okhp3-outcome-modeling-core

Topics in the source

• okhp3-outcome-modeling-core
• Scope
• Core mental model
• Computational payload
• Operating procedure
• 1. Define the decision before the model
• 2. Build the event-to-state representation
• 3. Aggregate at multiple scales

OverKill Hill P³ · overkillhill.com · github.com/OKHP3 Build a defensible model for systems where many noisy events contribute to a measurable outcome. The core separates the shared world model from the decision objective, so the same evidence can support forecasting, ranking, budgeting, or market comparison without confusing prediction with optimization. -- | In scope | Out of scope | |----------|-------------| | Repeated event histories, state vectors, and aggregate outcomes | A universal algorithm that fits every dataset | | Feature reduction and diminishing-returns testing | Claims that a fixed feature count always explains 95% of outcomes | | Calibrated forecasts and decision-ready uncertainty | Presenting a forecast as certainty or advice without constraints | | Domain-adapter routing and handoff contracts | Live trading, betting, or political targeting | -- Treat a complex system as a noisy, time-indexed process: Aggregation can reduce the relative influence of idiosyncratic noise, but it does not erase causal structure, dependencies, or meaningful rare events. The goal is to expose persistent signal while preserving uncertainty and time order. Use the formulas, glossary, sy

This is an excerpt. Refer to the complete source and its current requirements.