diff --git a/joss.06713/10.21105.joss.06713.crossref.xml b/joss.06713/10.21105.joss.06713.crossref.xml new file mode 100644 index 0000000000..73fb312a07 --- /dev/null +++ b/joss.06713/10.21105.joss.06713.crossref.xml @@ -0,0 +1,203 @@ + + + + 20240610102645-17ee33cc44f483b511e6487e93c1530fb0b0a05b + 20240610102645 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 06 + 2024 + + + 9 + + 98 + + + + Simulation Decomposition in Python + + + + Pamphile T. + Roy + https://orcid.org/0000-0001-9816-1416 + + + Mariia + Kozlova + https://orcid.org/0000-0002-6952-7682 + + + + 06 + 10 + 2024 + + + 6713 + + + 10.21105/joss.06713 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.11535796 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6713 + + + + 10.21105/joss.06713 + https://joss.theoj.org/papers/10.21105/joss.06713 + + + https://joss.theoj.org/papers/10.21105/joss.06713.pdf + + + + + + SALib: An open-source Python library for +sensitivity analysis + Herman + The Journal of Open Source +Software + 9 + 2 + 10.21105/joss.00097 + 2017 + Herman, J., & Usher, W. (2017). +SALib: An open-source Python library for sensitivity analysis. The +Journal of Open Source Software, 2(9). +https://doi.org/10.21105/joss.00097 + + + Uncovering heterogeneous effects in +computational models for sustainable decision-making + Kozlova + Environmental Modelling & +Software + 171 + 10.1016/j.envsoft.2023.105898 + 1364-8152 + 2024 + Kozlova, M., Moss, R. J., Yeomans, J. +S., & Caers, J. (2024). Uncovering heterogeneous effects in +computational models for sustainable decision-making. Environmental +Modelling & Software, 171, 105898. +https://doi.org/10.1016/j.envsoft.2023.105898 + + + Quasi-monte carlo methods in +Python + Roy + Journal of Open Source +Software + 84 + 8 + 10.21105/joss.05309 + 2023 + Roy, P. T., Owen, A. B., Balandat, +M., & Haberland, M. (2023). Quasi-monte carlo methods in Python. +Journal of Open Source Software, 8(84), 5309. +https://doi.org/10.21105/joss.05309 + + + SciPy 1.0: Fundamental algorithms for +scientific computing in Python + Virtanen + Nature methods + 3 + 17 + 10.1038/s41592-019-0686-2 + 2020 + Virtanen, P., Gommers, R., Oliphant, +T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, +P., Weckesser, W., & Bright, J. et al. (2020). SciPy 1.0: +Fundamental algorithms for scientific computing in Python. Nature +Methods, 17(3), 261–272. +https://doi.org/10.1038/s41592-019-0686-2 + + + Global Sensitivity Analysis. The +Primer + Saltelli + 10.1002/9780470725184 + 9780470725184 + 2007 + Saltelli, A., Ratto, M., Andres, T., +Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, +S. (2007). Global Sensitivity Analysis. The Primer (pp. 237–275). John +Wiley & Sons, Ltd. +https://doi.org/10.1002/9780470725184 + + + Sensitivity analysis for non-linear +mathematical models, originally “sensitivity estimates for non-linear +mathematical models” + Sobol + Math Model Comput Exp + 1 + 1993 + Sobol, I. M. (1993). Sensitivity +analysis for non-linear mathematical models, originally “sensitivity +estimates for non-linear mathematical models.” Math Model Comput Exp, 1, +407–414. + + + Better Regulation Toolbox + European Commission + 2021 + European Commission. (2021). Better +Regulation Toolbox. +https://ec.europa.eu/info/law/law-making-process/planning-and-proposing-law/better-regulation-why-and-how/better-regulation-guidelines-and-toolbox_en + + + Holoviz/panel: Version 1.4.3 + Rudiger + 10.5281/zenodo.11261266 + 2024 + Rudiger, P., Madsen, M. S., Hansen, +S. H., Liquet, M., Andrew, Artusi, X., Bednar, J. A., B, C., Stevens, +J.-L., Deil, C., Roumis, D., Signell, J., Paprocki, M., Wu, J., Mease, +J., Arne, Coderambling, Amanieu, H.-Y., thuydotm, … TBym. (2024). +Holoviz/panel: Version 1.4.3 (Version v1.4.3). Zenodo. +https://doi.org/10.5281/zenodo.11261266 + + + + + + diff --git a/joss.06713/10.21105.joss.06713.pdf b/joss.06713/10.21105.joss.06713.pdf new file mode 100644 index 0000000000..536ff37260 Binary files /dev/null and b/joss.06713/10.21105.joss.06713.pdf differ diff --git a/joss.06713/paper.jats/10.21105.joss.06713.jats b/joss.06713/paper.jats/10.21105.joss.06713.jats new file mode 100644 index 0000000000..33bbef97a8 --- /dev/null +++ b/joss.06713/paper.jats/10.21105.joss.06713.jats @@ -0,0 +1,349 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6713 +10.21105/joss.06713 + +Simulation Decomposition in Python + + + +https://orcid.org/0000-0001-9816-1416 + +Roy +Pamphile T. + + +* + + +https://orcid.org/0000-0002-6952-7682 + +Kozlova +Mariia + + + + + +Consulting Manao GmbH, Vienna, Austria + + + + +LUT Business School, LUT University, Lappeenranta, +Finland + + + + +* E-mail: + + +1 +4 +2024 + +9 +98 +6713 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +SimDec +statistics +Sensitivity Analysis +Visualization + + + + + + Summary +

Uncertainties are everywhere. Whether you are developing a new + Artificial Intelligence (AI) system, running complex simulations or + making an experiment in a lab, uncertainties influence the system. + Therefore, an approach is needed to understand how these uncertainties + impact the system’s performance.

+

SimDec offers a novel visual way to understand the intricate role + that uncertainties play. A clear Python Application Programming + Interface (API) and a no-code interactive web dashboard make + uncertainty analysis with SimDec accessible to everyone.

+ + + Statement of need +

From real life experiments to numerical simulations, uncertainties + play a crucial role in the system under study. With the advent of AI + and new regulations such as the + AI + Act or the Better Regulation Guideline + (European + Commission, 2021), there is a growing need for explainability + and impact assessments of systems under uncertainties.

+

Traditional methods to analyse the uncertainties focus on + quantitative methods to compare the importance of factors, there is a + large body of literature and the field is known as: Sensitivity + Analysis (SA) + (Saltelli + et al., 2007). The indices of Sobol’ are a prominent example of + such methods + (Sobol, + 1993).

+

Simulation Decomposition or SimDec moves the field of SA forward by + supplementing the computation of sensitivity indices with the + visualization of the type of interactions involved, which proves + critical for understanding the system’s behavior and decision-making + (Kozlova + et al., 2024). In short, SimDec is a hybrid + uncertainty-sensitivity analysis approach that reveals the critical + behavior of a computational model or an empirical dataset. It + decomposes the distribution of the output (target variable) by + automatically forming scenarios that reveal the most critical behavior + of the system. The scenarios are formed out of the most influential + input variables (defined with variance-based sensitivity indices) by + breaking down their numeric ranges into states + (e.g. low and high) and creating an + exhaustive list of their combinations (e.g. (i) low + A & low + B, (ii) low + A & high + B, (iii) high + A & low + B, and (iv) high + A and high + B). The resulting visualization shows + how different output ranges can be achieved and what kind of critical + interactions affect the output—as seen in + [fig:simdec]. The + method has shown value for various computational models from different + fields, including business, environment, and engineering, as well as + an emerging evidence of use for empirical data and AI.

+ +

SimDec: explanation of output by most important inputs. + A simulation dataset of a structural reliability model with one key + output variable and four input variables is used for this case. + Inputs 3 and 1 have the highest sensitivity indices and thus are + automatically chosen for decomposition. The most influential input 3 + divides the distribution of the output into three main states with + distinct colors. Input 1 further subdivides them into shades. From + the graph, it becomes obvious that input 1 influences the output + when input 3 is low, but has a negligible effect when input 3 is + medium or + high.

+ + +

Besides proposing a comprehensive yet simple API through a Python + package available on PyPi, SimDec is also made available to + practitioners through an online dashboard at + https://simdec.io. + The project relies on powerful variance-based sensitivity analysis + methods from SALib + (Herman + & Usher, 2017) and SciPy + (Roy et + al., 2023; + Virtanen + et al., 2020)—notably the Quasi-Monte Carlo capabilities with + sp.stats.qmc and in the future sensitivity + indices with sp.stats.sensitivity_indices. The + dashboard is made possible thanks to Panel + (Rudiger + et al., 2024).

+ + + Acknowledgements +

The work on this open-source software was supported by grant + #220177 from Finnish Foundation for Economic Foundation.

+ + + + + + + + + HermanJon + UsherWill + + SALib: An open-source Python library for sensitivity analysis + The Journal of Open Source Software + The Open Journal + 201701 + 2 + 9 + 10.21105/joss.00097 + + + + + + KozlovaMariia + MossRobert J. + YeomansJulian Scott + CaersJef + + Uncovering heterogeneous effects in computational models for sustainable decision-making + Environmental Modelling & Software + 2024 + 171 + 1364-8152 + 10.1016/j.envsoft.2023.105898 + 105898 + + + + + + + RoyPamphile T. + OwenArt B. + BalandatMaximilian + HaberlandMatt + + Quasi-monte carlo methods in Python + Journal of Open Source Software + The Open Journal + 2023 + 8 + 84 + 10.21105/joss.05309 + 5309 + + + + + + + VirtanenPauli + GommersRalf + OliphantTravis E + HaberlandMatt + ReddyTyler + CournapeauDavid + BurovskiEvgeni + PetersonPearu + WeckesserWarren + BrightJonathan et al. + + SciPy 1.0: Fundamental algorithms for scientific computing in Python + Nature methods + Nature Publishing Group + 2020 + 17 + 3 + 10.1038/s41592-019-0686-2 + 261 + 272 + + + + + + SaltelliAndrea + RattoMarco + AndresTerry + CampolongoFrancesca + CariboniJessica + GatelliDebora + SaisanaMichaela + TarantolaStefano + + Global Sensitivity Analysis. The Primer + John Wiley & Sons, Ltd + 200712 + 9780470725184 + 10.1002/9780470725184 + 237 + 275 + + + + + + SobolIlya M + + Sensitivity analysis for non-linear mathematical models, originally “sensitivity estimates for non-linear mathematical models” + Math Model Comput Exp + 1993 + 1 + 407 + 414 + + + + + + European Commission + + Better Regulation Toolbox + 202111 + https://ec.europa.eu/info/law/law-making-process/planning-and-proposing-law/better-regulation-why-and-how/better-regulation-guidelines-and-toolbox_en + + + + + + RudigerPhilipp + MadsenMarc Skov + HansenSimon Høxbro + LiquetMaxime + Andrew + ArtusiXavier + BednarJames A. + BChris + StevensJean-Luc + DeilChristoph + RoumisDemetris + SignellJulia + PaprockiMateusz + WuJerry + MeaseJon + Arne + Coderambling + AmanieuHugues-Yanis + thuydotm + Simon + sdc50 + FabbriLuca + kbowen + Theom + OstblomJoel + TotlaGovinda + FöhrNiko + TBym + + Holoviz/panel: Version 1.4.3 + Zenodo + 202405 + https://doi.org/10.5281/zenodo.11261266 + 10.5281/zenodo.11261266 + + + + +
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