reference-class-forecaster

Business

Reference class forecasting skill to counter optimism bias using historical analogies

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Reference Class Forecaster

Overview

The Reference Class Forecaster skill implements reference class forecasting methodology to counter optimism bias and the planning fallacy. It uses historical data from comparable projects or decisions to generate empirically-grounded forecasts, providing an "outside view" to complement internal estimates.

Capabilities

Reference class selection and validation
Distribution fitting from historical data
Adjustment factor calculation
Uncertainty quantification
Bias correction for planning fallacy
Documentation of reference class rationale
Comparison with inside view estimates
Reconciliation guidance

Used By Processes

Cognitive Bias Debiasing Process
Decision Quality Assessment
Strategic Scenario Development

Usage

Reference Class Definition

# Define reference class
reference_class = {
    "name": "Enterprise Software Implementations",
    "description": "Large-scale ERP implementations in manufacturing companies",
    "criteria": {
        "project_type": "ERP implementation",
        "industry": "manufacturing",
        "company_size": {"min": 1000, "max": 10000, "metric": "employees"},
        "project_budget": {"min": 5000000, "max": 20000000},
        "time_period": {"start": "2015", "end": "2023"}
    },
    "sample_size": 47,
    "data_source": "industry_benchmark_database"
}

Historical Data

# Reference class outcomes
historical_outcomes = {
    "cost_overrun": {
        "data": [1.15, 1.32, 1.08, 1.45, 1.22, ...],  # ratio to budget
        "unit": "ratio_to_budget"
    },
    "schedule_overrun": {
        "data": [1.20, 1.50, 1.10, 1.65, 1.35, ...],  # ratio to plan
        "unit": "ratio_to_planned_duration"
    },
    "benefit_realization": {
        "data": [0.75, 0.60, 0.85, 0.45, 0.70, ...],  # ratio to expected
        "unit": "ratio_to_expected_benefits"
    }
}

Inside View Estimate

# Current project estimate (inside view)
inside_view = {
    "project_name": "SAP S/4HANA Implementation",
    "estimated_cost": 12000000,
    "estimated_duration_months": 18,
    "expected_annual_benefits": 4000000,
    "confidence_level": 0.80,  # team's stated confidence
    "key_assumptions": [
        "Experienced implementation partner",
        "Strong executive sponsorship",
        "Proven methodology"
    ]
}

Adjustment Configuration

# Adjustment settings
adjustment_config = {
    "similarity_factors": {
        "project_complexity": {"current": "high", "weight": 0.3},
        "organizational_readiness": {"current": "medium", "weight": 0.25},
        "vendor_experience": {"current": "high", "weight": 0.2},
        "scope_definition": {"current": "medium", "weight": 0.25}
    },
    "adjustment_method": "regression_to_mean",
    "output_percentiles": [10, 25, 50, 75, 90]
}

Reference Class Selection Criteria

Criterion	Good Practice	Poor Practice
Similarity	Same project type, context	Loosely related
Sample Size	n >= 20	n < 10
Data Quality	Verified outcomes	Self-reported
Recency	Last 5-10 years	> 15 years old
Completeness	Full project lifecycle	Partial data

Input Schema

{
  "reference_class": {
    "name": "string",
    "criteria": "object",
    "sample_size": "number"
  },
  "historical_outcomes": {
    "metric_name": {
      "data": ["number"],
      "unit": "string"
    }
  },
  "inside_view": {
    "estimates": "object",
    "confidence_level": "number",
    "assumptions": ["string"]
  },
  "adjustment_config": {
    "similarity_factors": "object",
    "output_percentiles": ["number"]
  }
}

Output Schema

{
  "reference_class_statistics": {
    "metric_name": {
      "mean": "number",
      "median": "number",
      "std": "number",
      "percentiles": "object",
      "best_fit_distribution": "string"
    }
  },
  "adjusted_forecasts": {
    "metric_name": {
      "P10": "number",
      "P50": "number",
      "P90": "number",
      "expected_value": "number"
    }
  },
  "comparison": {
    "inside_view": "number",
    "outside_view_median": "number",
    "bias_factor": "number",
    "confidence_calibration": "string"
  },
  "reconciliation": {
    "recommended_estimate": "number",
    "rationale": "string",
    "residual_uncertainty": "object"
  }
}

Best Practices

Select reference class before seeing inside view estimate
Use objective criteria for class membership
Seek disconfirming evidence on project uniqueness
Document why reference class is appropriate
Present both views to decision-makers
Weight outside view more heavily initially
Update as project-specific information emerges

Bias Correction

Common biases addressed:

Planning fallacy: Systematic underestimation of time/cost
Optimism bias: Overestimation of benefits
Uniqueness bias: "Our project is different"
Anchoring: Insufficient adjustment from initial estimate

Integration Points

Feeds into Decision Quality Assessment
Connects with Calibration Trainer for accuracy improvement
Supports Debiasing Coach agent
Integrates with Pre-mortem Facilitator for risk identification