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 single strategic call, not a repeatable rule.

Instructions