decision-tree-builder

Business

Automated decision tree construction skill for structuring complex decisions with probabilities, payoffs, and expected value calculations

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Decision Tree Builder

Overview

The Decision Tree Builder skill provides automated construction and analysis of decision trees for structuring complex business decisions. It enables systematic evaluation of alternatives through probability assignments, payoff calculations, and expected value analysis, supporting both prescriptive decision-making and sensitivity analysis.

Capabilities

Decision node and chance node creation
Probability assignment and validation
Expected value calculation
Decision path optimization
Sensitivity analysis on probabilities
Rollback analysis automation
Decision tree visualization generation
Export to standard formats (JSON, XML)

Used By Processes

Structured Decision Making Process
Multi-Criteria Decision Analysis (MCDA)
Decision Quality Assessment

Usage

Basic Decision Tree Construction

# Define decision structure
decision_tree = {
    "type": "decision",
    "name": "Market Entry Strategy",
    "alternatives": [
        {
            "name": "Enter Now",
            "type": "chance",
            "outcomes": [
                {"name": "High Demand", "probability": 0.6, "payoff": 1000000},
                {"name": "Low Demand", "probability": 0.4, "payoff": -200000}
            ]
        },
        {
            "name": "Wait and See",
            "type": "chance",
            "outcomes": [
                {"name": "Market Grows", "probability": 0.5, "payoff": 600000},
                {"name": "Market Stagnates", "probability": 0.5, "payoff": 100000}
            ]
        }
    ]
}

Expected Value Calculation

The skill calculates expected monetary value (EMV) for each decision path:

EMV = Sum(probability * payoff) for each chance node
Optimal decision selected based on maximum EMV

Sensitivity Analysis

Identify critical probability thresholds where the optimal decision changes:

One-way sensitivity on individual probabilities
Two-way sensitivity for correlated probabilities
Tornado diagrams for parameter importance

Visualization Output

Generate decision tree diagrams with:

Clear node labeling (decision squares, chance circles, terminal triangles)
Probability annotations on branches
Payoff values at terminal nodes
Highlighted optimal path

Input Schema

{
  "decision_name": "string",
  "alternatives": [
    {
      "name": "string",
      "outcomes": [
        {
          "name": "string",
          "probability": "number (0-1)",
          "payoff": "number",
          "nested_decision": "optional object"
        }
      ]
    }
  ],
  "analysis_options": {
    "sensitivity_analysis": "boolean",
    "visualization": "boolean",
    "export_format": "json|xml|png|svg"
  }
}

Output Schema

{
  "optimal_alternative": "string",
  "expected_value": "number",
  "decision_path": ["string"],
  "sensitivity_results": {
    "critical_probabilities": ["object"],
    "tornado_data": ["object"]
  },
  "visualization_path": "string"
}

Best Practices

Ensure probabilities at each chance node sum to 1.0
Include all reasonably likely outcomes, not just best/worst cases
Use consistent monetary units for all payoffs
Document assumptions behind probability estimates
Perform sensitivity analysis on uncertain probabilities
Consider non-monetary values through utility functions when appropriate

Integration Points

Connects with Monte Carlo Engine for probability distributions
Feeds into Decision Visualization for reporting
Integrates with Bayesian Network Analyzer for probability updates
Supports Decision Journal for documentation