gcisens

Variance-based (Sobol') sensitivity analysis and Sensitivity Discrepancy Report for rule-based MCDA models — ESP-COMET and ESP-SPOTIS.

Criteria weights are the standard explanation of MCDA models, but they capture only linear effects. gcisens checks whether the weights of a model faithfully represent its global behaviour, and flags criteria whose influence is hidden in nonlinearities and interactions.

Based on:

• Śniegowski, Świder, Shekhovtsov, Sałabun — Detecting Hidden Criterion Influence When Weights Mislead in Rule-Based Decision Support Systems (KES 2026)
• Sałabun, Shekhovtsov, Wątróbski — Variance-Based Analysis of Global Criteria Importance in the ESP-COMET Method (ISD 2025)

Install

pip install gcisens

Quick start

import numpy as np
from gcisens import esp_comet, SobolStudy

bounds = np.array([[18, 60], [1, 29], [1009, 19999]], float)
esps = np.array([[25, 25, 2000]])          # expected solution point(s)

model = esp_comet(esps=esps, bounds=bounds,
                  criteria_names=["Age", "Distance", "Income"])

result = SobolStudy(model, n_samples=2048, seed=42).run()

result.table()        # weights, S1, ST, ranks, category per criterion
result.diagnosis()    # Sensitivity Discrepancy Report
result.summary()      # R², ΣS1, ΣST, Spearman correlations
result.plot_indices() # w vs S1 vs ST bar chart
result.to_latex()     # publication-ready table

Already know pymcdm? Build the model the pymcdm way — the classes are re-exported 1:1 and both styles mix freely:

from gcisens import COMET, ESPExpert, SobolStudy

expert = ESPExpert(esps=esps, bounds=bounds)
model = COMET(expert.make_cvalues_psi(), expert)
result = SobolStudy(model, bounds=bounds).run()

What you get

Supported models

Diagnosis thresholds are configurable via DiagnosisThresholds (defaults follow the KES 2026 article).

Examples

examples/article_esp_comet.py reproduces the three experiments of the KES 2026 article on the bundled IBM HR Attrition dataset.

License

MIT