think-linear-model-aggregationlisted

<!-- thinking-framework-skills | https://github.com/product-on-purpose/thinking-framework-skills | Apache-2.0 --> # Linear-Model Aggregation For a judgment you make over and over - screening candidates, scoring leads, triaging tickets - holistic expert intuition is unreliable mainly because it is inconsistent: the same expert scores the same case differently on different days. A simple mechanical rule removes that: pick a few predictive cues, weight them (even equal weights work), score each case, combine by a fixed formula, and apply it identically every time. The robust, counterintuitive result is that such rules match or beat holistic judgment, because consistency beats brilliance applied erratically. The output is a **scoring model**. Two honest limits: it is for *repeated* judgments (not one-off strategic choices), and it is only as good as its cues. ## When to Use - The same kind of evaluative/predictive judgment recurs (screening, lead/deal scoring, triage, prioritizing a queue). - Gut calls on these are inconsistent or overconfident. - A few cues with real predictive signal exist. ## When NOT to Use - A genuinely one-off decision among a few options (use `decision-option-review`). - No real predictive cues or data exist - do not invent cues and weights (false precision). - High-stakes judgments about individuals (hiring, lending, justice) where mechanical scoring raises fairness/legal/ethical issues - flag these, do not silently automate. - When the point is a si