## Working

I acted as the control policy and chose verbs freely from the library, in this order: **match → match (facets) → salience-rank → decompose → compose → evaluate-fit → reconcile → STOP**. I followed Approach B (free verb selection, retrieve only what I judged load-bearing).

### match (semantic, domain-stripped meta-model)
Query: *attaching strong incentives to a measurable proxy of a hard-to-observe target causes the proxy to diverge from the target as agents optimize the letter of the measure.*
The blended query landed in the incentive/mechanism neighborhood (conflict_of_interest, mechanism_design_and_incentive_structure, cooperation) — correct family, but too crowded to be decisive on its own. The situation is oblique (it bundles measurement, gaming, single-vs-many signals, and recalibration), so I switched to facet retrieval.

### match (search_by_facets)
Five facets, one per structural aspect:
1. "a measure becomes a target and ceases to be a good measure" → **performativity**, summative_assessment, commensurability
2. "hard-to-observe true outcome inferred from noisy proxy signals" → measurement_uncertainty_and_observational_noise, statistical_inference
3. "agents optimize the letter of a rule against its spirit" → **incentive_compatibility**, mechanism_design
4. "single fragile indicator vs. several triangulating signals" → **triangulation**, monitoring
5. "periodic recalibration to prevent gaming" → refinement, iteration, ultra_stability

The facet decomposition surfaced exactly the latent structures a single blended vector buried.

### salience-rank → prune
Load-bearing trio: **performativity** (the feedback by which a published measure reshapes the behavior it meant to measure — the Goodhart core), **incentive_compatibility** (does the agent's payoff-max action coincide with the designer's true objective?), **triangulation** (convergence across independent signals beats any single stream). Pruned the rest (measurement noise, statistical_inference, anchoring) as describing the terrain, not the decisive lever.

### decompose (framed → structural core)
- performativity → *a description, once adopted as a target and obeyed, becomes constitutive: behavior bends to the measure, so the measure stops tracking the latent target.* Its own §abstract_reasoning names the Goodhart-adjacent case explicitly; T4 adds the sting: a measure exerts MORE distorting force precisely when held as a hard, clean target.
- incentive_compatibility → *self-interested behavior aligns with the designer's objective only when the rule's reward coincides with the true objective.* Critically (its "what_it_is_not"): IC is defined over a SPECIFIED measured action set; agents exploit off-path moves the proxy never modeled. A single clean proxy maximizes that off-path gap.
- triangulation → *multiple independent signals corroborate at the intersection; divergence is itself diagnostic.* No single signal is gameable in isolation when the reward depends on convergence across several.

### compose (typed relational model)
- Target = latent, hard-to-observe outcome.
- Proxy(s) = observable signal(s) standing in for the target.
- Incentive = reward attached to the proxy.
- Agents = optimizers who will push on whatever the reward measures.
- Failure mode = proxy–target gap widening under pressure (the measure is hollowed out while the number improves).

Approach B = {single clean proxy} × {strong direct incentive} × {held fixed}. This is the maximal-performativity, minimal-incentive-compatibility configuration: one gameable surface, hardest reward, no recalibration. The cleaner and more fixed the single proxy, the larger the off-path region agents can exploit while the headline number rises — the success criterion's exact prohibited outcome.

Approach A = {several triangulating signals} × {each treated as provisional} × {periodically recalibrated}. Convergence-based reward raises the cost of gaming (you'd have to fake several independent signals at once), provisionality blunts the constitutive lock-in, and recalibration closes proxy–target gaps as they open — adaptively restoring incentive-compatibility over time.

### evaluate-fit (gate against known failure conditions)
- Against performativity T4/T6: B is the disguised-target trap at full strength; A keeps signals provisional so none ossifies into the world-making measure. A passes, B fails.
- Against incentive_compatibility "what_it_is_not" (bounded over specified actions, leaks off-path): B's single proxy maximizes the leak; A's multi-signal convergence shrinks it. A passes, B fails.
- Against triangulation's caveat: A's advantage is real only if the signals are genuinely independent and actively reconciled (not passively accumulated) — a design obligation, not a defeater. Note B's only honest advantage (cheap, legible, low-burden) is real but orthogonal to the success criterion, which demands the TRUE outcome improve without being hollowed out.

### reconcile
Both approaches "improve a number." Only A is built so the number improving implies the true outcome improving, and stays so as agents adapt. B optimizes the proxy at the cost of the target — precisely the failure the criterion forbids. STOP.

===DECISION_BEGIN===
DECISIVE_FACTOR: When a single easy-to-measure proxy carries a strong reward, people optimize the proxy rather than the real goal, so the measure stops reflecting the outcome it was meant to capture.
CHOICE: A
JUSTIFICATION: A hard-to-measure outcome cannot be safely reduced to one clean number under strong pressure, because people will chase the number and let the real goal drift; the gap only widens the cleaner and more fixed that single target is. Judging the outcome from several independent signals makes gaming far harder, since faking convergence across all of them at once is costly, and divergence between them flags trouble early. Treating each signal as provisional and periodically changing which signals count prevents any one measure from being locked in and hollowed out. This keeps rewarded behavior tied to the actual outcome as people adapt, which a single fixed proxy cannot do.
===DECISION_END===
