pyDecision is a comprehensive Python library designed to streamline Multi-Criteria Decision Analysis (MCDA). It offers a vast collection of MCDA methods, catering to researchers, analysts, and decision-makers seeking robust tools for comparing alternatives based on multiple criteria. The library's key benefit lies in its extensive methodological coverage, coupled with innovative AI-driven interpretation capabilities via ChatGPT integration, simplifying complex decision-making processes.

How It Works

The library implements over 50 MCDA methods, including popular techniques like AHP, ARAS, BWM, TOPSIS, VIKOR, and their fuzzy variants, alongside more specialized algorithms. It facilitates direct comparison of ranking alternatives and criterion weights derived from these diverse methods. A standout feature is its integration with ChatGPT, which enhances result interpretation by providing AI-powered insights. Additionally, pyDecision allows users to import results from custom or less common methods, promoting flexibility and extensibility.

Quick Start & Requirements

Installation is straightforward via pip: pip install pyDecision. The library requires NumPy for data handling, as demonstrated in the AHP example. Numerous Colab notebooks are readily available, offering interactive demonstrations for specific MCDA methods (e.g., AHP, Fuzzy ARAS, BWM, TOPSIS) and showcasing the comparative analysis features, including ChatGPT integration.

Highlighted Details

• Extensive Method Coverage: Implements a wide array of MCDA techniques, including numerous fuzzy logic extensions and advanced algorithms like ELECTRE variants and PROMETHEE.
• AI-Powered Interpretation: Integrates with ChatGPT for enhanced, AI-driven analysis and interpretation of decision-making results.
• Comparative Analysis: Enables direct comparison of alternative rankings and criterion weights across different MCDA methods.
• Custom Method Support: Provides flexibility to import and analyze results from user-defined or non-native methods.
• Rich Demonstrations: Offers a substantial collection of Colab notebooks for practical exploration and learning.

Maintenance & Community

The project acknowledges Sabir Mohammedi Taieb and Université Abdelhamid Ibn Badis Mostaganem for contributions. No specific community channels (like Discord or Slack) or explicit roadmap details are provided in the README.

Licensing & Compatibility

The provided README content does not specify a software license. This lack of explicit licensing information may pose a barrier for users considering commercial or closed-source integration.

Limitations & Caveats

The README does not detail specific limitations, known bugs, or the project's maturity level (e.g., alpha/beta status). Given the extensive number of implemented methods, users should anticipate potential variations in the robustness or completeness of individual algorithm implementations. The inclusion of "Advanced MCDA Methods" suggests ongoing development and refinement.