Similar tools, different responsibilities
Rules encode known conditions. Heuristics provide useful shortcuts when perfection is unnecessary. Statistics estimates relationships and uncertainty under modeling assumptions. Machine learning fits patterns that predict outcomes for new cases. They overlap, but differ in what is known at the start and how failure is diagnosed.
| Tool | Best fit | Risk |
|---|---|---|
| Rule | Stable known logic, audit required | Brittle on rare exceptions |
| Heuristic | Fast approximation, low stakes | Silent bias accumulates |
| Statistics | Inference and uncertainty | Wrong causal story |
| ML | Prediction from labeled examples | Leakage and drift |
The simplest tool is the baseline
Pick the smallest mechanism that absorbs the meaningful variation. If a stable, inspectable rule works, replacing it with a model only adds cost. If stakeholders cannot define the target consistently, a model will hide that disagreement behind a number rather than resolve it. Require evidence that the baseline misses something valuable before adding ML.
They coexist in real systems
A fraud system might block confirmed schemes with rules, pause high-velocity accounts with a heuristic, monitor normal ranges statistically, and assign long-tail cases to a learned model. The important design question is which layer owns which cases and what takes over when that layer fails.
Tool choice is component design
Do not force policy, uncertainty estimation, pattern recognition, and fallback behavior into one opaque component. Give rules responsibility for explicit policy, statistics responsibility for uncertainty, and models responsibility for repeatable predictive variation. Clear boundaries make replacement, validation, and incident diagnosis easier than a fashionable all-purpose model.