Sensitivity Analyzer Skill

Sensitivity Analyzer

Overview

The Sensitivity Analyzer skill provides comprehensive capabilities for identifying critical inputs and understanding how model outputs respond to parameter changes. It supports both local (one-at-a-time) and global sensitivity analysis methods, enabling robust decision-making under uncertainty.

Capabilities

One-at-a-time (OAT) sensitivity
Global sensitivity analysis (Sobol indices, Morris screening)
Tornado diagram generation
Spider plot creation
Parameter importance ranking
Threshold identification
Breakeven analysis
Scenario comparison

Used By Processes

Monte Carlo Simulation for Decision Support
Multi-Criteria Decision Analysis (MCDA)
Prescriptive Analytics and Optimization
What-If Analysis Framework

Usage

One-at-a-Time (OAT) Analysis

# Define OAT analysis
oat_config = {
    "base_case": {
        "price": 100,
        "volume": 10000,
        "cost": 60,
        "fixed_costs": 200000
    },
    "variations": {
        "price": {"range": [-20, 20], "step": 5, "unit": "%"},
        "volume": {"range": [-30, 30], "step": 10, "unit": "%"},
        "cost": {"range": [-15, 15], "step": 5, "unit": "%"},
        "fixed_costs": {"range": [-10, 10], "step": 5, "unit": "%"}
    },
    "output_variable": "profit"
}

Global Sensitivity (Sobol Indices)

# Define Sobol analysis
sobol_config = {
    "parameters": {
        "price": {"bounds": [80, 120], "distribution": "uniform"},
        "volume": {"bounds": [7000, 13000], "distribution": "uniform"},
        "cost": {"bounds": [50, 70], "distribution": "uniform"}
    },
    "sample_size": 10000,
    "calculate_second_order": True
}

Morris Screening

Efficient screening method for many parameters:

Identifies parameters with negligible effects
Distinguishes linear vs. non-linear effects
Detects interaction effects

Sensitivity Indices

| Index | Meaning | |-------|---------| | S1 (First-order) | Direct effect of parameter | | ST (Total) | Direct + all interaction effects | | S2 (Second-order) | Pairwise interaction effect |

Visualization Types

Tornado Diagram: Horizontal bars showing impact range
Spider Plot: Lines showing output vs. % change in each input
Scatter Plot: Output vs. single input with trend line
Sobol Bar Chart: First-order and total indices comparison
Morris Plot: Mean vs. standard deviation of elementary effects

Input Schema

{
  "analysis_type": "OAT|sobol|morris|breakeven",
  "model": "function or expression",
  "parameters": {
    "param_name": {
      "base_value": "number",
      "range": ["number", "number"],
      "distribution": "string"
    }
  },
  "options": {
    "sample_size": "number",
    "output_variable": "string",
    "calculate_interactions": "boolean",
    "confidence_level": "number"
  }
}

Output Schema

{
  "analysis_type": "string",
  "parameter_rankings": [
    {
      "parameter": "string",
      "importance_score": "number",
      "effect_direction": "positive|negative",
      "first_order_index": "number",
      "total_index": "number"
    }
  ],
  "breakeven_points": {
    "parameter": {
      "breakeven_value": "number",
      "current_distance": "number"
    }
  },
  "interactions": [
    {
      "parameters": ["string", "string"],
      "interaction_index": "number"
    }
  ],
  "tornado_data": {
    "parameter": {
      "low_output": "number",
      "high_output": "number",
      "swing": "number"
    }
  },
  "visualization_paths": ["string"]
}

Best Practices

Start with Morris screening for many parameters (>10)
Use Sobol indices for detailed analysis of top parameters
Include parameter correlations when they exist
Report confidence intervals for sensitivity indices
Consider non-linear effects (total vs. first-order indices)
Communicate results using tornado diagrams for executives
Document parameter ranges and their justification

Interpretation Guidelines

Sobol Index Interpretation

High S1, High ST: Important direct effect
Low S1, High ST: Important through interactions
High S1, Low ST-S1: Few interactions
Low ST: Parameter can be fixed at nominal value

Breakeven Analysis

Identifies the parameter value where:

NPV = 0
Profit = 0
Decision changes
Threshold is crossed

Integration Points

Receives model from Monte Carlo Engine
Feeds into Decision Visualization for charts
Supports MCDA methods for weight sensitivity
Connects with Real Options Analyzer for volatility impact

Agent Skills: Sensitivity Analyzer

Install this agent skill to your local

Skill Files