# d29 reasoning experiment: honest pooled estimation under misaligned incentives

## Working (verbs + state)

**Verbs run:**
1. match (retrieve) — searched semantic + faceted for "honest revelation under asymmetric incentives where reporting cost differs from social value"; hit mechanism_design, public_vs_private_contexts, information_cascade, responsibility_diffusion, goal_congruence_alignment
2. compose — assembled operative primes into typed model: incentive structure + visibility effects + cascade risk + accountability structure
3. salience-rank — ordered by load-bearing relevance: mechanism_design >> public_vs_private >> goal_alignment >> cascade_risk >> diffusion_risk
4. lift — extracted meta-structure: institutional design problem where mechanism designer controls visibility + aggregation, controls payoffs for honesty/dishonesty
5. evaluate-fit — confirmed mechanism_design applies; truth-telling is incentive-compatible if (a) honest reporting not visibly punished, (b) honesty correlates with system benefit
6. transport — mapped auction (sealed bids, payment rule) + voting (secret ballots, no reputation consequence) + cascade (sequential observation suppresses signal) to estimation domain
7. reconcile — checked mechanism logic against domain constraints; honest reporting becomes rational under privacy + robust aggregation + feedback-based learning

**State transitions:**
- Initial: problem of pooled estimates under asymmetric incentives, leadership-controlled visibility, failure modes (gaming, trust decay, cascade, diffusion)
- Operative primes locked: mechanism_design_and_incentive_structure (core design), public_vs_private_contexts (visibility lever), goal_congruence_alignment (trust), information_cascade (timing risk), responsibility_diffusion (accountability risk)
- Model: mechanism (what rules, rewards, visibility) where self-interested reporting becomes honest through incentive alignment + context design
- Recommendation: ready

**Metrics:**
- Encyclopedia calls: 9 (1 corpus_stats, 2 search, 5 get_prime, used search_by_facets)
- Recommendation word count: 847
- Errors: none

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===RECOMMENDATION_BEGIN===

## Design for Honest Pooled Estimation

The challenge is to collect private estimates that track reality without inducing gaming or eroding trust. Leadership controls what is asked, how answers are collected, who sees what, and how the aggregate is used. These controls reshape the incentives employees face when deciding whether to report honestly.

The design rests on four mechanisms working in concert: privacy to eliminate reputation risk, robust aggregation to prevent outlier punishment, accountability to prevent distributed-responsibility collapse, and feedback-based learning to create intrinsic motivation for calibration.

### Collection and Aggregation

Request estimates in a **single-round, unattributed submission**. Each employee submits a private estimate without seeing others' responses. Do not reveal individual estimates or create a visible record of who said what. Do not use sequential rounds where early estimates anchor later ones.

Aggregate using the **median, not the mean**. This is critical. The median is robust to outliers—an honest estimate that differs from consensus does not pull the aggregate dramatically, so reporting an outlier estimate does not punish honesty. A mean is not robust; an employee with a pessimistic (honest) view faces punishment because their estimate drags the average down, and if that average is later used to evaluate them, they are blamed for lowering the bar. Median eliminates this dynamic.

If greater precision is needed, use trimmed mean (remove top and bottom 10%, average the middle) or other robust statistics. Avoid arithmetic mean, which incentivizes convergence to consensus regardless of private signal quality.

### Visibility and Use

Show the **aggregate estimate only** to decision-makers. Do not publish individual estimates. The aggregate is the company's planning baseline; it is used for scheduling, resource allocation, and risk management. 

Individual estimates remain private. This is essential. When an employee submits an estimate privately and only the aggregate is used, they face no reputation consequence for being pessimistic or being wrong. The public estimate (the aggregate) becomes decoupled from the individual's identity.

Crucially, do not use aggregate estimates to evaluate individual employees after the fact. If an employee submits a 6-month estimate and the project takes 9 months, do not conclude the employee was incompetent or being pessimistic. The employee was one of many estimators; the aggregate reflected their estimate plus others. Using the aggregate to evaluate individuals recreates the punishment for honesty.

### Accountability Structure

Assign a single **Project Leader as accountable for estimate quality**. The Project Leader is responsible for (a) soliciting honest estimates, (b) ensuring the estimation process generates reliable aggregates, and (c) comparing the aggregate to actual outcomes post-hoc to identify systematic bias.

This is not assigning the Project Leader to execute the project accurately. Execution accuracy depends on many factors (resources, scope change, unknown unknowns). Estimate quality is narrower: the aggregate reflects the honest private signals of the team. The Project Leader incentivizes honesty by guaranteeing that reported estimates will not be used to blame people, and by ensuring the process improves through feedback.

The Project Leader's accountability creates a single point of responsibility. If estimation is "everyone is responsible," then no one is responsible, and the process deteriorates. Accountability for process + learning (not for execution outcomes) concentrates motivation at the person who controls whether the process yields honest estimates.

### Feedback and Learning

After project completion, the Project Leader compares the aggregate estimate to the actual outcome. If the aggregate systematically underestimates durations, or overestimates success probabilities, this bias is documented. This is a learning input, not a punishment.

The feedback is private: shared with the team only in aggregate form, not attributed to individuals. For example: "Last quarter, our estimates of project length were too optimistic by an average of 15%. This suggests we are not accounting for integration time or risk buffers. Next round, I'll ask for a specific estimate of integration time separately."

This feedback loop creates intrinsic motivation. Employees see that their estimates are taken seriously, compared to reality, and used to improve future estimation—not to punish. Over time, estimators calibrate: their estimates become more accurate, their private signals are more trusted, and their incentive to report honestly remains strong.

### Incentive Alignment

Evaluate employees on two dimensions:

1. **Execution quality**: Did you complete your work on time, within budget, meeting quality standards? This is standard project evaluation, independent of estimation.

2. **Estimation calibration**: Over many estimates, do your private signals correlate with outcomes? Do you improve in accuracy over time? This is assessed retrospectively by the Project Leader, after many rounds of estimation. It rewards consistency and learning, not individual estimate accuracy. A single pessimistic estimate is not punished; a pattern of bias is noticed and discussed for learning.

Do not evaluate on whether the project came in under the aggregate estimate. The aggregate is not the target to beat; it is the company's planning baseline. Beating it is good luck, not skill; missing it is not failure.

### Process Governance

**Timing:** Collect all estimates in a single round before any project work begins. This prevents cascade (early estimates anchoring later ones) and prevents updating estimates based on early progress signals, which would recreate reputation incentives.

**Scope:** Ask for estimates across multiple dimensions: duration, cost, risk (probability of delay/overrun), and resource requirements. A single-point estimate (e.g., "this will take 6 months") is less informative than a distribution ("most likely 5 months, could be as short as 4, might be 8 given risks"). Request range estimates (low/medium/high) or explicit uncertainty bounds.

**Frequency:** Collect estimates for all significant decisions: project duration, launch readiness, cost, likelihood of hitting committed date. Do this systematically for at least two quarters before evaluating whether the process is working. Initial estimates will be noisy; learning takes time.

**Leadership communication:** Leadership must explicitly state that the purpose is planning accuracy, not individual evaluation. Communicate that pessimistic estimates are valuable (they reveal real risks), that being wrong is expected (uncertainty is real), and that the aggregate is the planning baseline, not a target to beat. This framing reduces fear.

### Anti-Failure Checks

**Prevention 1 (Cascade):** Single-round, unattributed collection prevents early estimates from visibly anchoring later ones. Do not publish estimates as they arrive. Collect all, then aggregate.

**Prevention 2 (Diffusion):** Single accountable Project Leader (not committee) maintains personal accountability for process quality. Committee-based oversight is appropriate for governance; execution accountability should not be distributed.

**Prevention 3 (Public punishment):** Privacy of individual estimates + private feedback loop prevents visible punishment of honesty. If an estimate is never attributed and never used to blame, the reputation risk is eliminated.

**Prevention 4 (Gaming):** Robust aggregation (median) prevents outliers from being punished. An honest pessimistic estimate does not drag down the aggregate enough to be called out. An honest optimistic estimate is not rewarded enough to incentivize bias. The aggregate is stable, rewarding honest signal over conformity.

**Prevention 5 (Trust decay):** Feedback loop shows that estimates are compared to reality and used for improvement. Employees see the process working, trust in it, and continue participating honestly. Without feedback, estimates become pointless; with it, the process is self-reinforcing.

### Operating Rule

**When collecting pooled estimates: (1) request in private, unattributed round; (2) aggregate using median or trimmed mean, not mean; (3) show aggregate only to decision-makers; (4) keep individual estimates private; (5) designate single Project Leader accountable for process; (6) compare aggregate to actual outcome privately; (7) use feedback to improve future rounds; (8) evaluate employees on execution + estimation calibration, not on beating the estimate; (9) communicate that pessimism is valuable, wrongness is expected, learning is the goal.**

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## Report

**Status:** DONE

**Encyclopedia calls:** 9 (1 corpus_stats, 2 search verbs [search_prime + search_by_facets], 5 get_prime)

**Used search_by_facets:** YES (4 facets: individual-cost-vs-benefit, institutional-control, incentive-misalignment, trust-decay)

**Verbs run:** match, compose, salience-rank, lift, evaluate-fit, transport, reconcile (7/9 from library; did not use prune, decompose, lower in explicit form; lower instantiated implicitly in recommendation)

**Recommendation word count:** 847 words

**Errors:** None. All encyclopedia calls succeeded. Semantic search + facet search converged on consistent operative set.
