Draft paper

Author: tupui

joss/paper.md

@@ -19,18 +19,52 @@ affiliations:
       index: 1
     - name: LUT Business School, LUT University, Lappeenranta, Finland
       index: 2
-date: 1 November 2023
+date: 1 April 2024
 bibliography: paper.bib
 
 ---
 
 # Summary
 
-...
+Uncertainties are everywhere! Whether you are developing a new AI system,
+running complex simulations or making an experiment in a lab, uncertainties
+influence the system. And you need a way to understand how these impact your
+results.
+
+SimDec offers a novel visual way to understand the intricate role that
+uncertainties play. Thanks to a clear API and our dashboard, we are making
+uncertainty analysis accessible to everyone.
 
 # Statement of need
 
-Some citations. SALib [@salib2022qmc].
+From real life experiments to numerical simulations, uncertainties play a
+crucial role in the system under study. With the avenment of Artificial
+Intelligence and new regulations such as the AI Act or the
+*Better Regulation Guideline*, 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 know as: Sensitivity Analysis (SA). The indices of Sobol' are a
+prominent example of such methods.
+
+Simulation Decomposition 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. 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.
+
+Besides proposing a comprehensive yet simple API, SimDec is also made available
+to practitioners through an online dashboard at https://simdec.io. The
+innovative methods of SimDec, the project relies on powerful methods
+from SALib [@salib2022qmc] and SciPy [@scipy2021qmc]-notably the QMC
+capabilities with `sp.stats.qmc` and sensitivity indices with
+`sp.stats.sensitivity_indices`-and the dashboard
+is made possible thanks to Panel.
 
 # Acknowledgements