# FC5_A — Incentive approach decision (CONTROL POLICY working)

Executing the fixed nine-step procedure once each, in order.

## Step 1 — Specify the decision
Pick exactly ONE incentive design to drive a hard-to-measure outcome:
- **A**: reward the true outcome judged from several triangulating signals, treat any single number as provisional, periodically recalibrate the signal set.
- **B**: attach strong, direct incentives to one clean, easily-measured proxy and hold people to it.
Success = the *true* outcome actually improves over time AND is not hollowed out by people optimizing the letter of the measure. Note a contextual lure: leadership *wants* a single clean dashboard number. That is a preference about presentation, not a success criterion, and may bias toward B.

## Step 2 — Identify operative primes (search_prime / search_by_facets / get_prime)
- `search_prime` (meta-model: "an indicator chosen to stand in for a hard-to-observe target becomes the target and is gamed, degrading the correlation it relied on") — surfaced `observer_effect`, `monitoring`, `calibration`, `weak_signals`.
- `search_by_facets` (5 structural facets) — surfaced the load-bearing set: `commensurability`, `signaling`, `goal_congruence_alignment`, `mechanism_design_and_incentive_structure`, `refinement`, `weak_signals_emerging_issues`.
- `get_prime` on `goal_congruence_alignment`, `commensurability`, `signaling`.

## Step 3 — Salience-rank
1. **commensurability** — the act of collapsing a multidimensional true outcome into one metric necessarily *erases* the dimensions outside it; "the pretense that a metric captures everything it claims … is a form of dishonesty." Directly names B's core hazard. (HIGH)
2. **goal_congruence_alignment** — Kerr's "folly of rewarding A while hoping for B"; agents "retreat to measurable local metrics abandoning harder-to-measure system contributions." Directly names the hollowing-out failure. (HIGH)
3. **signaling / observer effect** — once a measure carries strong stakes, behavior reorganizes around producing the *measured signal*, decoupling it from the underlying type/quality (signal inflation, pooling collapse). Explains *why* a single staked proxy decays. (HIGH)
4. **commensurability remedies** (plural metrics, metric contestation, dimensional integrity, periodic auditing) — these ARE Approach A. (HIGH)
5. mechanism_design, refinement, weak_signals — supporting (MID).
6. monitoring, calibration, future_wheel, etc. — generic/peripheral (LOW).

## Step 4 — Prune
Drop the LOW generic measurement primes and the off-target `search_prime` attribution-bias hits. Keep: commensurability, goal_congruence_alignment, signaling. These three jointly specify both the failure mode and the remedy.

## Step 5 — Build a typed relational model
- Entities: True-Outcome (latent, hard to observe); Proxy(es) (observable signals); Agents (incentive-responsive); Principal/Leadership (sets incentives, wants clean dashboard).
- Relations: Incentive --attached-to--> Measure; Agents --optimize--> whatever-is-rewarded; Measure --imperfectly-correlates-with--> True-Outcome; Strength-of-incentive --amplifies--> gaming-pressure-on-the-measure.
- Dynamic: the correlation (measure ≈ true outcome) is NOT fixed — it *decays* as agents adapt their behavior to the rewarded measure (observer/signaling dynamic). The tighter and more singular the staked measure, the faster the decay.

## Step 6 — Domain-stripped meta-model
"A latent target is approximated by observable proxies. Reward pressure is applied to the proxy. Agents optimize the rewarded quantity. Because the proxy↔target correlation is contingent and erodes precisely under optimization pressure, a single hard-staked proxy converges to high *measured* performance with diverging *true* performance (letter beats spirit). Robust designs keep the proxy↔target map plural, provisional, and periodically re-fitted so no single measure becomes sovereign."

## Step 7 — Query archetypes whose source primes match (find_archetypes_for_prime / get_archetype)
- `find_archetypes_for_prime(goal_congruence_alignment)` → `alignment_governance_and_dispute_resolution`, `goal_valence_decomposition_and_separation`; cross-refs from the prime record also name `overoptimization_guardrail`, `objective_function_alignment`.
- `get_archetype(overoptimization_guardrail)` — canonical fit. Its explicit variant **"Metric Fixation Guardrail"** has protected invariant = "the real purpose behind the metric." Compression: "When further optimization … increases … side effects … add guardrails that stop, redirect, simplify, roll back, or escalate before the narrow target damages the broader system." Symptom list includes verbatim: "People respond to the target by gaming, neglecting non-measured work, or hiding harms" and "The metric improves while … stakeholders experience worse outcomes."

## Step 8 — Apply each candidate's logic to BOTH approaches; reconcile
Candidate logics: (i) commensurability — never let a single constructed metric pose as the whole of a multidimensional value; keep plural metrics + auditing. (ii) goal_congruence — reward the system outcome, not a convenient proxy, else agents abandon unmeasured contributions. (iii) overoptimization/metric-fixation guardrail — keep purpose sovereign over proxy; instrument side-effects; recalibrate.

- **Approach B** under all three: attaches *strong* incentives to *one* convenient proxy and *holds people to it* — i.e., it makes the proxy sovereign and the correlation static-by-assumption. This is the precise trigger condition the guardrail exists to prevent; it maximizes gaming pressure and erases off-metric dimensions. Predicted result: measured number rises, true outcome stalls or hollows. Fails the second success criterion structurally.
- **Approach A** under all three: triangulates several signals (plural metrics), treats any single number as provisional (refuses false commensuration), and *periodically recalibrates* (the guardrail's "review and recalibrate" / commensurability auditing). It rewards the actual outcome, so agents cannot satisfy the reward by gaming one letter. This IS the structural remedy the catalog prescribes.
- Reconciliation: all three candidate logics point the same way — A. No tension among them.

## Step 9 — Evaluate fit against failure conditions / success criteria
Failure conditions to walk: (a) true outcome fails to improve; (b) measure gets hollowed by letter-optimization.
- B: (a) likely — improvement concentrates on the proxy, not the latent target; (b) acute — single staked proxy is the textbook hollowing case (signal decay + erased dimensions). B's only advantage is satisfying leadership's dashboard *preference* — irrelevant to the stated criteria, and a known bias source.
- A: (a) satisfied — reward tracks the triangulated true outcome, so genuine improvement is what pays; (b) resisted — no single letter to game, and recalibration re-fits the map as agents adapt, defeating decay. Cost: more judgment, less dashboard-clean. Acceptable given the criteria.
- Leadership's clean-number wish can be partly honored *inside* A (publish a headline composite while treating it as provisional and triangulated) without conceding sovereignty to it.

Conclusion: A dominates on both success criteria; B fails the anti-hollowing criterion by construction.

===DECISION_BEGIN===
DECISIVE_FACTOR: Strong incentives on a single easy proxy guarantee people optimize the letter of that one number while the real, multidimensional outcome it stands for is left behind, so the proxy must be kept plural and provisional rather than made sovereign.
CHOICE: A
JUSTIFICATION: When you put strong rewards on one easy-to-measure number, people drive that number up by whatever means work, including ways that don't actually improve the real goal — so the clean metric rises while the true outcome stalls or gets hollowed out. Judging the outcome from several cross-checking signals, treating any one number as tentative, and periodically refreshing which signals you trust removes the single target people would otherwise game and keeps the reward pointed at genuine improvement. Leadership's wish for one motivating dashboard figure can still be met by publishing a headline summary, as long as it is treated as provisional rather than the thing everyone is held to.
===DECISION_END===
