# Working

**problem.** Pool employees' private estimates of uncertain quantities so the aggregate tracks reality. Threats: estimators own/fear/flatter, so reports are strategically biased; leadership controls what is asked, how collected, what is rewarded/recorded, who sees what, how combined. Success = aggregate beats naive polling AND honest participation persists; failure = gaming OR withdrawal.

**Verbs run (verb — why):**
- **match** — facet retrieval on (pooling signals / biased self-interested reports / rule design for honest disclosure / signal corrupted by being judged / outcome-contingent reward). Surfaced `incentive_compatibility`, `mechanism_design`, `wisdom_of_the_crowds`, `signaling`, `information_cascade`; archetypes `incentive_compatible_rule_design`, `anti_herding_signal_design`, `credible_signaling`, `blinding_and_expectancy_bias_reduction`.
- **salience-rank** — load-bearing: `incentive_compatibility`/`mechanism_design` (the report is gameable because incentives diverge — remove and the design collapses). Context: `wisdom_of_the_crowds` (aggregation substrate), `signaling`/`information_cascade` (a failure mode if reports are public).
- **prune** — drop `optimism_bias`, `fundamental_attribution_error`, `trust`, `conformity` as descriptive not operative; keep the incentive-design spine + crowd substrate.
- **compose** — nodes: estimator{owner,fearful,flatterer}, leadership(designer), report, outcome, aggregate, reward/record channel, visibility channel. Edges: estimator→report (biased by stake), designer→rules (sets payoff/info/verification), report→aggregate (combination), outcome→score (ex-post), score→reward. Every operative prime annotates an edge (incentive_compatibility on report→reward; wisdom_of_crowds on report→aggregate). Referential integrity holds.
- **lift L1** — skeleton: roles {signal-holders with private info + divergent payoff, rule-setter who cannot observe truth directly, ground-truth revealed later}; relations {elicit, combine, verify-against-outcome, reward}; invariants {honest report = best response; independence preserved; legitimate participants stay in}. Every role has a model witness; no domain terms remain.
- **evaluate-fit (gate)** — anti-signatures of incentive_compatible_rule_design: (1) problem is skill not strategy — NOT tripped (bias is strategic). (2) outcome morally contested, undefinable — NOT tripped (forecast targets are objectively scorable). (3) no real choice/private info — NOT tripped (private info is the whole point). (4) merely raising reward without modeling response — guarded against by the scoring/anti-gaming design. (5) coercion/manipulation without transparency — guarded by transparent rules + protected confidentiality. None disqualifies. Anti-herding anti-sig "crowd signal is the intended object" not tripped, but its concern (independence) is folded in.
- **lift L2** — scoped coverage map below.

**Operative pattern: Incentive-Compatible Rule Design** (single; the design's load-bearing spine — make honest disclosure the estimator's best response).

## Scoped coverage map (component → status → witness/reason)
1. **Participant Role Map** — [instantiated] — owners, fearful teams, flatterers, leadership-as-designer, scorer; who can game named.
2. **Desired Outcome Specification** — [instantiated] — calibrated truthful probability/estimate of the real quantity, distinct from "number leadership likes."
3. **Action and Choice Set** — [instantiated] — report high/low/honest, abstain, copy others; assessed over these.
4. **Incentive Payoff Map** — [instantiated] — accuracy reward vs. reputational fear of pessimism vs. flattery payoff mapped; design flips it.
5. **Information Structure Map** — [instantiated] — private signals; who sees whose report; the visibility lever.
6. **Truthfulness Condition** — [instantiated] — proper scoring rule scored against realized outcome makes truthful report expected-score-maximizing.
7. **Verification Rule** — [instantiated] — reconcile each report against the eventually observed outcome; track per-person calibration.
8. **Penalty or Reward Rule** — [instantiated] — reward tied to accuracy-vs-outcome, never to optimism; decoupled from project ownership.
9. **Strategic Response Test** — [instantiated] — walk owner/fearful/flatterer best responses against the rule before launch.
10. **Failure and Gaming Monitor** — [instantiated] — watch variance collapse, accuracy drift, withdrawal, correlated reports over rounds.
11. **Participation Constraint** — [instantiated] — confidentiality + no-blame guarantee + low effort so honest pessimists keep participating (directly the second success criterion).
12. **Fairness Constraint** — [instantiated] — honest pessimist not punished relative to flatterer; protects legitimacy.
13. **Appeal or Exception Channel** — [homolog] — periodic recalibration / dispute-of-scoring window discharges the "contest a bad outcome attribution" function without a heavy adjudication body.
14. **Adaptive Rule Update** — [instantiated] — re-tune weights/scoring each cycle as people learn the mechanism (equilibrium drift).
15. **Transparency Explanation** — [instantiated] — publish how scoring/aggregation works (but not live aggregate) so the rule is legible and trusted.

**Independence** (the wisdom-of-crowds / anti-herding concern): handled inside this design via private-before-public collection and not revealing the running aggregate — a [homolog] of Anti-Herding's "Independent Signal Preservation." I [correctly_declined] importing Anti-Herding as a *second* pattern: its remaining components (Popularity Signal Control, Circuit Breaker, Anti-Panic Protocol, Cascade Monitoring, Minority Report) would over-build — there is no live cascade to break, only a collection-order choice already covered by the Information Structure Map. No component left [missing].

COST: ~6 operations, ~10k tokens.

===RECOMMENDATION_BEGIN===
Treat this as an estimation system whose hardest part is not the math of averaging but the incentives of the people supplying the numbers. Build it so that, for an employee deciding what to report, telling the truth is simply the choice that pays best — and so that honest pessimists never feel exposed for it.

Start by separating what you are measuring from what people wish were true. For each uncertain quantity, define the real target as the eventual observed outcome (the date actually hit, the cost actually incurred), not a number anyone prefers to hear. Ask for estimates as ranges or probabilities, not single points, so confidence itself becomes information.

Collect privately and simultaneously. Each person submits independently, before seeing anyone else's answer and before any running aggregate is shown. Never display the live pooled number back to contributors during a round; a visible average pulls the next person toward it and quietly destroys the independence that makes pooling work. Publish how the system computes and uses results, but not the in-progress result.

Make accuracy the only thing rewarded, and reward it only after reality arrives. Score each person's past estimates against what actually happened, using a rule that gives the best expected score to whoever reports their genuine belief — so shading a number up or down lowers your own long-run score. Crucially, decouple this score from project ownership and from blame: a project owner who honestly forecasts a slip must be scored on forecasting accuracy, not punished for the slip. The person who said "we will probably miss" and was right should come out ahead of the person who said "we'll make it" to please the room. State explicitly, and enforce, that pessimistic estimates are never held against a person or team; reports are confidential to the aggregation process and feed calibration, not performance reviews.

Combine the inputs to exploit diversity, not just count. Average the independent estimates, but weight people by their demonstrated track record of calibration once you have history, and downweight reports that are suspiciously correlated (everyone suddenly converging without new evidence). Keep and surface dissent: when one or two estimates diverge sharply from the pack, record them rather than discarding them, because the minority is sometimes the only one who saw the problem.

Before launch, war-game it. Walk through what each kind of player does under the rule: the owner tempted to look good, the team afraid of reprisal, the person who reports what leadership wants. If any of them still profits by lying, the rule is not finished — adjust the scoring or the confidentiality guarantees until honesty dominates.

After launch, watch the system the way you would watch for gaming, because people learn the rule. Track three things every cycle: does the aggregate beat what a naive unweighted poll would have said (compare both against realized outcomes); is the spread of estimates collapsing too fast (a sign of copying or fear); and is participation holding, especially among the honest pessimists. If accuracy drifts, spread collapses, or people quietly stop answering, treat it as a design failure and retune — change weights, tighten confidentiality, or simplify the effort of reporting.

Give people a light way to contest how an outcome was scored against their estimate (a project genuinely derailed by an external shock should not tank someone's calibration record), but keep this a periodic recalibration window, not a standing tribunal. Re-tune the scoring and weighting each cycle as participants adapt.

The operating rule: reward only proven accuracy, never the comfort of the number; collect every estimate in private before any aggregate is seen; protect the honest pessimist absolutely; weight by track record and diversity, not headcount; and each cycle, check that the pooled answer beat naive polling and that the truth-tellers are still in the room — if either fails, the incentives have drifted and the rule must be redesigned, not the people blamed.
===RECOMMENDATION_END===
