PROMETHEE Evaluator
SkillDev toolsPROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) skill for outranking-based multi-criteria analysis
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Details
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What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/decision-intelligence/skills/promethee-evaluator/SKILL.md and read by Ahel’s review.
Overview
The PROMETHEE Evaluator skill implements the Preference Ranking Organization Method for Enrichment Evaluation methodology for multi-criteria decision analysis. It uses outranking relations based on pairwise comparisons of alternatives, allowing for flexible preference modeling through various preference functions.
Capabilities
- Preference function selection (Usual, U-shape, V-shape, Level, Linear, Gaussian)
- Unicriterion preference degree calculation
- Multicriteria preference index computation
- PROMETHEE I partial ranking
- PROMETHEE II complete ranking
- GAIA plane visualization
- Walking weights sensitivity analysis
- Net flow calculation
Used By Processes
- Multi-Criteria Decision Analysis (MCDA)
- Vendor Selection Analysis
- Resource Allocation Decisions
Usage
Preference Functions
- Usual (Type I): Binary preference (1 if better, 0 otherwise)
- U-shape (Type II): Indifference threshold q
- V-shape (Type III): Linear with preference threshold p
- Level (Type IV): Combination of q and p thresholds
- Linear (Type V): Linear between q and p thresholds
- Gaussian (Type VI): Normal distribution with sigma parameter
Configuration Example
# Define PROMETHEE configuration
config = {
"alternatives": ["Alt A", "Alt B", "Alt C", "Alt D"],
"criteria": [
{
"name": "Cost",
"weight": 0.3,
"type": "cost",
"preference_function": "linear",
"parameters": {"p": 20000, "q": 5000}
},
{
"name": "Quality",
"weight": 0.4,
"type": "benefit",
"preference_function": "gaussian",
"parameters": {"sigma": 10}
},
{
"name": "Delivery",
"weight": 0.3,
"type": "cost",
"preference_function": "v_shape",
"parameters": {"p": 5}
}
],
"performance_matrix": [
[100000, 85, 12], # Alt A
[120000, 92, 8], # Alt B
[80000, 78, 15], # Alt C
[110000, 88, 10] # Alt D
]
}
Flow Calculations
- Positive Flow (Phi+): How much an alternative outranks others
- Negative Flow (Phi-): How much an alternative is outranked
- Net Flow (Phi): Phi+ - Phi- (used for complete ranking)
PROMETHEE I vs II
- PROMETHEE I: Partial ranking based on Phi+ and Phi- separately (allows incomparabilities)
- PROMETHEE II: Complete ranking based on net flow Phi
GAIA Visualization
The GAIA plane provides:
- 2D projection of criteria and alternatives
- Decision axis showing weight sensitivity
- Clustering of similar alternatives
- Criteria correlation identification
Input Schema
{
"alternatives": ["string"],
"criteria": [
{
"name": "string",
"weight": "number",
"type": "benefit|cost",
"preference_function": "usual|u_shape|v_shape|level|linear|gaussian",
"parameters": "object"
}
],
"performance_matrix": "2D array of numbers",
"options": {
"method": "PROMETHEE_I|PROMETHEE_II",
"gaia_visualization": "boolean",
"sensitivity_analysis": "boolean"
}
}
Output Schema
{
"ranking": [
{
"alternative": "string",
"rank": "number",
"phi_plus": "number",
"phi_minus": "number",
"phi_net": "number"
}
],
"outranking_matrix": "2D array",
"partial_ranking": {
"preferred_pairs": ["object"],
"incomparable_pairs": ["object"]
},
"gaia_data": {
"alternative_coordinates": "object",
"criteria_axes": "object",
"decision_axis": "object"
},
"sensitivity_results": "object"
}
Best Practices
- Select appropriate preference functions based on criterion characteristics
- Use PROMETHEE I when incomparabilities are meaningful
- Set thresholds (p, q) based on domain expertise
- Analyze GAIA plane for insights beyond rankings
- Validate results with stakeholders
- Compare with other MCDA methods for robustness
Integration Points
- Receives weights from AHP Calculator or Stakeholder Preference Elicitor
- Feeds into Decision Visualization for GAIA planes
- Connects with ELECTRE Comparator for method comparison
- Supports Sensitivity Analyzer for weight robustness testing
Signals
- GitHub stars
- 2k
- Forks
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
promethee-evaluator- Source
- github.com/a5c-ai/babysitter
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