okhp3-outcome-modeling-corelisted

# okhp3-outcome-modeling-core **OverKill Hill P³** · [overkillhill.com](https://overkillhill.com) · [github.com/OKHP3](https://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. --- ## Scope | 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 | --- ## Core mental model Treat a complex system as a noisy, time-indexed process: ```text events -> entity state -> outcome estimate -> objective and constraints -> decision ``` 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. ## Computational payload Use the formulas, glossary, synthetic fixture, and deterministic helper sup