{"title":"The pymcdm-reidentify tool: Advanced methods for MCDA model re-identification","authors":"Bartłomiej Kizielewicz, Wojciech Sałabun","doi":"10.1016/j.softx.2024.101960","DOIUrl":null,"url":null,"abstract":"<div><div>The <span>pymcdm-reidentify</span> tool addresses the challenge of reconstructing multi-criteria decision analysis (MCDA) and decision-making (MCDM) models when original parameters are unavailable, but rankings are known. This Python package integrates with existing MCDA libraries and uses stochastic optimization to determine model parameters such as criterion weights and reference objects. Built on the <span>pymcdm</span> and <span>Mealpy</span> libraries, <span>pymcdm-reidentify</span> offers advanced methods for model re-identification, including visualization and fuzzy normalization. Its capabilities facilitate the update and adaptation of decision models, enhancing accuracy and efficiency in both academic and practical applications.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101960"},"PeriodicalIF":2.4000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024003303","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
The pymcdm-reidentify tool addresses the challenge of reconstructing multi-criteria decision analysis (MCDA) and decision-making (MCDM) models when original parameters are unavailable, but rankings are known. This Python package integrates with existing MCDA libraries and uses stochastic optimization to determine model parameters such as criterion weights and reference objects. Built on the pymcdm and Mealpy libraries, pymcdm-reidentify offers advanced methods for model re-identification, including visualization and fuzzy normalization. Its capabilities facilitate the update and adaptation of decision models, enhancing accuracy and efficiency in both academic and practical applications.
期刊介绍：
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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