Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python
<|reference_start|>Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python: 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.<|reference_end|>
@article{pereira2024enhancing,
    title={Enhancing Decision Analysis with a Large Language Model: pyDecision a
  Comprehensive Library of MCDA Methods in Python},
    author={Valdecy Pereira, Marcio Pereira Basilio, Carlos Henrique Tarjano
  SantosCarlos Henrique Tarjano Santos},
    journal={arXiv preprint arXiv:2404.06370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06370},
    primaryClass={cs.AI}
}