# FC5 — Incentive design for a hard-to-measure outcome (Control policy, nine-step run)

Acting as the CONTROL POLICY. Executing the fixed nine-step procedure once each, in order, without revisiting.

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## Step 1 — Specify the decision

Pick ONE incentive approach for driving a hard-to-measure outcome.

- **Approach A:** reward the *actual outcome* judged from several triangulating signals; treat any single metric as provisional; periodically recalibrate which signals are used.
- **Approach B:** attach strong, direct incentives to one clean, easily-measured *proxy* metric and hold people to it.

Success = (i) the true outcome actually improves over time, AND (ii) it is NOT hollowed out by people optimizing the letter of the measure. Both clauses must hold; clause (ii) is a hard floor, not a tradeable weight.

## Step 2 — Identify operative primes (search_prime / search_by_facets / get_prime)

Ran a blended semantic `search_prime` (landed in measurement/observer-effect neighborhoods — too diffuse for an oblique multi-structure problem), then decomposed with `search_by_facets`. The facet retrieval surfaced the load-bearing structures:

- **goal_congruence_alignment** (fetched) — distinguishes *nominal* alignment (stated goals match) from *enacted* alignment (incentives/metrics/behaviors actually reinforce). Core warning (Kerr, "rewarding A while hoping for B"): under pressure, agents "retreat to measurable local metrics, abandoning harder-to-measure system contributions." Names the exact gap-where-misaligned-behavior-hides hazard of a single convenient proxy.
- **incentive_compatibility** (fetched) — a rule is incentive-compatible when each agent's payoff-maximizing action *is* the action the designer wants, with no leaky external enforcement. Explicitly: "IC is defined with respect to a specified strategy set… real agents may exploit off-path actions the formal mechanism did not model." A single clean proxy defines a narrow strategy set begging to be gamed off-path.
- Supporting hits: **observer_effect** / **measurement_uncertainty_and_observational_noise** (a measure changes behavior once it becomes a target; any single signal is noisy), **wisdom_of_the_crowds** / **variability** (triangulating multiple independent noisy signals estimates an unobservable better than one), **mechanism_design_and_incentive_structure**, **self_fulfilling_prophecy**.

## Step 3 — Salience-rank

1. **incentive_compatibility** — the decisive structure: which approach keeps "optimize the letter" from diverging from "improve the outcome."
2. **goal_congruence_alignment** — enacted-vs-nominal + the proxy-substitution-under-pressure pathology (clause ii).
3. **observer_effect / measurement-noise** — why any *single* metric is both provisional and self-distorting once incentivized.
4. **wisdom_of_the_crowds / triangulation** — why several signals beat one for an unobservable target (clause i).
5. self_fulfilling_prophecy, mechanism_design — background.

## Step 4 — Prune

Drop pure-measurement framing primes (approximation, commensurability, monte_carlo) and generic decision primes (expected_utility, prioritization) — real but not discriminating between A and B. Keep the top four.

## Step 5 — Typed relational model

- Agents (self-interested, hold private information about the true outcome) → respond to whatever is rewarded.
- Designer's objective = the *true* hard-to-observe outcome (latent).
- Channel = the measure(s) the reward attaches to.
- Distortion law: incentive pressure flows to the measured channel; the gap between channel and true outcome is the exploitable surface. Strength of incentive × narrowness of channel = size of the hollowing-out risk.
- Time: the proxy↔outcome link decays as agents learn the channel (link is non-stationary).

## Step 6 — Domain-stripped meta-model

"A principal must steer agents toward a latent target it cannot directly observe, using a reward attached to observable signal(s). A single narrow observable, once made consequential, becomes a target agents optimize directly — severing it from the latent goal (the measure stops measuring). Mitigations: estimate the latent target from *several* triangulating, independently-gameable signals; treat each as provisional; recalibrate as agents adapt — so no single channel is worth capturing and drift is caught."

## Step 7 — Query archetypes whose source primes match (find_archetypes_for_prime / get_archetype)

From the operative primes, fetched the two most discriminating source/related archetypes:

- **objective_function_alignment** (source primes: optimization, feedback, incentive_compatibility). Core move: separate the *intended outcome* from the *target used to drive behavior*; a metric "is always a representation of value rather than value itself." Prescribes: validated metrics + protected invariants + anti-gaming safeguard + review cadence/owner. Decision rule (verbatim sense): "prefer a less convenient metric that tracks the intended outcome over a convenient metric that predictably distorts behavior"; "when actors can adapt to the metric, test how they would improve the score without improving the outcome."
- **overoptimization_guardrail** (related: goal_congruence; variant explicitly named *Metric Fixation Guardrail*). Symptom it names: "People respond to the target by gaming, neglecting non-measured work, or hiding harms → the target has become an incentive field rather than a faithful guide to purpose." Protected invariant: "Broader purpose remains more important than proxy improvement… prevents the score from becoming the goal."

## Step 8 — Apply each candidate's logic to BOTH approaches and reconcile

**objective_function_alignment applied:**
- Approach B = optimize a single convenient proxy with strong incentives = the textbook anti-pattern. It is exactly the "convenient metric that predictably distorts behavior" the decision rule says to reject; strong direct incentives maximize the gaming pressure; nothing in B validates the proxy against the outcome or guards against gaming.
- Approach A = makes the intended outcome the target, uses multiple signals as fallible representations, holds each provisional (validation), and recalibrates (review cadence / objective-owner). Directly instantiates the prescribed architecture.

**overoptimization_guardrail applied:**
- Approach B has no guardrail, no protected invariant, no side-effect monitoring — single visible metric improves while true purpose is hollowed (the "metric fixation" failure verbatim).
- Approach A's "treat any single metric as provisional" + "periodically recalibrate" *is* the guardrail/review machinery; triangulation supplies the side-effect/cross-check surface that catches a rising metric masking a degrading outcome.

**incentive_compatibility reconciliation:** Under B, the agent's payoff-maximizing action is "move the proxy," which diverges from "improve the latent outcome" — not incentive-compatible, and the divergence widens precisely because the incentive is strong and the channel is narrow (a single off-path target to capture). Under A, rewarding the triangulated outcome estimate and rotating signals means capturing any one signal does not reliably raise the reward — best response moves back toward the true outcome, and incentive-compatibility is far better approximated. Both archetypes and all primes agree; no conflict to reconcile beyond noting A is not free (more judgment, less measurement purity, possible disputes — accepted as the cost of fidelity).

## Step 9 — Evaluate fit against the situation's failure conditions

Failure conditions = (i) true outcome stagnates, (ii) outcome hollowed by letter-optimizing.

- **Approach B vs (ii):** fails hard. One clean, easily-measured proxy + strong direct incentives is the maximal-gaming configuration; agents optimize the letter, the proxy↔outcome link decays, the true outcome is hollowed even as the number looks great. Fails the hard floor.
- **Approach B vs (i):** improves only the proxy, not necessarily the latent outcome; once gamed, even proxy "improvement" is illusory. Fails.
- **Approach A vs (ii):** triangulating several signals removes any single capturable target; treating metrics as provisional + recalibrating catches drift and retires compromised signals before they hollow the outcome. Satisfies the floor.
- **Approach A vs (i):** estimating the latent outcome from multiple independent noisy signals is a better estimator of an unobservable than any one proxy, and rewarding *that* estimate pushes real improvement over time. Satisfies.

A satisfies both clauses; B fails both, decisively the hard floor (ii). Choice: **A**.

===DECISION_BEGIN===
DECISIVE_FACTOR: A single strongly-incentivized easy proxy is the maximal-gaming setup — people optimize the letter and sever it from the true goal — whereas rewarding the real outcome via several recalibrated, triangulating signals leaves no single capturable target.
CHOICE: A
JUSTIFICATION: When you attach strong rewards to one easy-to-measure number, people drive that number rather than the result you actually care about, and the link between the two erodes exactly when the pressure is highest. Judging the real result from several cross-checking signals, keeping any one of them provisional, and refreshing which signals you trust removes the single target that would otherwise be gamed and catches distortion early. That setup is the only one of the two that can both move the true result and resist being hollowed out over time. The extra judgment and lower measurement tidiness it requires are an acceptable price for that protection.
===DECISION_END===
