sensitivity} {Analysis} {Using} {Copulas} for {Efficient} {Identification} of {Dominant} {Hydrological} {Processes}},
	copyright = {Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)},
	shorttitle = {{VISCOUS}},
	url = {http://www.essoar.org/doi/10.1002/essoar.10505333.1},
	abstract = {Global Sensitivity Analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnosti},
	language = {EN},
	urldate = {2021-09-07},
	journal = {Earth and Space Science Open Archive},
	author = {Sheikholeslami, Razi and Gharari, Shervan and Papalexiou, Simon M. and Clark, Martyn P.},
	month = dec,
	year = {2020},
	doi = {10.1002/essoar.10505333.1},
	note = {Archive Location: world
Publisher: Earth and Space Science Open Archive
Section: Hydrology},
	file = {Snapshot:/Users/yulei/Zotero/storage/PIFPASQG/essoar.10505333.html:text/html},
}

@article{owen2014SobolIndicesShapley,
	title = {Sobol' {Indices} and {Shapley} {Value}},
	volume = {2},
	url = {https://epubs.siam.org/doi/10.1137/130936233},
	doi = {10.1137/130936233},
	abstract = {Global sensitivity analysis measures the importance of some input variables to a function \$f\$ by looking at the impact on \$f\$ of making large random perturbations to subsets of those variables. Using measures like those of Sobol' we can attribute importance to input variables based on the extent to which they help predict the target function \$f\$. There is a longstanding literature in economics and game theory that considers how to attribute the value of a team effort to individual members of that team. The primary result, known as the Shapley value, is the unique method satisfying some intuitively necessary criteria. In this paper we find the Shapley value of individual variables when we take “variance explained” as their combined value. The result does not match either of the usual Sobol' indices. It is instead bracketed between them for variance explained or indeed any totally monotone game. Because those indices are comparatively easy to compute, Sobol' indices provide effectively computable bounds for the Shapley value.},
	number = {1},
	urldate = {2021-09-07},
	journal = {SIAM/ASA Journal on Uncertainty Quantification},
	author = {Owen, Art B.},
	month = jan,
	year = {2014},
	note = {Publisher: Society for Industrial and Applied Mathematics},
	keywords = {97M50, 91B82, functional ANOVA, global sensitivity, value attribution},
	pages = {245--251},
	file = {Owen_2014_Sobol' Indices and Shapley Value.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Owen_2014_Sobol' Indices and Shapley Value.pdf:application/pdf},
}

@article{iooss2019SHAPLEYEFFECTSSENSITIVITY,
	title = {{SHAPLEY} {EFFECTS} {FOR} {SENSITIVITY} {ANALYSIS} {WITH} {CORRELATED} {INPUTS}: {COMPARISONS} {WITH} {SOBOL}' {INDICES}, {NUMERICAL} {ESTIMATION} {AND} {APPLICATIONS}},
	volume = {9},
	issn = {2152-5080, 2152-5099},
	shorttitle = {{SHAPLEY} {EFFECTS} {FOR} {SENSITIVITY} {ANALYSIS} {WITH} {CORRELATED} {INPUTS}},
	url = {https://www.dl.begellhouse.com/journals/52034eb04b657aea,23ab8f375b210514,706e4963504bc249.html},
	doi = {10.1615/Int.J.UncertaintyQuantification.2019028372},
	language = {English},
	number = {5},
	urldate = {2021-09-07},
	journal = {International Journal for Uncertainty Quantification},
	author = {Iooss, Bertrand and Prieur, Clementine},
	year = {2019},
	note = {Publisher: Begel House Inc.},
	file = {Snapshot:/Users/yulei/Zotero/storage/58S4JJWM/52034eb04b657aea,23ab8f375b210514,706e4963504bc249.html:text/html;Iooss_Prieur_2019_SHAPLEY EFFECTS FOR SENSITIVITY ANALYSIS WITH CORRELATED INPUTS.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Iooss_Prieur_2019_SHAPLEY EFFECTS FOR SENSITIVITY ANALYSIS WITH CORRELATED INPUTS.pdf:application/pdf},
}

@article{prieur2019GlobalSensitivityAnalysis,
	title = {A global sensitivity analysis approach for marine biogeochemical modeling},
	volume = {139},
	issn = {1463-5003},
	url = {https://www.sciencedirect.com/science/article/pii/S1463500318303688},
	doi = {10.1016/j.ocemod.2019.101402},
	abstract = {This paper introduces the Sobol’ indices approach for global sensitivity analysis (SA), in the context of marine biogeochemistry. Such an approach is particularly well suited for ocean biogeochemical models, which make use of numerous parameters within large sets of differential equations with complex dependencies. This SA allows for a detailed study of the relative influence of a large number of input parameters on output quantities of interest to be chosen. It is able to distinguish between direct effects of these parameters and effects due to interaction between two or more parameters. Although demanding in terms of computation, such a tool is now becoming affordable, thanks to the development of distributed computing environments. An applicative example is presented with the MODECOGeL biogeochemical model, and illustrates the advantages of this approach over standard local SA.},
	language = {en},
	urldate = {2021-09-07},
	journal = {Ocean Modelling},
	author = {Prieur, C. and Viry, L. and Blayo, E. and Brankart, J. -M},
	month = jul,
	year = {2019},
	keywords = {Sensitivity analysis, Marine biogeochemistry, Sobol’ indices},
	pages = {101402},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/4R7VXK9I/S1463500318303688.html:text/html;Prieur et al_2019_A global sensitivity analysis approach for marine biogeochemical modeling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Prieur et al_2019_A global sensitivity analysis approach for marine biogeochemical modeling.pdf:application/pdf},
}

@article{efron1987BetterBootstrapConfidence,
	title = {Better {Bootstrap} {Confidence} {Intervals}},
	volume = {82},
	issn = {0162-1459},
	url = {https://www.tandfonline.com/doi/abs/10.1080/01621459.1987.10478410},
	doi = {10.1080/01621459.1987.10478410},
	abstract = {We consider the problem of setting approximate confidence intervals for a single parameter θ in a multiparameter family. The standard approximate intervals based on maximum likelihood theory, , can be quite misleading. In practice, tricks based on transformations, bias corrections, and so forth, are often used to improve their accuracy. The bootstrap confidence intervals discussed in this article automatically incorporate such tricks without requiring the statistician to think them through for each new application, at the price of a considerable increase in computational effort. The new intervals incorporate an improvement over previously suggested methods, which results in second-order correctness in a wide variety of problems. In addition to parametric families, bootstrap intervals are also developed for nonparametric situations.},
	number = {397},
	urldate = {2021-09-07},
	journal = {Journal of the American Statistical Association},
	author = {Efron, Bradley},
	month = mar,
	year = {1987},
	note = {Publisher: Taylor \& Francis
\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/01621459.1987.10478410},
	keywords = {Approximate confidence intervals, Nonparametric intervals, Resampling methods, Second-order theory, Skewness corrections, Transformations},
	pages = {171--185},
	file = {Snapshot:/Users/yulei/Zotero/storage/RDTZM2J2/01621459.1987.html:text/html;Efron_1987_Better Bootstrap Confidence Intervals.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Efron_1987_Better Bootstrap Confidence Intervals.pdf:application/pdf},
}

@article{saltelli2002MakingBestUse,
	title = {Making best use of model evaluations to compute sensitivity indices},
	volume = {145},
	issn = {0010-4655},
	url = {https://www.sciencedirect.com/science/article/pii/S0010465502002801},
	doi = {10.1016/S0010-4655(02)00280-1},
	abstract = {This paper deals with computations of sensitivity indices in sensitivity analysis. Given a mathematical or computational model y=f(x1,x2,…,xk), where the input factors xi's are uncorrelated with one another, one can see y as the realization of a stochastic process obtained by sampling each of the xi from its marginal distribution. The sensitivity indices are related to the decomposition of the variance of y into terms either due to each xi taken singularly (first order indices), as well as into terms due to the cooperative effects of more than one xi. In this paper we assume that one has computed the full set of first order sensitivity indices as well as the full set of total-order sensitivity indices (a fairly common strategy in sensitivity analysis), and show that in this case the same set of model evaluations can be used to compute double estimates of: \&\#x02022;the total effect of two factors taken together, for all such k2 couples, where k is the dimensionality of the model;\&\#x02022;the total effect of k−2 factors taken together, for all k2 such (k−2) ples. We further introduce a new strategy for the computation of the full sets of first plus total order sensitivity indices that is about 50\% cheaper in terms of model evaluations with respect to previously published works. We discuss separately the case where the input factors xi's are not independent from each other.},
	language = {en},
	number = {2},
	urldate = {2021-09-07},
	journal = {Computer Physics Communications},
	author = {Saltelli, Andrea},
	month = may,
	year = {2002},
	keywords = {Sensitivity analysis, Importance measures, Sensitivity indices, Sensitivity measures},
	pages = {280--297},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/DTHESDYS/S0010465502002801.html:text/html;Saltelli_2002_Making best use of model evaluations to compute sensitivity indices.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli_2002_Making best use of model evaluations to compute sensitivity indices.pdf:application/pdf},
}

@article{sobol1967DistributionPointsCube,
	title = {On the distribution of points in a cube and the approximate evaluation of integrals},
	volume = {7},
	issn = {0041-5553},
	url = {https://www.sciencedirect.com/science/article/pii/0041555367901449},
	doi = {10.1016/0041-5553(67)90144-9},
	language = {en},
	number = {4},
	urldate = {2021-09-07},
	journal = {USSR Computational Mathematics and Mathematical Physics},
	author = {Sobol', I. M},
	month = jan,
	year = {1967},
	pages = {86--112},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/MHFCHQUM/0041555367901449.html:text/html;Sobol'_1967_On the distribution of points in a cube and the approximate evaluation of.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sobol'_1967_On the distribution of points in a cube and the approximate evaluation of.pdf:application/pdf},
}

@article{sobol2011ConstructionComparisonHighDimensional,
	title = {Construction and {Comparison} of {High}-{Dimensional} {Sobol}' {Generators}},
	volume = {2011},
	issn = {1541-8286},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wilm.10056},
	doi = {10.1002/wilm.10056},
	abstract = {Sobol' sequence generators are actively used in financial applications. In this paper, we explore the effect of the uniformity properties A and A' on the generator performance in high dimensional problems. It is shown that these properties provide an additional guarantee of uniformity for high-dimensional problems even at a small number of sampled points. By imposing additional uniformity properties on low dimensional projections of the sequence in addition to the uniformity properties of the d-dimensional sequence itself the efficiency of the Sobol' sequence can be increased. The SobolSeq16384 generator which satisfies additional uniformity properties (Property A for all 16384 dimensions and Property A' for adjacent dimensions) is constructed. A comparison of known Sobol' sequence generators for a set of tests shows that for majority of tests the SobolSeq16384 generator performs better than other generators. Copyright © 2012 Wilmott Magazine Ltd.},
	language = {en},
	number = {56},
	urldate = {2021-09-08},
	journal = {Wilmott},
	author = {Sobol', Ilya M. and Asotsky, Danil and Kreinin, Alexander and Kucherenko, Sergei},
	year = {2011},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wilm.10056},
	keywords = {effective dimensions, quasi-Monte Carlo, Sobol' sequences},
	pages = {64--79},
	file = {Snapshot:/Users/yulei/Zotero/storage/55ZJZUAW/wilm.html:text/html;Sobol' et al_2011_Construction and Comparison of High-Dimensional Sobol' Generators.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sobol' et al_2011_Construction and Comparison of High-Dimensional Sobol' Generators.pdf:application/pdf},
}

@misc{gilquin2016RecursiveEstimationProcedure,
	title = {Recursive estimation procedure of {Sobol}' indices based on replicated designs},
	url = {https://hal.inria.fr/hal-01291769},
	abstract = {In the context of global sensitivity analysis, the replication procedure allows to estimate Sobol' indices at an efficient cost. However this method still requires a large number of model evaluations. In this paper, we consider the ability of increasing the number of evaluation points, thus the accuracy of estimates, by rendering the replication procedure recursive. The key feature of this approach is the construction of structured space-filling designs. For the estimation of first-order indices, we exploit a nested Latin Hypercube already introduced in the literature. For the estimation of closed second-order indices, two methods are proposed to construct iteratively an orthogonal array. One of the two leads to a partition of the coordinate space over a Galois field. Various space-filling criteria are used to evaluate our designs.},
	language = {en},
	urldate = {2021-09-08},
	author = {Gilquin, Laurent and Arnaud, Elise and Prieur, Clémentine and Monod, Hervé},
	month = jan,
	year = {2016},
	file = {Snapshot:/Users/yulei/Zotero/storage/2CZM7Z8Z/hal-01291769v1.html:text/html;Gilquin et al_2016_Recursive estimation procedure of Sobol' indices based on replicated designs.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Gilquin et al_2016_Recursive estimation procedure of Sobol' indices based on replicated designs.pdf:application/pdf},
}

@article{sheikholeslami2017ProgressiveLatinHypercube,
	title = {Progressive {Latin} {Hypercube} {Sampling}: {An} efficient approach for robust sampling-based analysis of environmental models},
	volume = {93},
	issn = {1364-8152},
	shorttitle = {Progressive {Latin} {Hypercube} {Sampling}},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815216305096},
	doi = {10.1016/j.envsoft.2017.03.010},
	abstract = {Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional properties of interest (Latin hypercube properties, space-filling, etc.), as the sample size grows. Unlike Latin hypercube sampling, PLHS generates a series of smaller sub-sets (slices) such that (1) the first slice is Latin hypercube, (2) the progressive union of slices remains Latin hypercube and achieves maximum stratification in any one-dimensional projection, and as such (3) the entire sample set is Latin hypercube. The performance of PLHS is compared with benchmark sampling strategies across multiple case studies for Monte Carlo simulation, sensitivity and uncertainty analysis. Our results indicate that PLHS leads to improved efficiency, convergence, and robustness of sampling-based analyses.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Environmental Modelling \& Software},
	author = {Sheikholeslami, Razi and Razavi, Saman},
	month = jul,
	year = {2017},
	keywords = {Monte Carlo simulation, Sensitivity analysis, Uncertainty analysis, Design of computer experiments, Optimal Latin hypercube sampling, Sequential sampling},
	pages = {109--126},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/W6DKDEG8/S1364815216305096.html:text/html;Sheikholeslami_Razavi_2017_Progressive Latin Hypercube Sampling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sheikholeslami_Razavi_2017_Progressive Latin Hypercube Sampling.pdf:application/pdf},
}

@article{kucherenko2015ExploringMultidimensionalSpaces,
	title = {Exploring multi-dimensional spaces: a {Comparison} of {Latin} {Hypercube} and {Quasi} {Monte} {Carlo} {Sampling} {Techniques}},
	shorttitle = {Exploring multi-dimensional spaces},
	url = {http://arxiv.org/abs/1505.02350},
	abstract = {Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol sequences. Generally results show superior performance of the Quasi Monte Carlo approach based on Sobol sequences in line with theoretical predictions. Latin Hypercube Sampling can be more efficient than both Monte Carlo method and Quasi Monte Carlo method but the latter inequality holds for a reduced set of function typology and at small number of sampled points. In conclusion Quasi Monte Carlo method would appear the safest bet when integrating functions of unknown typology.},
	urldate = {2021-09-08},
	journal = {arXiv:1505.02350 [stat]},
	author = {Kucherenko, Sergei and Albrecht, Daniel and Saltelli, Andrea},
	month = may,
	year = {2015},
	note = {arXiv: 1505.02350},
	keywords = {⛔ No DOI found, 65C05, G.3, Statistics - Applications, Statistics - Computation},
	file = {arXiv.org Snapshot:/Users/yulei/Zotero/storage/A9B9INZ9/1505.html:text/html;Kucherenko et al_2015_Exploring multi-dimensional spaces.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Kucherenko et al_2015_Exploring multi-dimensional spaces.pdf:application/pdf},
}

@article{kucherenko2011IdentificationModelEffective,
	title = {The identification of model effective dimensions using global sensitivity analysis},
	volume = {96},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832010002437},
	doi = {10.1016/j.ress.2010.11.003},
	abstract = {It is shown that the effective dimensions can be estimated at reasonable computational costs using variance based global sensitivity analysis. Namely, the effective dimension in the truncation sense can be found by using the Sobol' sensitivity indices for subsets of variables. The effective dimension in the superposition sense can be estimated by using the first order effects and the total Sobol' sensitivity indices. The classification of some important classes of integrable functions based on their effective dimension is proposed. It is shown that it can be used for the prediction of the QMC efficiency. Results of numerical tests verify the prediction of the developed techniques.},
	language = {en},
	number = {4},
	urldate = {2021-09-08},
	journal = {Reliability Engineering \& System Safety},
	author = {Kucherenko, Sergei and Feil, Balazs and Shah, Nilay and Mauntz, Wolfgang},
	month = apr,
	year = {2011},
	keywords = {Global sensitivity analysis, Effective dimensions, Monte Carlo integration, Quasi-Monte Carlo, Sobol' sensitivity indices},
	pages = {440--449},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/QW5BJI5F/S0951832010002437.html:text/html;Kucherenko et al_2011_The identification of model effective dimensions using global sensitivity.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Kucherenko et al_2011_The identification of model effective dimensions using global sensitivity.pdf:application/pdf},
}

@article{razavi2012ReviewSurrogateModeling,
	title = {Review of surrogate modeling in water resources: {REVIEW}},
	volume = {48},
	issn = {00431397},
	shorttitle = {Review of surrogate modeling in water resources},
	url = {http://doi.wiley.com/10.1029/2011WR011527},
	doi = {10.1029/2011WR011527},
	language = {en},
	number = {7},
	urldate = {2021-09-08},
	journal = {Water Resources Research},
	author = {Razavi, Saman and Tolson, Bryan A. and Burn, Donald H.},
	month = jul,
	year = {2012},
	file = {Razavi et al_2012_Review of surrogate modeling in water resources.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Razavi et al_2012_Review of surrogate modeling in water resources.pdf:application/pdf},
}

@article{xiu2002WienerAskeyPolynomial,
	title = {The {Wiener}--{Askey} {Polynomial} {Chaos} for {Stochastic} {Differential} {Equations}},
	volume = {24},
	issn = {1064-8275},
	url = {https://epubs.siam.org/doi/10.1137/S1064827501387826},
	doi = {10.1137/S1064827501387826},
	abstract = {We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener's polynomial chaos. Specifically, we represent the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error. Several continuous and discrete processes are treated, and numerical examples show substantial speed-up compared to Monte Carlo simulations for low dimensional stochastic inputs.},
	number = {2},
	urldate = {2021-09-08},
	journal = {SIAM Journal on Scientific Computing},
	author = {Xiu, Dongbin and Karniadakis, George Em},
	month = jan,
	year = {2002},
	note = {Publisher: Society for Industrial and Applied Mathematics},
	keywords = {65C20, 65C30, Askey scheme, Galerkin projection, orthogonal polynomials, polynomial chaos, spectral methods, stochastic differential equations},
	pages = {619--644},
	file = {Xiu_Karniadakis_2002_The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Xiu_Karniadakis_2002_The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations.pdf:application/pdf},
}

@article{sudret2008GlobalSensitivityAnalysis,
	series = {Bayesian {Networks} in {Dependability}},
	title = {Global sensitivity analysis using polynomial chaos expansions},
	volume = {93},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832007001329},
	doi = {10.1016/j.ress.2007.04.002},
	abstract = {Global sensitivity analysis (SA) aims at quantifying the respective effects of input random variables (or combinations thereof) onto the variance of the response of a physical or mathematical model. Among the abundant literature on sensitivity measures, the Sobol’ indices have received much attention since they provide accurate information for most models. The paper introduces generalized polynomial chaos expansions (PCE) to build surrogate models that allow one to compute the Sobol’ indices analytically as a post-processing of the PCE coefficients. Thus the computational cost of the sensitivity indices practically reduces to that of estimating the PCE coefficients. An original non intrusive regression-based approach is proposed, together with an experimental design of minimal size. Various application examples illustrate the approach, both from the field of global SA (i.e. well-known benchmark problems) and from the field of stochastic mechanics. The proposed method gives accurate results for various examples that involve up to eight input random variables, at a computational cost which is 2–3 orders of magnitude smaller than the traditional Monte Carlo-based evaluation of the Sobol’ indices.},
	language = {en},
	number = {7},
	urldate = {2021-09-08},
	journal = {Reliability Engineering \& System Safety},
	author = {Sudret, Bruno},
	month = jul,
	year = {2008},
	keywords = {Global sensitivity analysis, Sobol’ indices, Analysis of variance, Generalized chaos, Polynomial chaos, Regression, Stochastic finite elements},
	pages = {964--979},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/XJRM3EWI/S0951832007001329.html:text/html;Sudret_2008_Global sensitivity analysis using polynomial chaos expansions.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Sudret_2008_Global sensitivity analysis using polynomial chaos expansions.pdf:application/pdf},
}

@phdthesis{bier1982MeasureUncertaintyImportance,
	type = {Thesis},
	title = {A measure of uncertainty importance for components in fault trees},
	copyright = {M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.},
	url = {https://dspace.mit.edu/handle/1721.1/15687},
	abstract = {Thesis (Ph.D.)--Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1983.},
	language = {eng},
	urldate = {2021-09-08},
	school = {Massachusetts Institute of Technology},
	author = {Bier, Vicki M.},
	year = {1982},
	note = {Accepted: 2005-08-04T22:08:51Z},
	file = {Snapshot:/Users/yulei/Zotero/storage/7GTDYG2D/15687.html:text/html;Bier_1982_A measure of uncertainty importance for components in fault trees.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Bier_1982_A measure of uncertainty importance for components in fault trees.pdf:application/pdf},
}

@article{butler2020OptimalExperimentalDesign,
	title = {Optimal experimental design for prediction based on push-forward probability measures},
	volume = {416},
	issn = {0021-9991},
	url = {https://www.sciencedirect.com/science/article/pii/S0021999120302928},
	doi = {10.1016/j.jcp.2020.109518},
	abstract = {Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the push-forward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Journal of Computational Physics},
	author = {Butler, T. and Jakeman, J. D. and Wildey, T.},
	month = sep,
	year = {2020},
	keywords = {Uncertainty quantification, Optimal experimental design, Push-forward measures, Stochastic inference},
	pages = {109518},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/EP5REPUX/S0021999120302928.html:text/html;Butler et al_2020_Optimal experimental design for prediction based on push-forward probability.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Butler et al_2020_Optimal experimental design for prediction based on push-forward probability.pdf:application/pdf},
}

@book{artb.owen2013MonteCarloTheory,
	title = {Monte {Carlo} theory, methods and examples},
	author = {{Art B. Owen}},
	year = {2013},
}

@article{ravalico2009SensitivityAnalysisDecisionmaking,
	series = {Special {Issue} on {Sensitivity} {Analysis}},
	title = {Sensitivity analysis for decision-making using the {MORE} method—{A} {Pareto} approach},
	volume = {94},
	issn = {0951-8320},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832009000234},
	doi = {10.1016/j.ress.2009.01.009},
	abstract = {Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.},
	language = {en},
	number = {7},
	urldate = {2021-09-08},
	journal = {Reliability Engineering \& System Safety},
	author = {Ravalico, Jakin K. and Maier, Holger R. and Dandy, Graeme C.},
	month = jul,
	year = {2009},
	keywords = {Sensitivity analysis, Decision-making, Integrated assessment modelling, Pareto optimization},
	pages = {1229--1237},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/9EX4V4IA/S0951832009000234.html:text/html;Ravalico et al_2009_Sensitivity analysis for decision-making using the MORE method—A Pareto approach.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Ravalico et al_2009_Sensitivity analysis for decision-making using the MORE method—A Pareto approach.pdf:application/pdf},
}

@book{dorfman1987LinearProgrammingEconomic,
	title = {Linear {Programming} and {Economic} {Analysis}},
	isbn = {978-0-486-65491-1},
	abstract = {Designed primarily for economists and those interested in management economics who are not necessarily accomplished mathematicians, this text offers a clear, concise exposition of the relationship of linear programming to standard economic analysis. The research and writing were supported by The RAND Corporation in the late 1950s. Linear programming has been one of the most important postwar developments in economic theory, but until publication of the present volume, no text offered a comprehensive treatment of the many facets of the relationship of linear programming to traditional economic theory. This book was the first to provide a wide-ranging survey of such important aspects of the topic as the interrelations between the celebrated von Neumann theory of games and linear programming, and the relationship between game theory and the traditional economic theories of duopoly and bilateral monopoly. Modern economists will especially appreciate the treatment of the connection between linear programming and modern welfare economics and the insights that linear programming gives into the determinateness of Walrasian equilibrium. The book also offers an excellent introduction to the important Leontief theory of input-output as well as extensive treatment of the problems of dynamic linear programming. Successfully used for three decades in graduate economics courses, this book stresses practical problems and specifies important concrete applications.},
	language = {en},
	publisher = {Courier Corporation},
	author = {Dorfman, Robert and Samuelson, Paul Anthony and Solow, Robert M.},
	month = jan,
	year = {1987},
	note = {Google-Books-ID: k5\_vzaCNQP4C},
	keywords = {Business \& Economics / Economics / General, Mathematics / General, Mathematics / Linear \& Nonlinear Programming},
}

@article{duinker2007ScenarioAnalysisEnvironmental,
	title = {Scenario analysis in environmental impact assessment: {Improving} explorations of the future},
	volume = {27},
	issn = {0195-9255},
	shorttitle = {Scenario analysis in environmental impact assessment},
	url = {https://www.sciencedirect.com/science/article/pii/S0195925506001302},
	doi = {10.1016/j.eiar.2006.11.001},
	abstract = {Scenarios and scenario analysis have become popular approaches in organizational planning and participatory exercises in pursuit of sustainable development. However, they are little used, at least in any formal way, in environmental impact assessment (EIA). This is puzzling because EIA is a process specifically dedicated to exploring options for more-sustainable (i.e., less environmentally damaging) futures. In this paper, we review the state of the art associated with scenarios and scenario analysis, and describe two areas where scenario analysis could be particularly helpful in EIA: (a) in defining future developments for cumulative effects assessment; and (b) in considering the influence of contextual change – e.g. climate change – on impact forecasts for specific projects. We conclude by encouraging EIA practitioners to learn about the promise of scenario-based analysis and implement scenario-based methods so that EIA can become more effective in fostering sustainable development.},
	language = {en},
	number = {3},
	urldate = {2021-09-08},
	journal = {Environmental Impact Assessment Review},
	author = {Duinker, Peter N. and Greig, Lorne A.},
	month = apr,
	year = {2007},
	keywords = {Climate change, Canada, Cumulative effects assessment, Environmental impact assessment, Futures studies, Scenario},
	pages = {206--219},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/6GWQHIJX/S0195925506001302.html:text/html;Duinker_Greig_2007_Scenario analysis in environmental impact assessment.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Duinker_Greig_2007_Scenario analysis in environmental impact assessment.pdf:application/pdf},
}

@article{maier2016UncertainFutureDeep,
	title = {An uncertain future, deep uncertainty, scenarios, robustness and adaptation: {How} do they fit together?},
	volume = {81},
	issn = {1364-8152},
	shorttitle = {An uncertain future, deep uncertainty, scenarios, robustness and adaptation},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815216300780},
	doi = {10.1016/j.envsoft.2016.03.014},
	abstract = {A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of “best-guess” future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the development of strategies that are best suited to dealing with a deeply uncertain future.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Environmental Modelling \& Software},
	author = {Maier, H. R. and Guillaume, J. H. A. and van Delden, H. and Riddell, G. A. and Haasnoot, M. and Kwakkel, J. H.},
	month = jul,
	year = {2016},
	keywords = {Robustness, Adaptability, Decision support, Deep uncertainty, Scenarios, Uncertain future},
	pages = {154--164},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/N399T4QW/S1364815216300780.html:text/html;Maier et al_2016_An uncertain future, deep uncertainty, scenarios, robustness and adaptation.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Maier et al_2016_An uncertain future, deep uncertainty, scenarios, robustness and adaptation.pdf:application/pdf},
}

@article{lamontagne2019RobustAbatementPathways,
	title = {Robust abatement pathways to tolerable climate futures require immediate global action},
	volume = {9},
	copyright = {2019 The Author(s), under exclusive licence to Springer Nature Limited},
	issn = {1758-6798},
	url = {https://www.nature.com/articles/s41558-019-0426-8},
	doi = {10.1038/s41558-019-0426-8},
	abstract = {Disentangling the relative importance of climate change abatement policies from the human–Earth system (HES) uncertainties that determine their performance is challenging because the two are inexorably linked, and the nature of this linkage is dynamic, interactive and metric specific1. Here, we demonstrate an approach to quantify the individual and joint roles that diverse HES uncertainties and our choices in abatement policy play in determining future climate and economic conditions, as simulated by an improved version of the Dynamic Integrated model of Climate and the Economy2,3. Despite wide-ranging HES uncertainties, the growth rate of global abatement (a societal choice) is the primary driver of long-term warming. It is not a question of whether we can limit warming but whether we choose to do so. Our results elucidate important long-term HES dynamics that are often masked by common time-aggregated metrics. Aggressive near-term abatement will be very costly and do little to impact near-term warming. Conversely, the warming that will be experienced by future generations will mostly be driven by earlier abatement actions. We quantify probabilistic abatement pathways to tolerable climate/economic outcomes4,5, conditional on the climate sensitivity to the atmospheric CO2 concentration. Even under optimistic assumptions about the climate sensitivity, pathways to a tolerable climate/economic future are rapidly narrowing.},
	language = {en},
	number = {4},
	urldate = {2021-09-08},
	journal = {Nature Climate Change},
	author = {Lamontagne, J. R. and Reed, P. M. and Marangoni, G. and Keller, K. and Garner, G. G.},
	month = apr,
	year = {2019},
	note = {Bandiera\_abtest: a
Cg\_type: Nature Research Journals
Number: 4
Primary\_atype: Research
Publisher: Nature Publishing Group
Subject\_term: Climate-change mitigation;Climate-change policy;Governance;Socioeconomic scenarios
Subject\_term\_id: climate-change-mitigation;climate-change-policy;governance;socioeconomic-scenarios},
	pages = {290--294},
	file = {Snapshot:/Users/yulei/Zotero/storage/K74PG7N9/s41558-019-0426-8.html:text/html;Lamontagne et al_2019_Robust abatement pathways to tolerable climate futures require immediate global.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Lamontagne et al_2019_Robust abatement pathways to tolerable climate futures require immediate global.pdf:application/pdf},
}

@article{herman2015HowShouldRobustness,
	title = {How {Should} {Robustness} {Be} {Defined} for {Water} {Systems} {Planning} under {Change}?},
	volume = {141},
	copyright = {© 2015 American Society of Civil Engineers},
	issn = {1943-5452},
	url = {https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29WR.1943-5452.0000509},
	doi = {10.1061/(ASCE)WR.1943-5452.0000509},
	abstract = {Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Robustness analyses have shifted from expected utility to exploratory bottom-up approaches which identify vulnerable scenarios prior to assigning likelihoods. Examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches based on their methods of (1) alternative generation, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) sensitivity analysis to identify important uncertainties. Building from the proposed taxonomy, we use a regional urban water supply case study in the Research Triangle region of North Carolina to illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to the selection of substantially different planning alternatives, underscoring the importance of an informed definition of robustness. Moreover, the results show that some commonly employed methodological choices and definitions of robustness can have undesired consequences when ranking decision alternatives. For the demonstrated test case, recommendations for overcoming these issues include: (1) decision alternatives should be searched rather than prespecified, (2) dominant uncertainties should be discovered through sensitivity analysis rather than assumed, and (3) a carefully elicited multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work emphasizes the importance of an informed problem formulation for systems facing challenging performance tradeoffs and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning.},
	language = {EN},
	number = {10},
	urldate = {2021-09-08},
	journal = {Journal of Water Resources Planning and Management},
	author = {Herman, Jonathan D. and Reed, Patrick M. and Zeff, Harrison B. and Characklis, Gregory W.},
	month = oct,
	year = {2015},
	note = {Publisher: American Society of Civil Engineers},
	pages = {04015012},
	file = {Snapshot:/Users/yulei/Zotero/storage/WC2CZ2PI/(ASCE)WR.1943-5452.html:text/html;Herman et al_2015_How Should Robustness Be Defined for Water Systems Planning under Change.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Herman et al_2015_How Should Robustness Be Defined for Water Systems Planning under Change.pdf:application/pdf},
}

@article{marangoni2017SensitivityProjectedLongterm,
	title = {Sensitivity of projected long-term {CO2} emissions across the {Shared} {Socioeconomic} {Pathways}},
	volume = {7},
	copyright = {2017 Nature Publishing Group},
	issn = {1758-6798},
	url = {https://www.nature.com/articles/nclimate3199},
	doi = {10.1038/nclimate3199},
	abstract = {Socioeconomic scenarios of climate change contain a number of assumptions, which lead to uncertainty in projections. Emission estimates in the scenarios are found to be most sensitive for assumptions about energy intensity and economic growth.},
	language = {en},
	number = {2},
	urldate = {2021-09-08},
	journal = {Nature Climate Change},
	author = {Marangoni, G. and Tavoni, M. and Bosetti, V. and Borgonovo, E. and Capros, P. and Fricko, O. and Gernaat, D. E. H. J. and Guivarch, C. and Havlik, P. and Huppmann, D. and Johnson, N. and Karkatsoulis, P. and Keppo, I. and Krey, V. and Ó Broin, E. and Price, J. and van Vuuren, D. P.},
	month = feb,
	year = {2017},
	note = {Bandiera\_abtest: a
Cg\_type: Nature Research Journals
Number: 2
Primary\_atype: Research
Publisher: Nature Publishing Group
Subject\_term: Climate-change mitigation;Socioeconomic scenarios
Subject\_term\_id: climate-change-mitigation;socioeconomic-scenarios},
	pages = {113--117},
	file = {Snapshot:/Users/yulei/Zotero/storage/J2XXU2SR/nclimate3199.html:text/html;Marangoni et al_2017_Sensitivity of projected long-term CO2 emissions across the Shared.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Marangoni et al_2017_Sensitivity of projected long-term CO2 emissions across the Shared.pdf:application/pdf},
}

@article{workman2020DecisionMakingContexts,
	title = {Decision making in contexts of deep uncertainty - {An} alternative approach for long-term climate policy},
	volume = {103},
	issn = {1462-9011},
	url = {https://www.sciencedirect.com/science/article/pii/S1462901119304319},
	doi = {10.1016/j.envsci.2019.10.002},
	abstract = {The majority of global emissions scenarios compatible with holding global warming to less than 2 °C depend on the large-scale use of bioenergy with carbon capture and storage (BECCS) to compensate for an overshoot of atmospheric CO2 budgets. Recent critiques have highlighted the ethical and environmental risks of this strategy and the danger of building long-term climate policy on such speculative technological scenarios emerging from integrated assessment models. Here, we critically examine both the use of BECCS in mitigation scenarios and the decision making philosophy underlying the use of integrated assessment modelling to inform climate policy. We identify a number of features of integrated assessment models that favour selection of BECCS over alternative strategies. However, we argue that the deeper issue lies in the tendency to view model outputs as objective science, capable of defining “optimal” goals and strategies for which climate policy should strive, rather than as exploratory tools within a broader policy development process. This model-centric decision making philosophy is highly sensitive to uncertainties in model assumptions and future trends, and tends to favour solutions that perform well within the model framework at the expense of a wider mix of strategies and values. Drawing on the principles of Robust Decision Making, we articulate the need for an alternative approach that explicitly embraces uncertainty, multiple values and diversity among stakeholders and viewpoints, and in which modelling exists in an iterative exchange with policy development rather than separate from it. Such an approach would provide more relevant and robust information to near-term policymaking, and enable an inclusive societal dialogue about the appropriate role for carbon dioxide removal within climate policy.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Environmental Science \& Policy},
	author = {Workman, Mark and Dooley, Kate and Lomax, Guy and Maltby, James and Darch, Geoff},
	month = jan,
	year = {2020},
	keywords = {Integrated assessment modelling, Carbon dioxide removal technologies, Diversity in value-sets, International climate policy, Predict then act, Robust decision making},
	pages = {77--84},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/6D8BQJJ8/S1462901119304319.html:text/html;Workman et al_2020_Decision making in contexts of deep uncertainty - An alternative approach for.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Workman et al_2020_Decision making in contexts of deep uncertainty - An alternative approach for.pdf:application/pdf},
}

@article{saltelli2002RelativeImportanceInput,
	title = {On the {Relative} {Importance} of {Input} {Factors} in {Mathematical} {Models}},
	volume = {97},
	issn = {0162-1459},
	url = {https://doi.org/10.1198/016214502388618447},
	doi = {10.1198/016214502388618447},
	abstract = {This article deals with global quantitative sensitivity analysis of the Level E model, a computer code used in safety assessment for nuclear waste disposal. The Level E code has been the subject of two international benchmarks of risk assessment codes and Monte Carlo methods and is well known in the literature. We discuss the Level E model with reference to two different settings. In the first setting, the objective is to find the input factor that drives most of the output variance. In the second setting, we strive to achieve a preestablished reduction in the variance of the model output by fixing the smallest number of factors. The emphasis of this work is on how to define the concept of importance in an unambiguous way and how to assess it in the simultaneous occurrence of correlated input factors and non-additive models.},
	number = {459},
	urldate = {2021-09-08},
	journal = {Journal of the American Statistical Association},
	author = {Saltelli, Andrea and Tarantola, Stefano},
	month = sep,
	year = {2002},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1198/016214502388618447},
	keywords = {Sensitivity analysis, Analysis of variance, Correlated input, Nonadditive model},
	pages = {702--709},
	file = {Snapshot:/Users/yulei/Zotero/storage/8L2C4376/016214502388618447.html:text/html;Saltelli_Tarantola_2002_On the Relative Importance of Input Factors in Mathematical Models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli_Tarantola_2002_On the Relative Importance of Input Factors in Mathematical Models.pdf:application/pdf},
}

@article{eisenhower2012MethodologyMetamodelBased,
	title = {A methodology for meta-model based optimization in building energy models},
	volume = {47},
	issn = {0378-7788},
	url = {https://www.sciencedirect.com/science/article/pii/S0378778811005962},
	doi = {10.1016/j.enbuild.2011.12.001},
	abstract = {As building energy models become more accurate and numerically efficient, model-based optimization of building design and operation is becoming more practical. The state-of-the-art typically couples an optimizer with a building energy model which tends to be time consuming and often leads to suboptimal results because of the mathematical properties of the energy model. To mitigate this issue, we present an approach that begins by sampling the parameter space of the building model around its baseline. An analytical meta-model is then fit to this data and optimization can be performed using different optimization cost functions or optimization algorithms with very little computational effort. Uncertainty and sensitivity analysis is also performed to identify the most influential parameters for the optimization. A case study is explored using an EnergyPlus model of an existing building which contains over 1000 parameters. When using a cost function that penalizes thermal comfort and energy, 45\% annual energy reduction is achieved while simultaneously increasing thermal comfort by a factor of two. We compare the optimization using the meta-model approach with an approach using the EnergyPlus model integrated with the optimizer on a smaller problem using only seven optimization parameters illustrating good performance.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Energy and Buildings},
	author = {Eisenhower, Bryan and O’Neill, Zheng and Narayanan, Satish and Fonoberov, Vladimir A. and Mezić, Igor},
	month = apr,
	year = {2012},
	keywords = {Machine learning, Sensitivity analysis, Comfort and energy optimization, EnergyPlus},
	pages = {292--301},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/VWMXTWRW/S0378778811005962.html:text/html;Eisenhower et al_2012_A methodology for meta-model based optimization in building energy models.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Eisenhower et al_2012_A methodology for meta-model based optimization in building energy models.pdf:application/pdf},
}

@article{becker2012BayesianSensitivityAnalysis,
	series = {Uncertainties in {Structural} {Dynamics}},
	title = {Bayesian sensitivity analysis of a nonlinear finite element model},
	volume = {32},
	issn = {0888-3270},
	url = {https://www.sciencedirect.com/science/article/pii/S0888327012000866},
	doi = {10.1016/j.ymssp.2012.03.009},
	abstract = {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 model built from a small set of model runs at specified points in input space. Such emulators are massively cheaper to run and can be used to mimic the “true” model, with the result that uncertainty analysis and sensitivity analysis can be performed for a greatly reduced computational cost. The work here investigates the use of an emulator known as a Gaussian process (GP), which is an advanced probabilistic form of regression. The GP is particularly suited to uncertainty analysis since it is able to emulate a wide class of models, and accounts for its own emulation uncertainty. Additionally, uncertainty and sensitivity measures can be estimated analytically, given certain assumptions. The GP approach is explained in detail here, and a case study of a finite element model of an airship is used to demonstrate the method. It is concluded that the GP is a very attractive way of performing uncertainty and sensitivity analysis on large models, provided that the dimensionality is not too high.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Mechanical Systems and Signal Processing},
	author = {Becker, W. and Oakley, J. E. and Surace, C. and Gili, P. and Rowson, J. and Worden, K.},
	month = oct,
	year = {2012},
	keywords = {Gaussian process, Uncertainty, Bayesian, Emulator, Finite element, Sensitivity},
	pages = {18--31},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/WHC3T3KJ/S0888327012000866.html:text/html;Becker et al_2012_Bayesian sensitivity analysis of a nonlinear finite element model.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Becker et al_2012_Bayesian sensitivity analysis of a nonlinear finite element model.pdf:application/pdf},
}

@book{box2005StatisticsExperimentersDesign,
	address = {Hoboken, N.J},
	edition = {2nd ed},
	series = {Wiley series in probability and statistics},
	title = {Statistics for experimenters: design, innovation, and discovery},
	isbn = {978-0-471-71813-0},
	shorttitle = {Statistics for experimenters},
	publisher = {Wiley-Interscience},
	author = {Box, George E. P. and Hunter, J. Stuart and Hunter, William Gordon},
	year = {2005},
	keywords = {Analysis of variance, Experimental design, Mathematical statistics},
}

@article{morris1991FactorialSamplingPlans,
	title = {Factorial {Sampling} {Plans} for {Preliminary} {Computational} {Experiments}},
	volume = {33},
	issn = {0040-1706},
	url = {https://www.tandfonline.com/doi/abs/10.1080/00401706.1991.10484804},
	doi = {10.1080/00401706.1991.10484804},
	abstract = {A computational model is a representation of some physical or other system of interest, first expressed mathematically and then implemented in the form of a computer program; it may be viewed as a function of inputs that, when evaluated, produces outputs. Motivation for this article comes from computational models that are deterministic, complicated enough to make classical mathematical analysis impractical and that have a moderate-to-large number of inputs. The problem of designing computational experiments to determine which inputs have important effects on an output is considered. The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input. Advantages of this approach include a lack of reliance on assumptions of relative sparsity of important inputs, monotonicity of outputs with respect to inputs, or adequacy of a low-order polynomial as an approximation to the computational model.},
	number = {2},
	urldate = {2021-09-08},
	journal = {Technometrics},
	author = {Morris, Max D.},
	month = may,
	year = {1991},
	note = {Publisher: Taylor \& Francis
\_eprint: https://www.tandfonline.com/doi/pdf/10.1080/00401706.1991.10484804},
	keywords = {Sensitivity analysis, Latin hypercube sampling, Computational model, Factor screening},
	pages = {161--174},
	file = {Snapshot:/Users/yulei/Zotero/storage/8SP3LASI/00401706.1991.html:text/html;Morris_1991_Factorial Sampling Plans for Preliminary Computational Experiments.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Morris_1991_Factorial Sampling Plans for Preliminary Computational Experiments.pdf:application/pdf},
}

@article{daveiga2015GlobalSensitivityAnalysis,
	title = {Global sensitivity analysis with dependence measures},
	volume = {85},
	issn = {0094-9655},
	url = {https://doi.org/10.1080/00949655.2014.945932},
	doi = {10.1080/00949655.2014.945932},
	abstract = {Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle multivariate variables in a limited way. In this paper, we introduce a new class of sensitivity indices based on dependence measures which overcomes these insufficiencies. Our approach originates from the idea to compare the output distribution with its conditional counterpart when one of the input variables is fixed. We establish that this comparison yields previously proposed indices when it is performed with Csiszár f-divergences, as well as sensitivity indices which are well-known dependence measures between random variables. This leads us to investigate completely new sensitivity indices based on recent state-of-the-art dependence measures, such as distance correlation and the Hilbert–Schmidt independence criterion. We also emphasize the potential of feature selection techniques relying on such dependence measures as alternatives to screening in high dimension.},
	number = {7},
	urldate = {2021-09-08},
	journal = {Journal of Statistical Computation and Simulation},
	author = {Da Veiga, Sebastien},
	month = may,
	year = {2015},
	note = {Publisher: Taylor \& Francis
\_eprint: https://doi.org/10.1080/00949655.2014.945932},
	keywords = {global sensitivity analysis, independence, kernel, mutual information},
	pages = {1283--1305},
	file = {Snapshot:/Users/yulei/Zotero/storage/9SMFPUPX/00949655.2014.html:text/html;Da Veiga_2015_Global sensitivity analysis with dependence measures.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Da Veiga_2015_Global sensitivity analysis with dependence measures.pdf:application/pdf},
}

@article{razavi2019VARSTOOLToolboxComprehensive,
	title = {{VARS}-{TOOL}: {A} toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis},
	volume = {112},
	issn = {1364-8152},
	shorttitle = {{VARS}-{TOOL}},
	url = {https://www.sciencedirect.com/science/article/pii/S1364815218304766},
	doi = {10.1016/j.envsoft.2018.10.005},
	abstract = {VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.},
	language = {en},
	urldate = {2021-09-08},
	journal = {Environmental Modelling \& Software},
	author = {Razavi, Saman and Sheikholeslami, Razi and Gupta, Hoshin V. and Haghnegahdar, Amin},
	month = feb,
	year = {2019},
	keywords = {Global sensitivity analysis, Uncertainty analysis, Sensitivity indices, Morris, Dynamical systems models, Performance metrics, Progressive Latin hypercube sampling (PLHS), Sobol’, Variogram analysis of response surface (VARS)},
	pages = {95--107},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/N95SGY9D/S1364815218304766.html:text/html;Razavi et al_2019_VARS-TOOL.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Razavi et al_2019_VARS-TOOL.pdf:application/pdf},
}

@article{saltelli2013WhatMakeYour,
	title = {What do {I} make of your latinorumc {Sensitivity} auditing of mathematical modelling},
	volume = {9},
	issn = {1740-2816, 1740-2824},
	url = {http://www.inderscience.com/link.php?id=58610},
	doi = {10.1504/IJFIP.2013.058610},
	language = {en},
	number = {2/3/4},
	urldate = {2021-09-08},
	journal = {International Journal of Foresight and Innovation Policy},
	author = {Saltelli, Andrea and Guimaraes Pereira, Ângela and Sluijs, Jeroen P. Van der and Funtowicz, Silvio},
	year = {2013},
	pages = {213},
	file = {Saltelli et al_2013_What do I make of your latinorumc Sensitivity auditing of mathematical modelling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Saltelli et al_2013_What do I make of your latinorumc Sensitivity auditing of mathematical modelling.pdf:application/pdf},
}

@article{gunawan2005SensitivityAnalysisDiscrete,
	title = {Sensitivity {Analysis} of {Discrete} {Stochastic} {Systems}},
	volume = {88},
	issn = {0006-3495},
	url = {https://www.sciencedirect.com/science/article/pii/S0006349505733087},
	doi = {10.1529/biophysj.104.053405},
	abstract = {Sensitivity analysis quantifies the dependence of system behavior on the parameters that affect the process dynamics. Classical sensitivity analysis, however, does not directly apply to discrete stochastic dynamical systems, which have recently gained popularity because of its relevance in the simulation of biological processes. In this work, sensitivity analysis for discrete stochastic processes is developed based on density function (distribution) sensitivity, using an analog of the classical sensitivity and the Fisher Information Matrix. There exist many circumstances, such as in systems with multistability, in which the stochastic effects become nontrivial and classical sensitivity analysis on the deterministic representation of a system cannot adequately capture the true system behavior. The proposed analysis is applied to a bistable chemical system—the Schlögl model, and to a synthetic genetic toggle-switch model. Comparisons between the stochastic and deterministic analyses show the significance of explicit consideration of the probabilistic nature in the sensitivity analysis for this class of processes.},
	language = {en},
	number = {4},
	urldate = {2021-09-11},
	journal = {Biophysical Journal},
	author = {Gunawan, Rudiyanto and Cao, Yang and Petzold, Linda and Doyle, Francis J.},
	month = apr,
	year = {2005},
	pages = {2530--2540},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/KF8VVQA7/S0006349505733087.html:text/html;Gunawan et al_2005_Sensitivity Analysis of Discrete Stochastic Systems.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Gunawan et al_2005_Sensitivity Analysis of Discrete Stochastic Systems.pdf:application/pdf},
}

@article{adelman1986SensitivityAnalysisDiscrete,
	title = {Sensitivity {Analysis} of {Discrete} {Structural} {Systems}},
	volume = {24},
	issn = {0001-1452},
	url = {https://doi.org/10.2514/3.48671},
	doi = {10.2514/3.48671},
	number = {5},
	urldate = {2021-09-12},
	journal = {AIAA Journal},
	author = {Adelman, Howard M. and Haftka, Raphael T.},
	year = {1986},
	note = {Publisher: American Institute of Aeronautics and Astronautics
\_eprint: https://doi.org/10.2514/3.48671},
	pages = {823--832},
	file = {AIAA Snapshot:/Users/yulei/Zotero/storage/922K8HW9/3.html:text/html;Adelman_Haftka_1986_Sensitivity Analysis of Discrete Structural Systems.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Adelman_Haftka_1986_Sensitivity Analysis of Discrete Structural Systems.pdf:application/pdf},
}

@techreport{dellavigna2018StructuralBehavioralEconomics,
	type = {Working {Paper}},
	title = {Structural {Behavioral} {Economics}},
	url = {https://www.nber.org/papers/w24797},
	abstract = {What is the role of structural estimation in behavioral economics? I discuss advantages, and limitations, of the work in Structural Behavioral Economics. I also cover common modeling choices and how to get started. Among the advantages, I argue that structural estimation builds on, and expands, a classical behavioral tool, simple calibrations, and that it benefits from the presence of a few parsimonious behavioral models which can be taken to the data. Estimation is also well suited for experimental work, common in behavioral economics, as it can lead to improvements in the experimental design. In addition, at a time where policy implications of behavioral work are increasingly discussed, it is important to ground these policy implications in (estimated) models. Structural work, however, has important limitations, which are relevant to its behavioral applications. Estimation takes much longer and the extra degree of complexity can make it difficult to know which of a series of assumptions is driving the results. For related reasons, it is also easy to over-reach with the welfare implications. Taking this into account, I provide a partial how-to guide to structural behavioral economics, covering: (i) the choice of estimation method; (ii) the modeling of heterogeneity; (iii) identification and sensitivity. Finally, I discuss common issues for the estimation of leading behavioral models. I illustrate this discussion with selected coverage of existing work in the literature.},
	number = {24797},
	urldate = {2021-09-12},
	institution = {National Bureau of Economic Research},
	author = {DellaVigna, Stefano},
	month = jul,
	year = {2018},
	doi = {10.3386/w24797},
	note = {Series: Working Paper Series},
	file = {DellaVigna_2018_Structural Behavioral Economics.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/DellaVigna_2018_Structural Behavioral Economics.pdf:application/pdf},
}

@incollection{reiss2007Chapter64Structural,
	title = {Chapter 64 {Structural} {Econometric} {Modeling}: {Rationales} and {Examples} from {Industrial} {Organization}},
	volume = {6},
	shorttitle = {Chapter 64 {Structural} {Econometric} {Modeling}},
	url = {https://www.sciencedirect.com/science/article/pii/S1573441207060643},
	abstract = {This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.},
	language = {en},
	urldate = {2021-09-12},
	booktitle = {Handbook of {Econometrics}},
	publisher = {Elsevier},
	author = {Reiss, Peter C. and Wolak, Frank A.},
	editor = {Heckman, James J. and Leamer, Edward E.},
	month = jan,
	year = {2007},
	doi = {10.1016/S1573-4412(07)06064-3},
	keywords = {auctions, entry, market power, regulation, structural econometric model},
	pages = {4277--4415},
	file = {ScienceDirect Snapshot:/Users/yulei/Zotero/storage/LFPEF43M/S1573441207060643.html:text/html;Reiss_Wolak_2007_Chapter 64 Structural Econometric Modeling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Reiss_Wolak_2007_Chapter 64 Structural Econometric Modeling.pdf:application/pdf},
}

@techreport{orazioattansio2019StructuralModellingPolicymaking,
	title = {Structural {Modelling} in {Policymaking}},
	url = {https://cedilprogramme.org/wp-content/uploads/2018/11/Inception-Paper-9-Orazio-Attanasio-Structural-Modelling-in-Policymaking.pdf},
	urldate = {2021-09-12},
	institution = {The Centre of Excellence for Development Impact and Learning (CEDIL) is an academic consortium supported by UKAID through DFID},
	author = {{Orazio Attansio} and {Debbie Blair}},
	year = {2019},
	file = {Orazio Attansio_Debbie Blair_2019_Structural Modelling in Policymaking.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Orazio Attansio_Debbie Blair_2019_Structural Modelling in Policymaking.pdf:application/pdf},
}

@techreport{browningTwoExamplesStructural,
	title = {Two examples of structural modelling. {Notes} for "{Structural} modelling".},
	language = {en},
	author = {Browning, Martin},
	pages = {9},
	file = {Browning_Two examples of structural modelling.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Browning_Two examples of structural modelling.pdf:application/pdf},
}

@article{attanasio2012EducationChoicesMexico,
	title = {Education {Choices} in {Mexico}: {Using} a {Structural} {Model} and a {Randomized} {Experiment} to {Evaluate} {PROGRESA}},
	volume = {79},
	issn = {0034-6527},
	shorttitle = {Education {Choices} in {Mexico}},
	url = {https://doi.org/10.1093/restud/rdr015},
	doi = {10.1093/restud/rdr015},
	abstract = {In this paper, we use an economic model to analyse data from a major randomized social experiment, namely PROGRESA in Mexico, and to evaluate its impact on school participation. We show the usefulness of using experimental data to estimate a structural economic model as well as the importance of a structural model in interpreting experimental results. The availability of the experiment also allows us to estimate the program's general equilibrium effects, which we then incorporate into our simulations. Our main findings are (i) the program's grant has a much stronger impact on school enrolment than an equivalent reduction in child wages; (ii) the program has a positive effect on the enrollment of children, especially after primary school; this result is well replicated by the parsimonious structural model; (iii) there are sizeable effects of the program on child wages, which, however, reduce the effectiveness of the program only marginally; and (iv) a revenue neutral change in the program that would increase the grant for secondary school children while eliminating for the primary school children would have a substantially larger effect on enrollment of the latter, while having minor effects on the former.},
	number = {1},
	urldate = {2021-09-12},
	journal = {The Review of Economic Studies},
	author = {Attanasio, Orazio P. and Meghir, Costas and Santiago, Ana},
	month = jan,
	year = {2012},
	pages = {37--66},
	file = {Snapshot:/Users/yulei/Zotero/storage/MD8GDC4G/1562110.html:text/html;Attanasio et al_2012_Education Choices in Mexico.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Attanasio et al_2012_Education Choices in Mexico.pdf:application/pdf},
}

@article{low2017UseStructuralModels,
	title = {The {Use} of {Structural} {Models} in {Econometrics}},
	volume = {31},
	issn = {0895-3309},
	url = {https://www.aeaweb.org/articles?id=10.1257/jep.31.2.33},
	doi = {10.1257/jep.31.2.33},
	abstract = {This paper discusses the role of structural economic models in empirical analysis and policy design. The central payoff of a structural econometric model is that it allows an empirical researcher to go beyond the conclusions of a more conventional empirical study that provides reduced-form causal relationships. Structural models identify mechanisms that determine outcomes and are designed to analyze counterfactual policies, quantifying impacts on specific outcomes as well as effects in the short and longer run. We start by defining structural models, distinguishing between those that are fully specified and those that are partially specified. We contrast the treatment effects approach with structural models, and present an example of how a structural model is specified and the particular choices that were made. We cover combining structural estimation with randomized experiments. We then turn to numerical techniques for solving dynamic stochastic models that are often used in structural estimation, again with an example. The penultimate section focuses on issues of estimation using the method of moments.},
	language = {en},
	number = {2},
	urldate = {2021-09-12},
	journal = {Journal of Economic Perspectives},
	author = {Low, Hamish and Meghir, Costas},
	month = may,
	year = {2017},
	keywords = {Single Equation Models, Single Variables: General, Model Construction and Estimation},
	pages = {33--58},
	file = {Snapshot:/Users/yulei/Zotero/storage/55VR24GW/articles.html:text/html;Low_Meghir_2017_The Use of Structural Models in Econometrics.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Low_Meghir_2017_The Use of Structural Models in Econometrics.pdf:application/pdf},
}

@book{train2009DiscreteChoiceMethods,
	address = {Cambridge},
	edition = {2},
	title = {Discrete {Choice} {Methods} with {Simulation}},
	isbn = {978-0-521-76655-5},
	url = {https://www.cambridge.org/core/books/discrete-choice-methods-with-simulation/49CABD00F3DDDA088A8FBFAAAD7E9546},
	abstract = {This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.},
	urldate = {2021-09-12},
	publisher = {Cambridge University Press},
	author = {Train, Kenneth E.},
	year = {2009},
	doi = {10.1017/CBO9780511805271},
	file = {Snapshot:/Users/yulei/Zotero/storage/FC35MEJC/49CABD00F3DDDA088A8FBFAAAD7E9546.html:text/html;Train_2009_Discrete Choice Methods with Simulation.pdf:/Users/yulei/Dropbox/ZoteroAttachments/master thesis/Train_2009_Discrete Choice Methods with Simulation.pdf:application/pdf},
}

@article{keane2009EmpiricalApplicationsDiscrete,
	title = {Empirical applications of discrete choice dynamic programming models},
	volume = {12},
	issn = {1094-2025},
	url = {https://www.sciencedirect.com/science/article/pii/S1094202508000318},
	doi = {10.1016/j.red.2008.07.001},
	abstract = {The development over the past 25 years of methods for the estimation of discrete choice

license: MIT

license_type: permissive

branch_name: refs/heads/main

extension: bib