# Working — d35 (control policy)

## STATE.problem
- statement: An abuse-detection system flags marketplace accounts. Hit rate is decaying because adversaries learn the fixed criteria and adapt past them; meanwhile innocent sellers are wrongly restricted with no way to learn why or contest it.
- success: stays effective as targets adapt AND innocents are not driven off by wrong restrictions.
- failure: degrades into an evadable rubber stamp OR a blunt instrument punishing the innocent.

## Verbs run (verb — why)
- **match** — anchored the problem to the catalog with a blended semantic query plus a 5-facet decomposition (classifier criteria become known/evadable; enforcement with no contest; static rule gamed over time; measure-becomes-target; rare adversarial signal among costly false positives). Facet retrieval was far sharper than the blended vector.
- **salience-rank** — the facets collapse onto two loads: (A) a detector that must decide on a hidden, adaptively-concealed attribute (abuse intent) before acting, and (B) enforcement decisions affecting livelihoods with no recourse. Retrieval put `hidden_type_screening` top (fused archetype) and `procedural_fairness_design` top of the contest facet.
- **prune** — operative primes: screening, adverse_selection, incentive_compatibility (anti-gaming), threshold/type_i_type_ii_errors (error policy), procedural_fairness (recourse), monitoring/recalibration (drift). Cut myopic-policy framing (sequential_policy_optimization) and measurement-noise primes as context, not load-bearing.
- **compose** — model: nodes {account, signal-set, score, routing-gate, review-body, decision, appeal-path, recalibration-loop, adversary}; edges {adversary→observes→signal-set; signal-set→feeds→score; score→routes→gate; gate→issues→decision; account→enters→appeal-path; appeal-path→informs→recalibration-loop; outcomes→retrain→signal-set}. Every operative prime annotates an edge (referential integrity holds).
- **lift L1** — skeleton: an uninformed judge must decide membership (trusted vs. restricted) on a hidden, strategically-concealed attribute, before commitment, where the judged population observes and adapts to the test, and wrong exclusions are costly to the excluded. No domain terms; every role has a witness in the model.
- **match→get_archetype** — pulled `hidden_type_screening`, `procedural_fairness_design`, `sequential_policy_optimization` full records; primes `screening`, `incentive_compatibility`.
- **evaluate-fit (gate)** — walked `hidden_type_screening` anti-signatures (below).
- **lift L2** — scoped coverage map on the SINGLE operative pattern (below).

## evaluate-fit — anti-signatures of Hidden-Type Screening
- "attributes already known/verified" — NOT tripped; abuse intent is precisely hidden and concealed.
- "trivial, easily reversed decision" — NOT tripped; restriction harms livelihood, hard to undo for the wrongly-flagged.
- "screen cannot be validated against outcomes" — NOT tripped; confirmed-abuse and overturned-appeal labels are ground truth.
- "problem is risky behavior AFTER protection (moral hazard)" — NOT tripped; the threat is concealment/adaptation BEFORE and around the decision.
- "problem is pool composition under self-selection" — partially adjacent (adverse_selection) but screening subsumes it as a source prime; not disqualifying.
None disqualifies. Pattern fits.

## SCOPED COVERAGE MAP — single operative pattern: **Hidden-Type Screening**
(I deliberately do NOT instantiate Procedural Fairness Design as a second pattern — its function is discharged inside this pattern's fairness/appeal components, which already exist. Bolting it on would over-build.)

| Component | Status | Witness / Reason |
|---|---|---|
| Hidden Attribute Hypothesis | instantiated | Name the hidden attribute as "intent/behavior consistent with fraud/manipulation," not "looks unusual." |
| Candidate Pool Boundary | instantiated | Screen scoped to accounts whose behavior crosses defined risk surfaces; not blanket surveillance of all sellers. |
| Screening Signal or Test | instantiated | A rotating, partly-private mix of behavioral and relational signals (not a single fixed fingerprint). |
| Elicitation Channel | instantiated | Active probes: graduated friction / verification challenges that are costly to fake but cheap for honest sellers (incentive_compatibility). |
| Threshold and Routing Rule | instantiated | Banded routing: clear→pass, ambiguous→soft review, strong→restrict-with-review; thresholds tied to harm not convenience. |
| Validation and Base-Rate Check | instantiated | Continuous backtest of flags against confirmed-abuse and overturned-appeal labels; retire signals with low predictive value. |
| False Positive / False Negative Policy | instantiated | Explicit asymmetry: bias toward reversible soft actions for ambiguous cases so a wrong flag rarely lands as a hard ban. |
| Fairness and Accessibility Review | instantiated | Subgroup audit so the screen tracks abuse, not seller resources/language/category. |
| Review or Appeal Path | instantiated | Reasoned notice + a real contest route with remedy authority (the recourse pillar lives here). |
| Monitoring and Recalibration Loop | instantiated | The core anti-decay engine: watch hit-rate decay + appeal-overturn rate, rotate signals, hold out a fresh adversary signal. |
| Self-Selection Menu | homolog | Discharged by the graduated-friction tiers: honest accounts clear cheap checks, evaders face escalating verification — a self-sorting menu without a separate construct. |
| Staged Commitment Path | homolog | Discharged by the soft→hard escalation ladder (warn/limit before ban); same "limited commitment before full" function. |
| Evidence Standard | instantiated | Hard actions require corroborated, account-specific evidence, not a lone score. |
| Privacy and Data Minimization Rule | correctly_declined | Already governed by the pool boundary + evidence standard; adding a separate minimization regime would over-build relative to this problem's stated levers (signals, actions, logging, appeal). |
| Assessor Independence Rule | instantiated | Appeals reviewed by reviewers separate from the flagging system, with authority to overturn — discharges impartiality cheaply. |
| Fallback Measurement Path | correctly_declined | When signals are unreliable the routing already defaults to soft review + human judgment; a distinct fallback measurement apparatus is not earned here. |

No component is `missing`. Stop condition satisfied.

COST: ~9 operations, ~14k tokens

===RECOMMENDATION_BEGIN===
Treat the system as a process for deciding a hidden question about each account — is this behavior consistent with abuse? — rather than as a fixed list of red flags. The two ways it fails, going blind to adapted abusers and crushing innocents, are the same failure: the test stopped tracking what it was meant to measure. Fix both at once.

Stop relying on a static signature. Bad actors decay your hit rate by learning the exact thing you look for and editing around it, so make the thing you look for a moving, partly-private mix. Combine many weak behavioral and relational signals rather than a few decisive ones, keep some of them confidential and rotated, and continuously hold out a fresh signal that the current population has not yet learned to dodge. Lean on signals that are intrinsically expensive for a real abuser to fake but cheap for an honest seller to satisfy, so that evading the screen costs roughly what behaving well would have cost — that is what keeps the screen alive as people adapt.

Separate detection from punishment with a routing rule keyed to confidence and harm, not convenience. Clear-low-risk accounts pass untouched. Ambiguous accounts get soft, reversible handling — a verification challenge, a temporary limit, a quiet hold on payouts — never a silent hard ban. Only strong, corroborated, account-specific evidence justifies a hard restriction, and even then it routes through review. This graduated ladder doubles as a sorting menu: honest accounts clear the cheap checks and move on, while genuine evaders face escalating verification they cannot satisfy, revealing themselves without a single rigid tripwire.

Give every restricted account a reason and a way out. When you restrict, send understandable notice that states the basis specifically enough to respond to (without handing abusers a recipe), and open a real contest path: the account can submit context and evidence, and a reviewer separate from the flagging engine — with authority to overturn and restore — decides. A reviewer who can only rubber-stamp the original call is not a remedy. Set a response deadline so appeals are not a black hole.

Close the loop, because this is where decay is caught. Log every flag, action, appeal, and outcome. Continuously check current flags against two ground truths: accounts later confirmed abusive, and restrictions later overturned on appeal. A falling confirmation rate means abusers are adapting — rotate and refresh signals. A rising overturn rate means the screen is drifting toward punishing the innocent — loosen thresholds, demand more corroboration, or retire the offending signal. Retire any signal whose predictive value has collapsed; a flag that no longer separates abuse from noise is worse than admitting uncertainty, because it keeps banning real sellers while feeling legitimate. Audit across seller segments so the screen tracks abuse rather than who has fewer resources or sells in an unusual category.

Operating rule: never let a hard, hard-to-reverse restriction rest on an unexplained score alone — every hard action must carry corroborated, account-specific evidence, a stated reason, and an independent route to reverse it; and the moment confirmation rate falls or overturn rate rises, treat the screen as decaying and change it before the next flag goes out.
===RECOMMENDATION_END===
