Acting as the CONTROL POLICY of a reasoning engine, I executed the prescribed nine-step procedure in order, once each, calling the encyclopedia tools.

## Step 1 — Specify the decision
Choose ONE governance rule for a shared, slowly-degrading resource that is **permanently lost past a threshold** and whose use is **hard to meter directly**, drawn on by **many independent users**.
- **Approach A:** one uniform per-user quota, equal for all, fixed in advance.
- **Approach B:** allocation with state-monitoring, graduated sanctions for over-use, and a collective-adjustment process as conditions change.
- **Success criteria (joint):** resource must stay above the point of permanent loss AND users must remain viable.

## Step 2 — Identify operative primes (search_prime / search_by_facets)
Semantic meta-model query plus a five-facet decomposition (oblique, multi-structure). Top operative hits:
- `tragedy_of_the_commons` (distinctive, top hit on both the blended query 0.765 and the facet fusion) — the spine: many users, rivalrous + non-excludable, 1/N cost-share, Nash overshoot to collapse.
- `irreversibility` (distinctive) — the threshold past which loss is permanent.
- `adaptive_capacity` (mid) — governance that reconfigures as conditions change; the efficiency-vs-viability reserve.
- `governance`, `mechanism_design_and_incentive_structure`, `mandatory_vs_default_norms` (governance facet).
- supporting: `scarcity`, `free_riding`, `social_dilemma`, `temporal_decay_and_degradation`, `threshold_and_criticality`.

## Step 3 — Salience-rank
1. **tragedy_of_the_commons** (names the whole structure; carries the governance-architecture menu).
2. **irreversibility / threshold_and_criticality** (raises the cost of being wrong — collapse is non-recoverable).
3. **adaptive_capacity** (the distinguishing feature of Approach B: state-coupled adjustment).
4. **mechanism_design / governance** (how rules align private use with collective cost).
5. **scarcity, free_riding, social_dilemma** (the incentive misalignment being corrected).

## Step 4 — Prune
Drop `social_loafing`, `social_identity_theory`, `responsibility_diffusion`, `solidarity` — framed social-psychology twins that don't bear on the rule-choice. Drop `measurement_uncertainty` as a standalone (folded into the "hard to meter" boundary condition of the commons). Keep the top five.

## Step 5 — Typed relational model
- Substrate: rivalrous, non-excludable common-pool resource with finite carrying capacity and a **hard floor** (cross it → irreversible loss).
- Operator: N independent rational users; each captures full private benefit, bears ~1/N of collective cost → over-extraction.
- Invariant: while private benefit > 1/N · cost, every user over-uses; uncoordinated best-responses overshoot sustainable yield.
- Boundary conditions: use is **hard to meter** (weak observability); degradation is **slow** (lagged feedback); the floor is **absorbing** (no recovery).
- Failure modes: (a) overshoot → collapse; (b) rule mismatched to local/changing state → either collapse or non-viable users.

## Step 6 — Domain-stripped meta-model
"Many independent agents draw on a shared, rivalrous, hard-to-observe stock that decays slowly and is destroyed irreversibly past a threshold; private incentive diverges from collective survival; pick a static uniform allocation rule OR a state-monitoring, graduated-sanction, collectively-adaptive rule, under a joint constraint of resource survival AND user viability."

## Step 7 — Query archetypes whose source primes match (find_archetypes_for_prime / get_archetype)
`find_archetypes_for_prime(tragedy_of_the_commons)` → source archetype **`commons_governance`** (plus related: harmful_emergence_containment, iterative_reciprocity, public_goods_provision, reciprocity_protocol_design).
`get_archetype(commons_governance)` — canonical formula: shared_resource + distributed_access + private_gain/shared_cost → governance_rules + **monitoring** + **graduated sanctions** + **replenishment** + legitimacy → sustainable_collective_use. Components include Monitoring Signal, Use Limit, Graduated Sanction Rule, Adaptation Cadence, Dispute Resolution. Decision rule: *use-limits must be connected to observable resource condition; adapt rules when conditions change; arbitrary limits will be resisted and may fail to protect the actual resource.* Target invariant: *use limits connected to observable resource condition*; *the governance system can revise itself as conditions change.* Failure mode flagged explicitly: **Rigid quota failure — when use limits ignore changing conditions; mitigation is resource-condition triggers, periodic review, exception processes.**

## Step 8 — Apply each candidate's logic to BOTH approaches; reconcile
- **commons_governance** maps almost one-to-one onto **Approach B** (monitoring + graduated sanctions + collective adaptation = its core mechanism set) and identifies **Approach A** as the named "rigid quota failure" antipattern.
- **tragedy_of_the_commons:** both A and B break the 1/N over-extraction logic in principle. But the prime is explicit that Hardin's single fixed-rule prescription is the *contested* form, and Ostrom's design principles (monitoring, graduated sanctions, collective-choice, adaptation) are the empirically successful pathway — i.e. B.
- **irreversibility / threshold:** because the floor is absorbing, the controlling consideration is the *worst-case*, not the average. A fixed quota set without state feedback can only be right by luck: if demand rises, the renewal rate falls, or the quota was set too high, a uniform fixed rule has **no mechanism to detect the approach to the floor and tighten before it is crossed**. B's monitoring + adjustment is precisely a margin-of-safety against the irreversible failure.
- **adaptive_capacity:** A has zero reconfiguration reserve (static by construction); B embeds sensing + adjustment, the reserve needed when conditions move past current scope.
- Reconcile: A's only advantages are simplicity and low monitoring cost. But "hard to meter" cuts *against the assumption A relies on* — a fixed per-user quota is unenforceable/uncheckable without some monitoring anyway, while B's graduated sanctions tolerate noisy observation. A's strengths do not touch the binding constraint (avoiding an irreversible floor under uncertainty); B's strengths target it directly.

## Step 9 — Evaluate fit against failure conditions / success criteria
Walk each approach against the two failure conditions (cross the floor; users non-viable):
- **Approach A (fixed uniform quota):** Fails the irreversibility test under any drift — wrong initial calibration, changing demand, or slow renewal decline goes undetected until the floor is crossed, and crossing is permanent. A uniform quota also ignores user heterogeneity, so a single number is simultaneously too tight for some (non-viable users) and too loose to protect the stock for others. No recovery path. Satisfies neither criterion robustly.
- **Approach B (monitored, graduated-sanction, adaptive):** Keeps use coupled to observed resource condition, tightens *before* the floor when monitoring shows decline, and graduated sanctions sustain compliance under imperfect metering; the collective-adjustment process keeps users viable as it rebalances. This is the empirically successful commons-governance pathway and directly preserves both the resource-survival and user-viability invariants.

**Choice: Approach B.** The decisive lever is the *irreversible threshold under imperfect observation* — only a state-coupled, adjustable rule can detect and arrest the approach to permanent loss; a fixed uniform quota is the named rigid-quota failure mode and is right only by luck.

===DECISION_BEGIN===
DECISIVE_FACTOR: Because crossing the threshold destroys the resource permanently and use cannot be metered precisely, only a rule that tracks the resource's actual condition and adjusts before the limit is reached can reliably prevent irreversible loss.
CHOICE: B
JUSTIFICATION: A single fixed quota set in advance can only protect the resource if the original guess about safe use, demand, and renewal stays correct forever; any drift goes undetected until the resource is already past the point of no return, with no way back. Watching the resource's condition, escalating penalties for over-use, and adjusting allocations as things change lets the rule tighten before collapse while still leaving users enough to stay viable. It also copes better with the fact that use is hard to measure, since penalties can scale with the severity and repetition of harm rather than relying on one exact number. The fixed equal quota is simpler but bets everything on a guess that cannot be corrected once it proves wrong.
===DECISION_END===
