JOSS Paper - Correct References

Author: mspada

JOSS_paper/paper.bib

@@ -13,7 +13,7 @@ @article{pereira_enhancing_2024
 	title = {Enhancing Decision Analysis with a Large Language Model: {pyDecision} a Comprehensive Library of {MCDA} Methods in Python},
 	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},
+	journaltitle = {ArXiv},
 	author = {Pereira, Valdecy and Basilio, Marcio Pereira and Santos, Carlos Henrique Tarjano},
 	date = {2024},
 }
@@ -125,7 +125,7 @@ @article{fusco_spatial_2018
 
 @incollection{roy_decision_1996,
 	address = {Boston, MA},
-	title = {Decision {Aiding}: {Major} {Actors} and the {Role} of {Models}},
+	title = {Decision aiding: {Major} actors and the role of models},
 	isbn = {978-1-4757-2500-1},
 	url = {https://doi.org/10.1007/978-1-4757-2500-1_2},
 	abstract = {Before describing how this book approaches the idea of decision aiding, we must examine the idea of a model. We define a model in Section 2.1 and discuss the types of models treated in this book — namely, conscious models possessing some explicit form.},
@@ -140,10 +140,10 @@ @incollection{roy_decision_1996
 
 @incollection{bouyssou_problem_2006,
 	address = {Boston, MA},
-	title = {Problem {Formulation} and {Structuring}: {The} {Decision} {Aiding} {Process}},
+	title = {Problem formulation and structuring: {The} decision aiding process},
 	isbn = {978-0-387-31099-2},
 	url = {https://doi.org/10.1007/0-387-31099-1_2},
-	booktitle = {Evaluation and {Decision} {Models} with {Multiple} {Criteria}: {Stepping} stones for the analyst},
+	booktitle = {Evaluation and {Decision} {Models} with {Multiple} {Criteria}: {Stepping} {Stones} for the {Analyst}},
 	publisher = {Springer US},
 	editor = {Bouyssou, Denis and Marchant, Thierry and Pirlot, Marc and Tsoukiàs, Alexis and Vincke, Philippe},
 	year = {2006},
@@ -152,7 +152,7 @@ @incollection{bouyssou_problem_2006
 }
 
 @article{greco_methodological_2019,
-	title = {On the {Methodological} {Framework} of {Composite} {Indices}: {A} {Review} of the {Issues} of {Weighting}, {Aggregation}, and {Robustness}},
+	title = {On the methodological framework of composite indices: {A} review of the issues of weighting, aggregation, and robustness},
 	volume = {141},
 	issn = {0303-8300, 1573-0921},
 	shorttitle = {On the {Methodological} {Framework} of {Composite} {Indices}},
@@ -162,7 +162,7 @@ @article{greco_methodological_2019
 	language = {en},
 	number = {1},
 	urldate = {2023-11-06},
-	journal = {Social Indicators Research},
+	journal = {Social {Indicators} {Research}},
 	author = {Greco, Salvatore and Ishizaka, Alessio and Tasiou, Menelaos and Torrisi, Gianpiero},
 	month = jan,
 	year = {2019},
@@ -219,7 +219,7 @@ @article{volkart_interdisciplinary_2016
 
 @incollection{stewart_dealing_2016,
 	address = {New York, NY},
-	title = {Dealing with {Uncertainties} in {MCDA}},
+	title = {Dealing with uncertainties in {MCDA}},
 	isbn = {978-1-4939-3094-4},
 	url = {https://doi.org/10.1007/978-1-4939-3094-4_12},
 	abstract = {This chapter presents various approaches to incorporating formal modelling of risks and uncertainties into multi-criteria decision analysis, in a theoretically valid but also operationally meaningful manner. We consider both internal uncertainties (in the formulation and modelling of the decision problem), and external uncertainties arising from exogenous factors, but with greater attention paid to the latter. After a broad discussion on the meaning of uncertainty, we first review approaches to sensitivity analysis, which is particularly, although not exclusively, relevant to internal uncertainties. We discuss the role, but also some limitations, of representing uncertainties in formal probabilistic structures, linked also to concepts of expected (multi-attribute) utility theory. Such probability/utility approaches may be used in explicitly identifying a most preferred solution, or simply to eliminate certain courses of action when stochastically dominated (in various senses) by others. In some contexts it may be useful to view minimization of various risk measures as additional criteria in more standard MCDA models, and we comment on advantages and disadvantages of such approaches. Finally we discuss the integration of MCDA with scenario planning, in order to deal with deeper uncertainties (not easily if at all representable by probability models), particularly in a strategic planning context. The emphasis throughout is on the practice of MCDA rather than on esoteric theoretical results.},