# Coverage Key — L4: Internal Forecasting (pooling employees' private estimates)

## Chosen archetypes

The problem is a three-structure composite. A complete design must braid all three:

1. **Incentive-Compatible Rule Design** (`incentive_compatible_rule_design`) — the elicitation core. Employees have private information and incentives that diverge from accuracy (they own the projects, fear pessimistic numbers, anticipate what leadership wants). The rule must make honest reporting the rational best response, not assume virtue.
2. **Ensemble Decision Aggregation** (`ensemble_decision_aggregation`) — the combining core. Turning many partially-reliable private estimates into one decision-useful aggregate that beats naive averaging, while keeping disagreement and minority signals visible.
3. **Psychological Safety Enablement** (`psychological_safety_enablement`) — the sustainability core. The explicit second success criterion ("people keep participating honestly over time") is a speak-up-safety problem: honest pessimism must not feel career-risky, or the input dries up.

## Load-bearing components

A complete design MUST address each of the following:

1. **Target outcome vs. proxy specification** — Define what the aggregate is actually meant to predict (real schedule/cost/likelihood), separate from the convenient number people report, so the design doesn't just relocate gaming to a new proxy.

2. **Participant + incentive map** — Identify who estimates what, who owns/benefits from the thing being estimated, and what each person expects to gain or lose from an honest vs. flattering number. The misreporting structure differs for project-owners vs. bystanders.

3. **Truthfulness condition / scoring rule** — Make accurate reporting the dominant strategy: a proper scoring or accuracy-tracking rule that rewards calibration over time, so the best move is to report your true belief rather than what looks good. [HARD] — Most designers will reach for "anonymize and average" and stop; they rarely install an actual incentive that makes truth the *best response*, leaving the core gaming pressure intact.

4. **Information structure / independence protocol** — Control what each person sees before they report (collect estimates privately, before social anchoring) so the aggregate isn't herding, cascade, or "what leadership wants" echoed back. [HARD] — Easy to overlook because polling feels neutral; but visible prior answers or known leadership preference collapses independence and quietly destroys the wisdom-of-crowds gain.

5. **Diversity criterion** — Ensure the contributors carry genuinely different information/vantage points, not correlated copies of the same upstream assumption — otherwise pooling adds volume, not accuracy.

6. **Estimate capture format** — Capture each estimate in a comparable form that preserves uncertainty (ranges/confidence/probability), not just a single point, so spread is usable downstream.

7. **Aggregation rule** — An explicit, auditable rule for combining estimates (weighted pool, median, trimming) matched to the output type — not an unexamined mean.

8. **Accuracy-based weighting / calibration feedback loop** — Track who is reliably right over time and weight them up; feed outcomes back to recalibrate. [HARD] — A quick designer treats all voices equally and never closes the loop against realized outcomes, so the system can't learn who to trust and can't reward calibration — which also makes the truthfulness scoring rule (#3) impossible to run.

9. **Disagreement measure + decision rule under spread** — Surface and act on the spread/outliers; high disagreement should trigger more inquiry or hedging rather than being averaged away into false consensus.

10. **Retaliation safeguard / protected pessimism** — Guarantee, structurally, that an honest pessimistic estimate cannot be held against the reporter or their team (separation of estimate from performance evaluation; confidentiality/anonymity where the power gradient is steep). [HARD] — This is the failure mode the problem names explicitly ("reporting feels risky") yet it lives outside the statistics; a numerically elegant design that ignores it still bleeds participation, especially from the lower-power project owners who hold the most relevant private info.

11. **Estimation–decision separation** — Keep the elicitation step distinct from the decision/accountability step that consumes the number, so reporters aren't implicitly bargaining over the outcome when they answer.

12. **Participation constraint / sustained engagement** — Keep the burden low and the value visible (show that surfaced estimates actually inform decisions) so people keep contributing over repeated rounds rather than quietly opting out.

13. **Gaming & equilibrium-drift monitor** — Watch for new gaming patterns and recalibrate the rules as participants learn the mechanism; an initially honest-compatible system degrades as people discover how to play it.

14. **Transparency / legitimacy of the rule** — Make the rules (what's asked, what's recorded, who sees what, how the aggregate is used) legible enough that participants trust the system — without exposing so much detail that it becomes exploitable.

## Flagged [HARD] — most likely to be overlooked or under-built

- **#3 Truthfulness condition / scoring rule** — designers default to anonymize-and-average and never make honesty the *best response*.
- **#4 Information structure / independence protocol** — polling feels neutral, but un-protected independence silently kills the crowd-wisdom gain via herding/anchoring on leadership preference.
- **#8 Accuracy-based weighting / calibration feedback loop** — equal-weighting with no outcome loop means the system never learns who to trust and can't run a real proper-scoring incentive.
- **#10 Retaliation safeguard / protected pessimism** — the explicitly-named failure mode lives outside the math; without structural protection, the richest private info (from project owners) never gets reported honestly.
