The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Razavi, Saman; Jakeman, Anthony; Saltelli, Andrea; Prieur, Clémentine; Iooss, Bertrand; Borgonovo, Emanuele; Plischke, Elmar; Piano, Samuele Lo; Iwanaga, Takuya; Becker, William E.; Tarantola, Stefano; Guillaume, Joseph H. A.; Jakeman, John Davis; Gupta, Hoshin; Melillo, Nicola; Rabitti, Giovanni; Chabridon, Vincent; Duan, Qingyun; Sun, Xifu; Smith, Stefán Thor; Sheikholeslami, Razi; Hosseini, Nasim; Asadzadeh, Masoud; Puy, Arnald; Kucherenko, Sergei; Maier, Holger R.

doi:10.1016/j.envsoft.2020.104954

Cited by 355 publications

(207 citation statements)

References 260 publications

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“…Therefore, geographic modeling and simulation systems should support not only open collaboration but also the solution exploration of geographic problems. First, to meet the needs of solving complex geo-problems, geographic modeling and simulation systems should be able to describe and archive the iterative exploration process that consists of different activities, including sensitivity analysis and uncertainty analysis (Jakeman et al, 2006;Badham et al, 2019;Hamilton et al, 2019;Koo et al, 2020aKoo et al, , 2020bRazavi et al, 2021). In the modeling scenarios for different geographic problems, modeling and simulation systems should support participants in sharing their knowledge and experience and help to establish different modeling and simulation solutions that can lead different participants to high-level collaboration (Simão et al, 2009;Zare et al, 2020).…”

Section: Exploratory Geographic Modeling and Simulationmentioning

confidence: 99%

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Chen

Zhou

et al. 2021

Sci. China Earth Sci.

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Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

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Section: Exploratory Geographic Modeling and Simulationmentioning

confidence: 99%

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Chen

Zhou

et al. 2021

Sci. China Earth Sci.

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“…It is quite unfortunately that the authors seem to be utterly unaware, with the exception of Davis et al (2007), of this rich existing body of literature on exploratory modeling. The reported case study could have benefitted from various techniques for model analysis like scenario discovery (Bryant & Lempert, 2010;Kwakkel & Jaxa-Rozen, 2016) and global sensitivity analysis (Razavi et al, 2021).…”

Section: E Xplor Atory Modeling Is Alre Ady Well E S Tab Lis Hedmentioning

confidence: 99%

Reaction: A commentary on Lustick and Tetlock (2021)

Kwakkel

Auping

2021

Futures & Foresight Science

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Lustick and Tetlock (2021) argue for the use of theory-guided simulation for aiding geopolitical planning and decision-making. This is a very welcome contribution, as in our experience, geopolitical analyses are often qualitative in nature and prone to group think. The complexities of geopolitical issues, however, really call for the use of theory-guided simulations. These issues are too complex for mental simulation (Atkins et al., 2002;Sterman, 1989Sterman, , 1994. Dynamic simulations, if properly grounded in appropriate theories and wellmotivated assumptions, can derive the possible dynamics from interacting nonlinear processes, and thus aid human reasoning about system behavior (Sterman, 2002). Moreover, since geopolitical issues are subject to uncertainty, scarce data, and conflicting information, using an ensemble modeling approach is appropriate. An ensemble

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“…Moreover, Iooss and Prieur reported that The so-called Sobol' indices [...], present a difficult interpretation in the presence of statistical dependence between inputs [21]. Finally, a recent position paper Razavi et al reported that The field of SA in terms of methods to handle input constraints and correlation structures is still embryonic [43].…”

Section: Introductionmentioning

confidence: 99%

A latent variable approach to account for correlated inputs in global sensitivity analysis

Melillo

Darwich

2021

J Pharmacokinet Pharmacodyn

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In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

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The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Cited by 355 publications

References 260 publications

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Reaction: A commentary on Lustick and Tetlock (2021)

A latent variable approach to account for correlated inputs in global sensitivity analysis

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