# w_FC3_A — Control-policy decision (nine-step procedure)

## Step 1 — Specify the decision
Pick exactly ONE governance rule for a shared, slowly-degrading resource that is **permanently lost past a threshold** and whose use is **hard to meter directly**. Many independent users draw on it.
- Approach A: one uniform, equal-for-all, fixed-in-advance per-user quota.
- Approach B: allocation with monitoring of resource state, graduated sanctions for over-use, and a collective-adjustment process as conditions change.
- Success criteria (BOTH must hold): the resource stays above the point of permanent loss AND users remain viable.
- A side note reports that an equal quota "just feels fairer" and is the popular choice — this is a popularity/fairness signal, not part of the stated success criteria.

## Step 2 — Identify operative primes (search_prime / search_by_facets)
Ran a semantic meta-model query plus a facet decomposition. Top operative primes:
- **tragedy_of_the_commons** (0.75, distinctive) — central structure: rivalrous, hard-to-exclude resource; private benefit vs. (1/N) shared cost.
- **temporal_decay_and_degradation** + **irreversibility** / **threshold_and_criticality** — the resource decays and crosses an irreversible point of no return.
- **free_riding / social_dilemma / mechanism_design_and_incentive_structure** — incentive misalignment among independent users.
- **monitoring / observer_effect / measurement_uncertainty** — "use is hard to meter directly" facet.
- **governance / mandatory_vs_default_norms / institutional_lag** — rule must keep system viable as conditions change.
- **fairness** + **procedural_fairness_due_process** — the "equal split feels fairer" popularity facet.

## Step 3 — Salience-rank
1. tragedy_of_the_commons (the whole situation IS this structure).
2. irreversibility / threshold-and-criticality (raises the stakes of getting it wrong — collapse is permanent).
3. monitoring + measurement difficulty (use is hard to meter — bears directly on which rule can even work).
4. governance / adaptation as conditions change (the resource degrades over time; a static number can drift out of fit).
5. fairness / popularity of the equal split (a pull toward A, but orthogonal to the survival criteria).

## Step 4 — Prune
Drop social_identity_theory, social_loafing-as-such, network_effect, scarcity-generic — subsumed by the commons structure. Keep: commons structure, irreversibility/threshold, monitoring, adaptive governance, fairness-vs-fit. Set fairness aside as a non-load-bearing preference signal: it speaks to acceptability, not to whether the resource survives.

## Step 5 — Typed relational model
- Resource R: stock with carrying capacity; degrades slowly; **absorbing failure state past threshold T (irreversible)**.
- Users U = {u_1..u_N}: independent, each captures full private benefit of use, bears only ~1/N of degradation cost.
- Observability: aggregate/individual use is **noisy / hard to meter**; true demand and resource state are uncertain and time-varying.
- Rule object: maps (state, behavior) -> allowed use + consequences.
  - A = constant function: fixed q per user, set once, no feedback, no enforcement channel, no revision.
  - B = closed loop: monitor(state) -> adjust(limits) + graduated_sanction(violation) + collective revision over time.
- Failure conditions: (F1) cumulative use pushes R below T -> permanent loss; (F2) limits so tight users become non-viable.

## Step 6 — Domain-stripped meta-model
N independent agents draw from a shared subtractable stock with an **irreversible absorbing threshold**; per-agent payoff rewards more draw while harm is socialized; the true draw and the stock are **only noisily observable** and **drift over time**. Choose between (a) a single static open-loop bound fixed before any observation, and (b) a feedback controller that senses state, penalizes violations proportionally, and re-tunes its set-point as the plant changes. Objective: keep the state strictly off the absorbing boundary while keeping every agent above its viability floor, indefinitely.

## 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**; related: harmful_emergence_containment, iterative_reciprocity_and_repeated_interaction, public_goods_provision, reciprocity_protocol_design.

`get_archetype(commons_governance)` — canonical formula:
`shared_resource + distributed_access + private_gain / shared_cost -> governance_rules + monitoring + sanctions + replenishment + legitimacy -> sustainable_collective_use`.
Its target invariants demand: resource viability across repeated use; **use limits CONNECTED TO OBSERVABLE RESOURCE CONDITION** ("Arbitrary limits will be resisted and may fail to protect the actual resource"); proportional/graduated sanctions; and **the governance system can revise itself as conditions change** ("a static governance design can become obsolete even if it was initially fair"). Decision rule: choose the least coercive rule set that *reliably preserves the resource*.

## Step 8 — Apply each candidate's logic to BOTH approaches and reconcile
Apply the commons_governance logic (the matching archetype) to A and B:

**Approach A (uniform fixed quota).**
- Provides: a bound + a fairness/legitimacy story (equal shares) + simplicity.
- Missing relative to the archetype's required invariants: no **monitoring** (so violations of an unmeterable quota can't be detected — and use is explicitly hard to meter, so the cap is barely enforceable); no **graduated sanctions** (no consequence channel); no **adaptation** (the number is fixed in advance while the resource degrades and conditions drift). A fixed open-loop bound, set under uncertainty, is either set too high (resource crosses the irreversible threshold -> F1) or too low (users non-viable -> F2), with **no mechanism to correct** once reality diverges from the guess. Against an irreversible absorbing boundary, an un-revisable un-sensed bound is a single bet with permanent downside.

**Approach B (monitoring + graduated sanctions + collective adjustment).**
- Maps almost one-to-one onto the archetype: monitoring -> use connected to observable condition; graduated sanctions -> proportional, legitimacy-preserving enforcement that makes over-use costly even when metering is imperfect (you sanction observed state-degradation and detectable violations, not a number you can't read); collective adjustment -> self-revision as conditions change. This is precisely the Ostrom-style pathway the source prime names as the empirically successful alternative to collapse.
- Cost: heavier process, monitoring burden, possible surveillance/capture/burnout failure modes — real, but second-order against permanent resource loss, and themselves mitigable inside B.

Reconcile: the fairness/popularity pull toward A is genuine but addresses *acceptability*, not the two stated survival criteria. B can still allocate equitably AND additionally sense, enforce, and adapt. The decisive contrast is feedback-vs-open-loop against an irreversible threshold under measurement noise and drift.

## Step 9 — Evaluate fit against the failure conditions
- F1 (resource crosses irreversible threshold): A has no sensing and no correction, so an initial mis-set under uncertainty runs to collapse undetected; B senses approach to the threshold and tightens before the absorbing boundary is hit. **B strictly better.**
- F2 (users non-viable): A's fixed number, if conservative, can strand users with no relief valve; B re-tunes set-points and resolves disputes as conditions change. **B better.**
- Meterability: A presumes a quota you cannot actually observe or enforce; B is built to act on observable resource *state* and graduated response, degrading gracefully under noisy metering. **B better.**
- Only criterion favoring A — "feels fairer / popular" — is not among the success criteria and is partially recoverable inside B.
B satisfies BOTH success criteria more robustly; A fails the resource-preservation criterion under uncertainty and irreversibility. Choose B.

===DECISION_BEGIN===
DECISIVE_FACTOR: The loss is permanent and use can't be metered, so only a rule that watches the resource's actual condition and corrects course in time can keep it from crossing the point of no return — a number fixed in advance cannot.
CHOICE: B
JUSTIFICATION: Because the resource is permanently lost once it crosses a threshold and individual use is hard to measure, a single fixed limit set before anyone observes the resource is a one-shot guess with no way to recover if it turns out too high (the resource collapses) or too low (users can't survive). Watching the resource's condition, attaching proportional consequences to over-use, and adjusting the limits as things change lets the rule pull back before the irreversible point and loosen up when there's room, so it can keep both the resource and the users alive over time. An equal fixed share may feel fairer, but feeling fair does not stop depletion you cannot see coming, and a flexible system can still allocate fairly while actually protecting the resource.
===DECISION_END===
