I am acting as the CONTROL POLICY of a reasoning engine. Below I show the verbs I ran (in order), the state they wrote, and my reasoning. I chose Approach B for the engine condition: free verb choice, stopping when the decision is sound. (No coverage-gate enforced at STOP.)

# Working

## STATE.problem
- statement: An organization must drive a hard-to-measure outcome through incentives, choosing exactly one of two incentive designs.
- constraints: leadership wants/prefers a single clean dashboard number; the true outcome is hard to measure directly.
- success_criteria: the TRUE outcome improves over time AND is not hollowed out by people optimizing the letter of the measure.
- failure_criteria: people game the measure; the proxy improves while the true outcome stagnates or degrades.

## Verb: match (retrieve)
Anchored the problem to the catalog with a semantic meta-model query and a facet decomposition (oblique, multi-structure problem: a proxy/target gap + optimization pressure + gaming + triangulation + recalibration).

`search_prime` ("strong incentives on a single proxy of a hard-to-observe target degrade the proxy's correlation with the truth") returned, near the top: `mechanism_design_and_incentive_structure`, `goal_congruence_alignment`, and the `incentive_compatibility` neighborhood.

`search_by_facets` (5 facets) was decisive:
- "agents optimize the letter of a rule rather than its intent" -> **incentive_compatibility** (0.718, top hit), mechanism_design.
- "optimization pressure degrades the measure as a signal" -> **observer_effect** / measurement_and_disturbance / signaling.
- "a proxy stands in for a hard-to-observe true target" -> regulatory_capture, **signaling**, anchoring.
- "triangulating an unobservable quantity from several noisy independent signals" -> **wisdom_of_the_crowds**, monte_carlo, measurement_uncertainty_and_observational_noise.
- "periodically recalibrating which indicators are trusted" -> **monitoring**, temporal_dynamics, system_archetypes.
- Fused top: observer_effect, signaling, incentive_compatibility, diminishing_incremental_gains.

Also surfaced: `performativity` (a framed prime — measuring/acting on an indicator changes the behavior it was meant to passively reflect).

Post: candidates recorded with source-prime overlap. Retrieval hit; no crowding problem (the two governing primes — incentive_compatibility, observer_effect — are clearly identified and observer_effect is distinctive, 0.79 pct).

## Verb: salience-rank
Ordered the operative primes by load-bearing relevance (would removing it change the recommendation?).

- **incentive_compatibility — LOAD-BEARING.** The success criterion explicitly forbids the outcome being "hollowed out by people optimizing the letter of the measure." That IS the incentive-compatibility condition: a rule is sound only if each agent's payoff-maximizing action coincides with the designer's true objective. Strong incentives on a clean proxy make optimizing the *letter* of the proxy the rational best response — and the entry is explicit that IC is bounded by the modeled strategy set: real agents exploit off-path actions (gaming) the formal proxy never modeled. Remove this prime and the decision loses its spine.
- **observer_effect / performativity — LOAD-BEARING.** The measured/incentivized quantity is not inert. Coupling a strong incentive to a proxy of a hard-to-observe target perturbs the system: the measured value decouples from the true latent state (S -> S'), so the proxy stops tracking the truth precisely *because* it is now the target. This is exactly why a "clean single number" degrades fastest under pressure.
- **wisdom_of_the_crowds / triangulation + monitoring/recalibration — LOAD-BEARING (for A).** Multiple noisy independent signals estimate an unobservable quantity better than any one signal, and the estimate's robustness depends on the signals staying independent. Periodic recalibration is the mitigation the observer_effect entry names directly: triangulate across methods with different observer profiles; revise instruments as they get gamed. This is the structural content of Approach A.
- context (not load-bearing here): regulatory_capture, signaling, anchoring, diminishing_incremental_gains — they color the dynamics but do not flip the choice.

## Verb: prune
Operative subset retained: {incentive_compatibility, observer_effect/performativity, wisdom_of_the_crowds (triangulation), monitoring (recalibration)}. Cut the rest as context — they describe symptoms, not the decision axis.

## Verb: compose (typed relational model)
- nodes: TrueOutcome (latent target), Proxy (one clean metric), Signals[] (several triangulating indicators), Agents (the incentivized people), Incentive (the reward coupling), Leadership (prefers one number).
- edges:
  - Incentive --drives--> Agents [annotation: incentive_compatibility — agents maximize whatever Incentive is attached to]
  - Agents --optimize--> (Proxy under B | TrueOutcome-as-triangulated under A)
  - Proxy --is-a-noisy-stand-in-for--> TrueOutcome [annotation: signaling / proxy gap]
  - Incentive+Measurement --perturbs--> Proxy's correlation-with-TrueOutcome [annotation: observer_effect / performativity]
  - Signals[] --triangulate--> TrueOutcome [annotation: wisdom_of_the_crowds; valid while signals stay independent]
  - Operator --recalibrates--> Signals[] over time [annotation: monitoring / observer-effect mitigation]
Referential integrity holds: every edge references declared nodes; every operative prime appears as an annotation.

## Verb: reconcile (concrete model vs meta-model)
- Meta-model reading: "Apply maximal pressure to a single proxy of an unobservable target" -> the proxy is driven to its limit while its linkage to the target is silently severed (observer_effect) and gaming becomes rational (incentive-incompatibility). Predicts proxy improves, truth stalls/degrades — i.e., the explicit failure criterion.
- Concrete reading: Leadership's stated preference for one motivating number is real and has value (clarity, focus, low overhead). A single clean metric is genuinely easier to track and rally around.
- Divergence: the concrete pull (one clean number is motivating and simple) directly conflicts with the meta-model verdict (a single hard-pressed proxy of a hard-to-measure target is the canonical hollowing-out trap). Resolving toward the reading consistent with BOTH views: the legitimate value in leadership's preference is *focus and legibility*, NOT *single-point optimization pressure*. Approach A preserves the legible-dashboard value (it can still surface a headline read) while keeping each metric *provisional* and recalibrated — capturing the benefit without taking on the decoupling/gaming liability. The views genuinely disagreed, so the meta-model is not decorative; it overrides the surface temptation.

## Verb: evaluate-fit (gate the candidates)
- Approach B (strong incentive on one clean proxy): trips the incentive-incompatibility anti-signature (gameable proxy + strong direct pressure + hard-to-verify true outcome => optimizing the letter is the dominant strategy) AND the observer-effect/performativity anti-signature (the act of hard-incentivizing the proxy corrupts the very correlation that made it a proxy). VERDICT: drop. B satisfies the "improve the number" half but structurally violates the "not hollowed out" half — which is a hard success criterion, not a soft one.
- Approach A (reward the true outcome via several triangulating signals, treat any single metric as provisional, periodically recalibrate): triangulation's main anti-signature is signal collapse — if all signals share a common gaming channel, multiplicity is illusory. But A explicitly treats metrics as provisional and recalibrates which signals are used, which is precisely the mitigation that defuses this anti-signature (revise instruments as they are gamed; keep observer profiles diverse). Residual cost: more complex, less instantly legible, requires ongoing judgment. None of these is disqualifying against the stated success criteria. VERDICT: keep.

## STOP decision
The success criterion has two clauses and is conjunctive: improve the true outcome AND resist hollowing-out. Approach B can only reliably deliver the first clause and structurally fails the second. Approach A is the only candidate that survives the gate on both clauses. Decision is sound. STOP.

===DECISION_BEGIN===
DECISIVE_FACTOR: Whichever quantity carries strong direct incentives becomes the thing people optimize, so attaching that pressure to a single easy proxy of a hard-to-see goal guarantees the proxy is gamed and pulls away from the real outcome.
CHOICE: A
JUSTIFICATION: When you tie strong rewards to one easy-to-measure number, people will drive that number up by the cheapest route available, which usually means working the letter of the measure rather than the result it was meant to capture — and the harder the real goal is to observe, the wider that gap grows. Rewarding the real outcome as read from several cross-checking signals, while treating any one number as provisional and periodically revising which signals you trust, keeps people aimed at the actual goal and lets you catch and close gaming as it emerges. It still gives leadership a clear headline figure to watch, just without betting everything on a single corruptible target. This is the only option that can both raise the true outcome and keep it from being hollowed out over time.
===DECISION_END===
