JOSS paper

.github/workflows/joss_paper.yml

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+
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: joss/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: joss/paper.pdf

joss/paper.bib

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+@article{Herman2017,
+  doi = {10.21105/joss.00097},
+  year  = {2017},
+  month = {jan},
+  publisher = {The Open Journal},
+  volume = {2},
+  number = {9},
+  author = {Jon Herman and Will Usher},
+  title = {{SALib}: An open-source Python library for Sensitivity Analysis},
+  journal = {The Journal of Open Source Software}
+}
+
+@article{Kozlova2024,
+  title = {Uncovering heterogeneous effects in computational models for sustainable decision-making},
+  journal = {Environmental Modelling & Software},
+  volume = {171},
+  pages = {105898},
+  year = {2024},
+  issn = {1364-8152},
+  doi = {10.1016/j.envsoft.2023.105898},
+  author = {Mariia Kozlova and Robert J. Moss and Julian Scott Yeomans and Jef Caers},
+  keywords = {Global sensitivity analysis, Simulation decomposition, Monte Carlo simulation, Decision-making under uncertainty}
+}
+
+@article{Roy2023,
+  doi = {10.21105/joss.05309},
+  year = {2023},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {84},
+  pages = {5309},
+  author = {Pamphile T. Roy and Art B. Owen and Maximilian Balandat and Matt Haberland},
+  title = {Quasi-Monte Carlo Methods in Python},
+  journal = {Journal of Open Source Software}
+}
+
+@article{Virtanen2020,
+  title={SciPy 1.0: fundamental algorithms for scientific computing in {P}ython},
+  author={Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan et al.},
+  journal={Nature methods},
+  volume={17},
+  number={3},
+  pages={261--272},
+  year={2020},
+  publisher={Nature Publishing Group},
+  doi={10.1038/s41592-019-0686-2}
+}
+
+@book{Saltelli2007,
+author = {Saltelli, Andrea and Ratto, Marco and Andres, Terry and Campolongo, Francesca and Cariboni, Jessica and Gatelli, Debora and Saisana, Michaela and Tarantola, Stefano},
+booktitle = {Global Sensitivity Analysis. The Primer},
+doi = {10.1002/9780470725184},
+isbn = {9780470725184},
+month = {dec},
+pages = {237--275},
+publisher = {John Wiley {\&} Sons, Ltd},
+title = {{Global Sensitivity Analysis. The Primer}},
+year = {2007}
+}
+
+@article{sobol1993,
+  title={Sensitivity analysis for non-linear mathematical models, originally “Sensitivity estimates for non-linear mathematical models”},
+  author={Sobol, Ilya M},
+  journal={Math Model Comput Exp},
+  volume={1},
+  pages={407--414},
+  year={1993}
+}
+
+@report{europeancommission2021,
+  title = {Better {{Regulation Toolbox}}},
+  author = {{European Commission}},
+  date = {2021-11},
+  url = {https://ec.europa.eu/info/law/law-making-process/planning-and-proposing-law/better-regulation-why-and-how/better-regulation-guidelines-and-toolbox_en},
+  city = {Brussels},
+  keywords = {\#nosource}
+}
+
+@software{panel,
+  author       = {Philipp Rudiger and
+                  Marc Skov Madsen and
+                  Simon Høxbro Hansen and
+                  Maxime Liquet and
+                  Andrew and
+                  Xavier Artusi and
+                  James A. Bednar and
+                  Chris B and
+                  Jean-Luc Stevens and
+                  Christoph Deil and
+                  Demetris Roumis and
+                  Julia Signell and
+                  Mateusz Paprocki and
+                  Jerry Wu and
+                  Jon Mease and
+                  Arne and
+                  Coderambling and
+                  Hugues-Yanis Amanieu and
+                  thuydotm and
+                  Simon and
+                  sdc50 and
+                  Luca Fabbri and
+                  kbowen and
+                  Theom and
+                  Joel Ostblom and
+                  Govinda Totla and
+                  Niko Föhr and
+                  TBym},
+  title        = {holoviz/panel: Version 1.4.3},
+  month        = may,
+  year         = 2024,
+  publisher    = {Zenodo},
+  version      = {v1.4.3},
+  doi          = {10.5281/zenodo.11261266},
+  url          = {https://doi.org/10.5281/zenodo.11261266}
+}

joss/paper.md

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+---
+title: 'Simulation Decomposition in Python'
+tags:
+  - Python
+  - SimDec
+  - statistics
+  - Sensitivity Analysis
+  - Visualization
+authors:
+  - name: Pamphile T. Roy
+    affiliation: 1
+    corresponding: true
+    orcid: 0000-0001-9816-1416
+  - name: Mariia Kozlova
+    affiliation: 2
+    orcid: 0000-0002-6952-7682
+affiliations:
+    - name: Consulting Manao GmbH, Vienna, Austria
+      index: 1
+    - name: LUT Business School, LUT University, Lappeenranta, Finland
+      index: 2
+date: 1 April 2024
+bibliography: paper.bib
+
+---
+
+# 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 Artificial
+Intelligence and new regulations such as the [AI Act](https://artificialintelligenceact.eu) or the
+*Better Regulation Guideline* [@europeancommission2021], 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) [@Saltelli2007]. The indices of Sobol' are a
+prominent example of such methods [@sobol1993].
+
+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 [@Kozlova2024]. 
+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 the multivariable scenarios, formed out of the most
+influential input variables. The resulting visualization shows how different
+output ranges can be achieved and what kind of critical interactions affect
+the output–as seen in \autoref{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.\label{fig:simdec}](simdec_presentation.png)
+
+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](https://simdec.io). The project
+relies on powerful variance-based sensitivity analysis methods from SALib [@Herman2017] and
+SciPy [@Virtanen2020; @Roy2023]—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 [@panel].
+
+# Acknowledgements
+
+The work on this open-source software was supported by grant #220177 from
+Finnish Foundation for Economic Foundation.
+
+# References