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base repository: wetransform-os/ProMCDA
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head repository: wetransform-os/ProMCDA
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compare: 5790d7d7639d4a35bf432c92f8d879298d843ca8
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Showing with 30 additions and 20 deletions.
  1. +4 −2 JOSS_paper/paper.bib
  2. +26 −18 JOSS_paper/paper.md
6 changes: 4 additions & 2 deletions JOSS_paper/paper.bib
Original file line number Diff line number Diff line change
@@ -11,7 +11,7 @@ @article{cinelli_mcda_2020

@article{pereira_enhancing_2024,
title = {Enhancing Decision Analysis with a Large Language Model: {pyDecision} a Comprehensive Library of {MCDA} Methods in Python},
doi = {https://doi.org/10.48550/arXiv.2404.06370},
doi = {10.48550/arXiv.2404.06370},
abstract = {Purpose: Multicriteria decision analysis ({MCDA}) has become increasingly essential for decision-making in complex environments. In response to this need, the {pyDecision} library, implemented in Python and available at https://bit.ly/3tLFGtH, has been developed to provide a comprehensive and accessible collection of {MCDA} methods. Methods: The {pyDecision} offers 70 {MCDA} methods, including {AHP}, {TOPSIS}, and the {PROMETHEE} and {ELECTRE} families. Beyond offering a vast range of techniques, the library provides visualization tools for more intuitive results interpretation. In addition to these features, {pyDecision} has integrated {ChatGPT}, an advanced Large Language Model, where decision-makers can use {ChatGPT} to discuss and compare the outcomes of different methods, providing a more interactive and intuitive understanding of the solutions. Findings: Large Language Models are undeniably potent but can sometimes be a double-edged sword. Its answers may be misleading without rigorous verification of its outputs, especially for researchers lacking deep domain expertise. It's imperative to approach its insights with a discerning eye and a solid foundation in the relevant field. Originality: With the integration of {MCDA} methods and {ChatGPT}, {pyDecision} is a significant contribution to the scientific community, as it is an invaluable resource for researchers, practitioners, and decision-makers navigating complex decisionmaking problems and seeking the most appropriate solutions based on {MCDA} methods.},
journaltitle = {Arvix},
author = {Pereira, Valdecy and Basilio, Marcio Pereira and Santos, Carlos Henrique Tarjano},
@@ -68,6 +68,7 @@ @article{kizielewicz2023pymcdm
volume={22},
pages={101368},
year={2023},
doi = {10.1016/j.softx.2023.101368},
publisher={Elsevier}
}

@@ -171,6 +172,7 @@ @article{greco_methodological_2019
@article{klaus_schwab_global_2018,
title = {The global competitiveness report 2018},
url = {https://apo.org.au/node/262576},
isbn = {978-92-95044-76-0},
abstract = {The 2018 edition of the Global Competitiveness Report represents a milestone in the four-decade history of the series, with the introduction of the new Global Competitiveness Index 4.0. The new index sheds light on an emerging set of drivers of productivity and long-term growth in the era of the Fourth Industrial Revolution.},
author = {{Klaus Schwab}},
year = {2018},
@@ -241,4 +243,4 @@ @article{langhans_method_2014
keywords = {Aggregation, Multi-attribute value theory, Multi-criteria decision analysis, One-out all-out, River assessment, Water Framework Directive},
pages = {494--507},
file = {Langhans-2014-The method matters_ A guide for:C\:\\Users\\spaa\\Zotero\\storage\\VTIJFTBP\\Langhans-2014-The method matters_ A guide for.pdf:application/pdf},
}
}
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