This model extracts structured Z-number decision matrices from conversational text describing multi-criteria decision problems. Given a natural language narrative about alternatives, criteria, and preferences (often messy, subjective, or contradictory), the model outputs a markdown table with:
Z-numbers extend traditional fuzzy numbers by incorporating reliability/confidence, making them ideal for real-world decision-making under uncertainty.
The extracted matrix can be analyzed using Z-number-based MCDM methods (TOPSIS, PROMETHEE) to produce ranked alternatives. See
text2mcdm for the full pipeline.
