<!-- 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 single strategic call, not a repeatable rule.

Instructions

When asked to build a scoring model, follow these steps:

1. State the recurring judgment and the outcome it predicts (and confirm the outcome is eventually measurable). If it is a one-off, stop and route to a decision review.
2. Choose a few predictive cues. 3 to 6 cues that plausibly carry real signal; say why each. Resist adding cues that feel thorough but lack validity.
3. Assign weights. Default to equal weights unless real data justifies otherwise - the evidence says simple/equal weights capture most of the benefit; do not fake precision.
4. Define the per-cue rubric. How each cue is scored, so two people would score a case the same way.
5. Set the formula and threshold. How the cue scores combine, and the decision rule (e.g. above X -> advance).
6. Mandate consistency, and flag the caveats. State that the model must be applied the same way every time (overriding it on a hunch reintroduces the noise it removes), that it is only as good as its cues, and any fairness/ethical caveat for judgments about people.
7. Emit the scoring model per references/TEMPLATE.md.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the scoring model (cues, weights, rubric, formula, threshold, caveats), not prose.

Quality Checklist

Before finalizing, verify:

• The judgment is genuinely repeated with a (measurable) outcome, not a one-off.
• Cues are few and each has a stated reason to be predictive.
• Weights default to equal/simple unless data justifies otherwise (no fake precision).
• The per-cue rubric is concrete enough for consistent scoring.
• The model includes the "apply consistently" mandate and the cue-validity caveat.
• Fairness/legal/ethical caveats are flagged for judgments about individuals.
• The output is the scoring-model artifact, not prose.

Evidence

Tier S. Across decades and many domains, mechanical/actuarial combination of cues equals or beats holistic expert judgment in the large majority of studies (Meehl 1954; Grove et al. 2000 meta-analysis), and even equal-weight "improper" models capture most of the benefit (Dawes 1979); the driver is reduced inconsistency/noise (Kahneman, Noise, 2021). It applies to repeated judgments, is only as good as its cues, and raises fairness considerations for judgments about people. Evidence is from human expert-vs-model studies, transferred to AI use, not AI-validated. Full grading: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed scoring model.