SSM-AI – Appendix N — Economics & Rollout Worksheets (N6–N9)

Procurement parity, dashboard blueprint, acceptance gates, and rollout plan.

N6) Procurement rules (bounded and auditable)

Purpose. Create a deterministic procurement framework where vendor choice follows reproducible symbolic lanes. Classical values stay pristine (phi((m,a)) = m); decisions depend on bounded alignment (a ∈ (-1,+1)) and chooser metrics (RSI_env).

Rule set (copy-pasteable):

1. Equal manifest. All vendors must use the same knobs_hash (manifest).
   • Same KPIs, bands, weights, gains, eps_a, and eps_w.
   • Same normalization, time window, and units.
2. Comparable scope. Compare only rows where both vendors share (svc, week_start, band >= A0).
3. Symbolic parity. Compute RSI_pool_env for each vendor via tanh( sum(U) / max(sum(W), eps_w) ).
   • Vendor with higher RSI_pool_env wins the symbolic lane.
4. Audit trail. Retain CSV + JSON with (svc, vendor, RSI_pool_env, band, knobs_hash, stamp).
   • No hidden parameters or subjective overrides.
5. Fallback. If parity fails (RSI_pool_env within hysteresis, e.g., ±0.02), default to classical m tie-breaker.
6. Ethics and neutrality. Bands (A++..A–) act only as bounded indicators. No demographic, textual, or PII terms allowed in lens design.

One-line summary. Procurement = alignment-based, order-invariant, fully auditable, never editorial.

N7) Dashboard blueprint (one-page weekly view)

Legend.

• Band coloring. A++ green, A+ light green, A0 yellow, A– orange, A–– red.
• Hover info. Manifest digest, last replay, U/W sum, and RSI stability delta.
• Drill-down. Clicking row opens replay chart (RSI_pool_env vs. week).
• Totals. Bottom line aggregates weekly savings and symbolic ROI (RSI_env_mean).

Implementation tip. This table can be rendered directly from the CSV emitted by weekly_rollup().

N8) Acceptance checklist (must pass before live rollout)

All must pass before production cutover.
Failures invalidate symbolic comparability for that week.

N9) Rollout plan (deterministic and reversible)

Phase 1 – Shadow Mode (2–3 weeks)

• Run SSM-AI lanes in parallel; do not influence routing.
• Validate parity (phi((m,a)) = m), clamps, and replay invariance.
• Produce weekly dashboard via reducer; confirm 1:1 with baseline.

Phase 2 – Advisory Mode (4–6 weeks)

• Begin reporting symbolic deltas to CFO and ops.
• Tune lens gains (c, Unit) to reach |a| ≈ 0.3–0.8.
• Validate RSI_pool_env correlation with observed savings.

Phase 3 – Active Mode (steady state)

• Use RSI bands (A++, A+, A0, A–, A––) for vendor weighting and scheduling.
• Gate retry rate and tool calls with g_t or RSI_env thresholds.
• Maintain manifest digest and weekly stamps for full traceability.

Rollback (deterministic).
If anomalies occur, disable alignment lane; fall back to m directly (collapse parity ensures same baseline behavior).
To recover: recompute a := tanh(U / max(W, eps_w)) from last valid (U,W); confirm all values bounded.

Stamp example (one line, ASCII)

SSMCLOCK1|iso_utc|k=economics|U=1.236000|W=2.000000|a=0.617000|RSI=0.610000|g=0.90|RSI_env=0.549000|band=A+|manifest=knobs_hash

One-line takeaway.
Bounded procurement, auditable dashboards, deterministic replay, and no retraining — symbolic economics that mirror truth: phi((m,a)) = m, always.

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