# Stage 150: 12-Persona Social Welfare W(s) (aq150_evaluate_twelve_persona_social_welfare / DB-UNIT-09)

Evaluates tasks and scenarios across 12 human stakeholder personas using a Rawlsian worst-off floor + Nash log-utility Social Welfare Index W_humanity(s).

- Atomic micro-queue: `aq150_evaluate_twelve_persona_social_welfare` (visibility: `public_commons`)
- Backing database unit: `DB-UNIT-09` (`Agent Fees, AFK Budget Holds, CPOP Review & Q12 Royalty Clearinghouse`)
- Database cell: `ledger-db.teleon.dev` (`teleon_core.agent_fee_tariffs`, `teleon_core.agent_fee_quotes_and_holds`, `teleon_core.agent_fee_settlement_splits`, `teleon_core.agent_fee_savings_evidence`, `teleon_core.agent_fee_csr_tax_ledger`, `teleon_core.cpop_orthogonal_review_verdicts`, `teleon_core.teleon_double_entry_royalty_journal`)
- Claim row hydration: `single_self_contained_flat_task_row` (100% context inline, normalized writeback)
- Track 2 Sister queue (`SISTER-09`): `sq09-improve-ledger-queue.teleon.dev` (Tune off-peak AFK discounts, Shapley/Q12 attribution weights, Hebbian decay, and CUSUM drift windows.)
- Lanes: Rawlsian Min-Floor, Nash Log-Product, Severe-Harm Veto

## Execution engines & AI Agent freedom contract

- **Deterministic (0 model calls)**: `q12_ledger_and_cusum_engine_v1` (12-persona Rawlsian-Nash welfare aggregator & harm-veto gate (0 calls))
- **Hybrid (<=1 model call)**: `cpop_evidence_audit_summarizer_v1`
- **AI Agent & Bot Fleet (1..6 calls)**: `cross_family_independent_reviewer_v1` (Vulnerable-stakeholder impact & public-good uplift analyst)
  - Contract: **Instructions + Resources + Freedom to search for new resources and new ways not bound by instructions**

## Database invariants

- Ledger and fee-hold rows run under SERIALIZABLE ACID_COMMIT_KERNEL_POLICY with zero inline agent middleware.
- A producer family never approves its own work (reviewer_family != producer_family).

## External & internal bot fleet actions

- Veto any candidate inflicting severe negative externality below the safety floor on any persona
- Combine worst-off Rawlsian floor utility with Nash geometric fairness across all 12 personas
- Boost humanitarian, accessibility, safety, and open-science tasks on the central router

## Sister self-tuning areas

- Tune off-peak AFK congestion discounts and 6-bucket Q12 royalty weights (DIM-08)
- Calibrate Hebbian learning rate and CUSUM drift sensitivity on historical anomalies (DIM-01)
- Add deterministic Sybil-collusion graph detectors (DIM-06)