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 decision-making problems and seeking the most appropriate solutions based on MCDA methods.

翻译：目的：多准则决策分析（MCDA）在复杂环境中的决策过程中日益重要。为满足这一需求，开发了基于Python实现的pyDecision库（可从https://bit.ly/3tLFGtH获取），该库提供了一套全面且易于获取的MCDA方法。方法：pyDecision包含70种MCDA方法，包括AHP、TOPSIS以及PROMETHEE和ELECTRE家族。除了提供广泛的技术手段外，该库还配备了可视化工具，以便更直观地解读结果。此外，pyDecision集成了先进的大语言模型ChatGPT，决策者可通过ChatGPT讨论并比较不同方法的结果，从而更互动、更直观地理解解决方案。发现：大语言模型无疑功能强大，但有时可能是一把双刃剑。若未经严格验证其输出结果，尤其对于缺乏深厚领域专业知识的研究人员，其答案可能产生误导。必须以审慎的态度和扎实的相关领域基础来对待其见解。原创性：通过将MCDA方法与ChatGPT相结合，pyDecision为科学界做出了重要贡献，它成为研究人员、实践者和决策者在应对复杂决策问题、根据MCDA方法寻求最合适解决方案时的宝贵资源。