# Control-policy working — FC5 (condition C)

Acting as the CONTROL POLICY. Verbs run in the order shown; I stop when the
decision is sound and all CHECKABLE post-conditions hold (compose referential
integrity, lift-L1 witness-check, the scoped L2 coverage map with nothing
`missing`, and evaluate-fit on the chosen approach's anti-signatures).

---

## Step 1 — match (retrieve)

`problem`: an organization wants to improve a **hard-to-measure true outcome**
and must drive it through incentives. Choice between (A) reward the actual
outcome read off several triangulating, periodically-recalibrated signals, any
single metric provisional; or (B) attach strong incentives to one clean,
easily-measured proxy and hold people to it.

Semantic `search_prime` (meta-model query: "attaching strong incentives to a
measurable proxy for a hard-to-observe target corrupts the proxy and detaches it
from the true goal") + `search_by_facets` over five structural facets.

Candidate primes with recorded source-prime overlap:

- **observer_effect** (structural, distinctive p≈0.79) — surfaced by the facet
  "agents optimize the letter of the measure not its intent." The act of
  measuring-under-incentive perturbs the measured system: M = f(S, apparatus,
  interaction); strong coupling makes |S − S′|/|S| large, so you end up
  measuring "the system as disturbed by the measurement." This is the
  metric-gaming / Goodhart core. **Load-bearing.**
- **incentive_compatibility** (mixed-framed) — surfaced by the same facet. A
  mechanism is IC when each agent's payoff-max action is the action the designer
  wanted; heavy enforcement is *evidence of IC failure, not a substitute*. IC is
  defined only over a *specified* strategy set — agents exploit off-path actions
  the proxy didn't model. **Load-bearing** (diagnoses why B fails).
- **triangulation** (research-methodology) — surfaced by "triangulating multiple
  noisy signals to estimate a latent variable." Cross-verify a latent target via
  multiple methodologically-independent signals; convergence raises confidence,
  divergence is informative; no single source is decisive. This is the operative
  pattern Approach A relies on. **Load-bearing for A.**
- context: goal_congruence_alignment, mechanism_design_and_incentive_structure,
  moral_hazard, commensurability (single-proxy collapse), periodicity
  (recalibration cadence). **context, not load-bearing.**

## Step 2 — salience-rank (by ablation)

- observer_effect — **load_bearing**: remove it and the failure mode of B
  (measuring the disturbed system) disappears; it is the reason a hard-to-measure
  outcome resists a single proxy. Decisive for *why not B*.
- incentive_compatibility — **load_bearing**: names that strong incentives on a
  gameable proxy make gaming the *rational* response; "hold people to it" is the
  costly-enforcement tell of IC failure.
- triangulation — **load_bearing**: it is the structure of Approach A; remove it
  and A has no content.
- commensurability, moral_hazard, periodicity, goal_congruence — context.

## Step 3 — prune

Operative subset (4): observer_effect, incentive_compatibility, triangulation,
plus commensurability as the single-proxy-collapse annotation. Cuts: coordination,
cooperation, herding, sunk_cost (no behavioral-collapse / irreversibility role
here) — justified out.

## Step 4 — compose (referential integrity CHECKABLE)

`model` nodes:
- n1 = true_outcome (kind: latent_target)
- n2 = proxy_metric (kind: signal)
- n3 = signal_set {s1..sk} (kind: signal_bundle)
- n4 = agents (kind: optimizer)
- n5 = incentive (kind: coupling)
- n6 = recalibration_loop (kind: control)

edges:
- e1 n5→n4 relation:drives [incentive_compatibility]
- e2 n4→n2 relation:optimizes-letter-of [observer_effect — measurement under
  strong coupling perturbs S→S′]
- e3 n2→n1 relation:proxies (lossy) [commensurability — collapse of a
  multi-dim target to one scalar]
- e4 n3→n1 relation:estimates [triangulation]
- e5 n6→n3 relation:reweights/recalibrates [periodicity/triangulation refresh]
- e6 n4→n3 relation:harder-to-game (no single lever) [observer_effect mitigation:
  differently-intrusive methods]

Referential-integrity check: every edge references declared nodes (n1–n6 all
used); every operative prime appears as an annotation (incentive_compatibility e1;
observer_effect e2/e6; commensurability e3; triangulation e4/e5). **PASS.**

## Step 5 — lift L1 (model → meta_model; witness-check CHECKABLE)

Domain-stripped skeleton:
- **Roles**: a *principal* with a latent objective it cannot observe directly; a
  set of *agents* who maximize their own payoff; a *coupling* (incentive) the
  principal sets; one or more *observable signals* correlated-but-not-identical
  with the objective.
- **Relations**: coupling drives agents; agents optimize whatever the coupling
  scores; each signal is a lossy, perturbable estimate of the objective; an
  estimator combines signals; a control loop updates the estimator.
- **Invariants**: (i) scoring a signal under strong coupling changes the signal's
  relation to the objective (measurement perturbs the measured); (ii) the more a
  single signal carries the full payoff, the faster it detaches from the
  objective; (iii) alignment that needs constant external enforcement is
  evidence the rule is not self-aligned.

Witness-check: principal=n1-owner, agents=n4, coupling=n5, signals=n2/n3,
estimator=n3-combiner, control=n6 — every role/relation has a witness in `model`;
no domain terms remain. **PASS.**

## Step 6 — reconcile (model vs meta_model)

Concrete `model` reading of B: one clean proxy + strong incentive → e2 fires hard
→ agents move S′ to score the proxy while n1 stagnates ("hollowed out").
Meta-model reading of B: invariant (ii) — concentrating payoff on one signal
maximizes detachment; invariant (i) — strong coupling guarantees the perturbation.
The two views **agree** that B maximizes the gaming failure and **agree** that A's
multi-signal + provisional + recalibrated structure attacks invariants (i) and
(ii) directly (no single lever to push; reweighting re-references the latent
target). Views do not collapse into one — meta-model adds the *why* (detachment
scales with payoff-concentration) the concrete model alone does not state. Not
decorative.

## Step 7 — lift L2 — SCOPED COVERAGE CHECK on the single operative pattern A relies on

The operative pattern Approach A instantiates is **triangulation** (estimate a
latent target from multiple methodologically-independent signals, treat any one
as provisional, reconcile convergence/divergence, refresh). Coverage map over its
named components against the draft design (Approach A). Statuses:
instantiated / homolog / correctly_declined / missing. **None may be missing.**

`coverage_map`:
pattern = triangulation (as A's operative pattern)

| Component | Status | Witness / reason |
|---|---|---|
| Multiple methodologically-distinct sources | **instantiated** | A's "several triangulating signals"; distinct signal types, not one metric repeated. |
| Methodological **independence** of signals | **instantiated (with note)** | A treats any single metric as provisional and uses *triangulating* (differently-intrusive, per observer_effect mitigation) signals; the design must pick signals with independent gaming-surfaces, else convergence is spurious — flagged as the implementation discipline, but the requirement is met by A's premise. |
| Active **comparison / reconciliation** of signals (convergence vs divergence) | **instantiated** | "judged from several triangulating signals" entails reading them against each other; divergence flags a signal going bad rather than silently scoring it (homolog of "divergence as insight"). |
| Treat divergence as informative, not pure error | **instantiated** | A's "treat any single metric as provisional" = do not let one signal dominate when it diverges; it becomes a recalibration trigger. |
| Temporal/contextual re-examination + refresh | **instantiated** | A's "periodically recalibrate which signals are used" = the control loop n6; directly closes the observer_effect drift over time. |
| Rigor of each individual signal | **homolog** | A does not specify per-signal rigor, but "any single metric provisional" plus recalibration discharges the same function (a degraded signal is down-weighted/replaced) — not a gap, a substrate that performs the role. |
| Integration framework to combine independent signals | **homolog** | "reward the actual outcome judged from several signals" implies a judgment/combiner step; supplied by the judging body rather than a formula. Witness: n3-combiner. |
| Treating triangulation as a *proof of truth* / validity guarantee | **correctly_declined** | Over-lift guard: triangulation reduces but does not eliminate error (signals can be jointly wrong). Building A as if convergence = certified truth would distort it; A correctly keeps signals provisional rather than claiming guaranteed validity. Declined to avoid over-building. |
| "Method pluralism" — accumulate many signals without integration | **correctly_declined** | Over-lift guard: piling on signals without the comparison/recalibration step is the failure mode, not the pattern. A's design is disciplined (provisional + recalibrate), so we decline the undisciplined-accumulation expansion. |

**No component is `missing`.** Stop-condition for L2 satisfied.

## Step 8 — evaluate-fit (gate the CHOSEN approach's anti-signatures)

Anti-signatures of the triangulation-based approach (A), each explicitly evaluated:

- **AS1 — cost/time burden too high for the stakes** (triangulation T5). Tripped?
  *No.* The outcome is explicitly hard-to-measure and the success criterion is
  long-run + anti-gaming — exactly the high-stakes regime that warrants
  multi-signal design. reason: stakes justify the overhead. → keep.
- **AS2 — signals not actually independent (correlated-but-all-wrong)** (T4/T6).
  Tripped? *Partially latent, manageable.* The risk exists, but A's provisional +
  recalibration loop is the standard mitigation; B has no analogue. reason: design
  carries its own corrective; not disqualifying. → keep.
- **AS3 — divergence dismissed as artifact / convergence as false comfort** (T3).
  Tripped? *No*, by construction: A treats every metric as provisional, so it
  cannot lock onto a single converged number. → keep.
- **AS4 — triangulation used as a truth-guarantee** (what_it_is_not). Tripped?
  *No* — declined in coverage (correctly_declined). → keep.

`gate_log`: candidate = Approach A → verdict **keep** (no disqualifying
anti-signature tripped).

Counter-gate on Approach B (single clean proxy + strong incentive): trips the
observer_effect anti-signature (strong coupling guarantees large perturbation of a
hard-to-measure target → measures the disturbed system) AND the
incentive_compatibility tell ("hold people to it" = costly enforcement = evidence
of IC failure on a gameable proxy) AND commensurability collapse (one scalar for a
multi-dimensional latent outcome). verdict **drop** — B structurally maximizes the
"hollowed out by optimizing the letter of the measure" failure the success
criterion forbids.

## STOP

All CHECKABLE post-conditions hold: compose referential integrity PASS, lift-L1
witness-check PASS, L2 coverage map has nothing `missing`, evaluate-fit ran each
anti-signature. Decision is sound. Emit recommendation: **Approach A.**

===DECISION_BEGIN===
DECISIVE_FACTOR: Because the true outcome cannot be observed directly, tying strong incentives to one easy proxy guarantees people will optimize the proxy until it detaches from the real goal, whereas estimating the outcome from several provisional, periodically-recalibrated signals removes the single lever that gaming exploits.
CHOICE: A
JUSTIFICATION: The outcome you actually care about can't be measured directly, so any single clean number is only a stand-in — and putting strong pressure on a stand-in reliably teaches people to move the number without moving the real result. Reading the outcome from several independent signals, treating each as provisional, and periodically changing which signals you trust denies people a single thing to game and lets you catch a measure that has started to drift. Holding people hard to one proxy optimizes exactly the letter-of-the-measure behavior the goal says to avoid. The extra effort of maintaining multiple signals is warranted precisely because the stakes are a long-run, hard-to-measure outcome.
===DECISION_END===
