Reference class forecasting skill to counter optimism bias using historical analogies
Generates forecasts using historical analogies to counter optimism bias in project planning.
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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.
# 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"
}
# 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"
}
}
# 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 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]
}
| 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 |
{
"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"]
}
}
{
"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"
}
}
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