[Paper Review] Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python

Valdecy Pereira, Márcio Pereira Basílio+1 more|arXiv (Cornell University)|Apr 9, 2024

Advanced Data Processing Techniques9 citations

TL;DR

The paper presents pyDecision, a Python library housing 70 MCDA methods (including AHP, TOPSIS, PROMETHEE, ELECTRE) with visualization tools, and integrates ChatGPT to discuss and compare outcomes.

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

Motivation & Objective

• Address the need for accessible, comprehensive MCDA tooling in Python for complex decision problems.
• Provide a library offering a wide range of MCDA methods including AHP, TOPSIS, PROMETHEE, and ELECTRE families.
• Enhance interpretability with visualization tools to aid result interpretation.
• Explore the integration of a Large Language Model (ChatGPT) to discuss and compare MCDA outcomes.

Proposed method

• Implement a Python library named pyDecision encompassing 70 MCDA methods.
• Include visualization tools to aid interpretation of MCDA results.
• Integrate ChatGPT to enable discussion and comparison of outcomes across methods.
• Provide a narrative on the potential of LLMs to assist decision-makers while noting verification needs.

Experimental results

Research questions

• RQ1 How can a comprehensive MCDA library in Python improve accessibility and consistency in method application?
• RQ2 What value does integrating a Large Language Model (ChatGPT) add to comparing and interpreting MCDA results?
• RQ3 What are the practical considerations and limitations of using LLMs to assist MCDA decision support?

Key findings

• pyDecision offers 70 MCDA methods, including AHP, TOPSIS, PROMETHEE, and ELECTRE families.
• The library provides visualization tools for intuitive results interpretation.
• ChatGPT integration enables discussion and comparison of MCDA outcomes in an interactive manner.
• The paper cautions that LLM outputs can be misleading without rigorous verification and domain expertise.