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ASSEMBLY-WP-0009-T04: jurisdiction archetypes J1–J4 (perm lever, jurisdictions.py, AdoptionBand § 4, provisional EU/US/AR mapping)

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Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

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1
AGENTS.md
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@ -31,6 +31,7 @@ cd simulations/gadm02 && python3 -B agency.py # GADM-0.4 cohort agency
cd simulations/gadm02 && python3 -B grounded_readings.py # readings under grounded parameters
cd simulations/gadm02 && python3 -B band_probe.py # scenarios across the adoption band B1–B4
cd simulations/gadm02 && python3 -B legitimacy.py # sentiment and legitimacy loop across the band
cd simulations/gadm02 && python3 -B jurisdictions.py # jurisdiction archetypes J1–J4 across the band
```
Reality checkpoints (ASSEMBLY-WP-0010): scheduled by activity-core and

93
models/AdoptionBand.md
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@ -165,3 +165,96 @@ peak (0.40 at year 10).
`Ppol` suggests that it should.
- The weights in `pb` and all loop parameters are not yet chosen by data. The
readings are qualitative until the L6 sentiment series constrain them.
## 4. Jurisdiction archetypes (T04)
### 4.1 Settings
Archetypes are lever settings (`research/ScenarioBandMethod.md` § 4). Every
archetype starts from the same anchor equilibrium. Its levers apply from
t = 0, so differences come from policy, not from different starting states.
`perm` (deployment permissiveness) is a new `gadm04` lever, default 1. It
scales the organism inflow.
| Archetype | perm | m | expl | L / lin_open | x_att | other | initial Σ |
|---|---|---|---|---|---|---|---|
| J1 rights-first / precautionary | 0.6 | 0.7 | 0.7 | 0.6 / 0.6 | 0.3 | std 0.6 | 0.45 |
| J2 deregulated fast-adopter | 1.5 | 0.2 | 0.2 | 0.2 / 0.2 | 0.7 | — | 0.65 |
| J3 platform-led | 1.3 | 0.1 | 0.4 | 0.1 / 0.1 | 0.8 | disc 0.6 | 0.5 |
| J4 state-coordinated | 1.3 | 0.2 | 0.4 | 0.2 / 0.2 | 0.5 | managed sentiment: sig_gain 0.5, leg_lag 10 | 0.6 |
Lever magnitudes are choices that express the method's low/medium/high
table, not estimates. Reproduce: `cd simulations/gadm02 && python3 -B
jurisdictions.py` (`results/jurisdictions.md`).
### 4.2 Results
J1 has the highest VFAC at t 100 at every anchor, with and without the
legitimacy loop. It is the only archetype that keeps domination below the
P floor (P ≤ 0.65 against 0.79–0.89 for J2–J4).
J1 − J2 (positive = lagging does better):
| Anchor | Loop | Δ mean Y* 0–25 | Δ mean Y* 0–100 | Δ VFAC t 100 | Δ G | Δ A |
|---|---|---|---|---|---|---|
| B1 | off / on | +0.000 / +0.012 | +0.195 / +0.205 | +0.32 / +0.29 | +0.24 / +0.21 | +0.14 / +0.06 |
| B2 | off / on | −0.024 / +0.024 | +0.167 / +0.247 | +0.32 / +0.27 | +0.23 / +0.20 | +0.15 / +0.03 |
| B3 | off / on | −0.038 / +0.047 | +0.074 / +0.263 | +0.25 / +0.23 | +0.16 / +0.15 | +0.19 / +0.03 |
| B4 | off / on | −0.044 / +0.069 | −0.017 / +0.271 | +0.18 / +0.19 | +0.13 / +0.12 | +0.17 / +0.02 |
Decomposition (each lever regime at perm 0.6 versus 1.5, rest unchanged):
lagging by itself always costs early output (mean Y* 0–25 lower by 0.01 to
0.10) and raises VFAC by 0.01–0.13. Under J2 levers with the legitimacy loop
on, speed hardly matters (|Δ VFAC| ≤ 0.01), because backlash slows adoption
anyway.
### 4.3 Readings
1. **The archetype gap comes mainly from rights, not from speed.** The J1–J2
VFAC gap is 0.18–0.32. The speed component alone is 0.01–0.13. The
owner's question "is lagging better or worse?" therefore splits: lagging
*as such* costs output early and helps agency and attention a little. What
decides the long run is membrane rights, open lineages and attention
protection.
2. **"Europe lags" (J1-like):** the model says lagging costs early output when
there is no backlash (B2–B4, loop off: −0.02 to −0.04 mean Y*). It does
not make the EU worse off in the long run at any anchor. This rests on the
rights levers and on the closed-economy assumption: a J1 jurisdiction that
imports concentrated foreign lineages does not get J1's L and lin_open.
3. **"Argentina may thrive" (J2-like):** a J2 regime is richer early only
without the legitimacy loop and only at B2–B4 (+0.02 to +0.04 mean Y*
0–25). With the loop, the positive start turns into backlash (Σ 0.65 →
0.21–0.29). J2 then ends poorer than J1 at every anchor and breaks the
domination floor. "Thriving" in the model requires sentiment that stays
positive, which requires broad claims and contained domination. That is
what checkpoint indicators L6 (sentiment) and F2 (concentration) can test.
4. **Managed sentiment (J4) sustains adoption but not VFAC.** With the loop
on, J4 keeps the highest K_Ω (0.38 at B4, t 100 against 0.08 for J2),
because its weak and slow sentiment response avoids backlash. Its domination
stays at P ≈ 0.82, and VFAC is among the lowest.
5. **Long-run prosperity follows domination.** Y* falls in J2–J4 because
domination lowers effective demand and selected value in GADM. The
reading "deregulation is poorer in the long run" depends on that
mechanism (GADM-0.2 § auxiliaries) and should be read as conditional on it.
### 4.4 Mapping real jurisdictions (provisional)
Policy evidence as known up to 2025. The L7 legislation collection at each
checkpoint updates it, and the mapping is revisited at every full checkpoint.
| Jurisdiction | Nearest archetype | Evidence | Open questions |
|---|---|---|---|
| EU | J1, with moderate perm | AI Act (Reg. 2024/1689) in force since Aug 2024, prohibitions from Feb 2025, general-purpose AI duties from Aug 2025; DMA; Data Act applicable from Sep 2025. Digital-omnibus proposals (late 2025) to delay parts of the high-risk regime point to drift toward higher perm. | lineage imports: EU adoption runs largely on non-EU lineages, so effective L and lin_open are lower than its rules suggest |
| US | J3 (market structure) with J2 federal stance | Jan 2025 executive order revoking EO 14110; AI Action Plan (Jul 2025); no federal AI statute. Several state laws (e.g. Colorado, California) apply rights levers locally. | federal–state split: one archetype per level may be needed |
| Argentina | J2 by stance | Deregulation programme (DNU 70/2023, Ley Bases 2024 with the RIGI investment regime); no comprehensive AI law as of 2025. | adoption level unknown (L3 manual); AR adopts foreign lineages, so its L and lin_open are set abroad; initial sentiment unmeasured (L6) |
| (reference) China | J4 | state-coordinated deployment, algorithm registration and generative-AI rules | not a tracked jurisdiction; anchors J4 only |
### 4.5 Limitations
- Closed economy: no lineage import, trade or capital flows between
jurisdictions. This is the largest gap for both hypotheses.
- One lever setting per archetype. No lever trajectories (for example the EU
loosening over time).
- The output advantage of fast adoption is short-lived in the model partly
by construction (domination feeds back on demand); a model with
export-led gains from organisms could reverse reading 3.

2
simulations/README.md
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@ -9,7 +9,7 @@ model version it implements.
| `gadm02/gadm03.py` | GADM-0.3: power channels, wealth, attention (`models/GADM-0.3.md`) | `power_channels.py`, `wealth.py`, `organism_grid.py`, `test_gadm03.py` |
| `gadm02/gadm03c.py` | GADM-0.3 with life-cycle cohorts (`models/GADM-0.3.md` § 5) | `life_cycle.py`, `test_gadm03c.py` |
| `gadm02/gadm04.py` | GADM-0.4: durable capability, grounded dO, equilibrium start, cohort agency (`models/GADM-0.4.md`) | `life_cycle.py --model gadm04`, `robustness.py`, `agency.py`, `grounded_readings.py`, `test_gadm04.py` |
| `gadm02/grounding.py` | evidence-grounded parameter sets and adoption band B1–B4 (`models/ParameterGrounding.md`, `models/AdoptionBand.md`) | `variant("slow" \| "central" \| "fast" \| "aggressive")`, `band_probe.py`, `legitimacy.py` |
| `gadm02/grounding.py` | evidence-grounded parameter sets and adoption band B1–B4 (`models/ParameterGrounding.md`, `models/AdoptionBand.md`) | `variant("slow" \| "central" \| "fast" \| "aggressive")`, `band_probe.py`, `legitimacy.py`, `jurisdictions.py` |
Python 3.10+, standard library only. Use `python3 -B` after quick edits: a stale
bytecode cache can mask same-size changes made within the same second.

14
simulations/gadm02/gadm04.py
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@ -83,6 +83,9 @@ PARAMS.update({
"leg_m": 0.5, # membrane rights tightening per unit of stance below neutral
"leg_expl": 0.5, # explainability tightening per unit of stance below neutral
"sig_pb0": None, # reference perceived benefit; None = value at the initial state
# WP-0009-T04: deployment permissiveness of a jurisdiction; scales the organism
# inflow (investment, learning, seeding) like the legislative stance. 1 = neutral.
"perm": 1.0,
})
@ -151,12 +154,15 @@ def derivatives(s: dict, p: dict) -> tuple[dict, dict]:
Ko = s["Ko"]
grow = (p["aO"] * a["Vsel"] * a["dem"] + p["ell"] * Ko * a["dem"] * s["Q"]) * (1 - Ko)
d["Ko"] += p["selfenh"] * Ko * grow
if p["sent"]:
# sentiment and permissiveness scale the organism inflow, not its depreciation
scale = max(0.0, 1 + p["sig_adopt"] * 2 * (s["Sig"] - 0.5)) \
* max(0.0, 1 + p["leg_perm"] * 2 * (s["Lg"] - 0.5))
if p["sent"] or p["perm"] != 1.0:
# permissiveness, sentiment and stance scale the organism inflow, not its depreciation
scale = p["perm"]
if p["sent"]:
scale *= max(0.0, 1 + p["sig_adopt"] * 2 * (s["Sig"] - 0.5)) \
* max(0.0, 1 + p["leg_perm"] * 2 * (s["Lg"] - 0.5))
inflow = d["Ko"] + p["dO"] * s["Ko"]
d["Ko"] = scale * inflow - p["dO"] * s["Ko"]
if p["sent"]:
pb = perceived_benefit(s, a, p)
pb0 = pb if p["sig_pb0"] is None else p["sig_pb0"]
target = min(1.0, max(0.0, 0.5 + p["sig_gain"] * (pb - pb0)))

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simulations/gadm02/jurisdictions.py Normal file
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@ -0,0 +1,120 @@
"""Jurisdiction archetypes J1-J4 across the adoption band (ASSEMBLY-WP-0009-T04).
Archetypes are lever settings (research/ScenarioBandMethod.md § 4), not
country names. Every archetype starts from the same anchor equilibrium; its
levers apply from t = 0. Tests the owner's hypotheses: under which band anchor
and with or without the legitimacy loop does lagging (J1) or fast deregulated
adoption (J2) do better?
Usage: python3 -B simulations/gadm02/jurisdictions.py
"""
from __future__ import annotations
import sys
from pathlib import Path
sys.dont_write_bytecode = True
import gadm04 as g # noqa: E402
import grounding as gr # noqa: E402
OUT = Path(__file__).resolve().parent / "results"
ANCHORS = list(gr.BAND)
FLOORS = {"G": (">=", 0.2), "E": (">=", 0.2), "P": ("<=", 0.7), "A": (">=", 0.2)}
# (params, initial sentiment). Defaults: m .30, L .30, lin_open .30, expl .40,
# x_att .50, disc .40, std .50, perm 1.
ARCHETYPES = {
"J1": ("rights-first / precautionary",
{"perm": 0.6, "m": 0.7, "expl": 0.7, "L": 0.6, "lin_open": 0.6, "std": 0.6, "x_att": 0.3}, 0.45),
"J2": ("deregulated fast-adopter",
{"perm": 1.5, "m": 0.2, "expl": 0.2, "L": 0.2, "lin_open": 0.2, "x_att": 0.7}, 0.65),
"J3": ("platform-led",
{"perm": 1.3, "m": 0.1, "expl": 0.4, "L": 0.1, "lin_open": 0.1, "disc": 0.6, "x_att": 0.8}, 0.5),
"J4": ("state-coordinated",
{"perm": 1.3, "m": 0.2, "expl": 0.4, "L": 0.2, "lin_open": 0.2, "x_att": 0.5,
"sig_gain": 0.5, "leg_lag": 10.0}, 0.6), # managed sentiment: weak, slow response
}
LOOP = {"off": 0.0, "on": 1.0}
def run(anchor: str, j: str, loop: str, var: dict, perm: float | None = None) -> dict:
vp, vi = var[anchor]
_t, lev, sig0 = ARCHETYPES[j]
lev = lev if perm is None else {**lev, "perm": perm}
rows = g.simulate({**vp, **lev, "sent": LOOP[loop]}, {**vi, "Sig": sig0, "Lg": sig0})
f = rows[-1]
viol = [k for k, (op, lim) in FLOORS.items() if (f[k] < lim if op == ">=" else f[k] > lim)]
return {"Ko10": rows[10]["Ko"], "Ko100": f["Ko"],
"Y25": sum(r["Ystar"] for r in rows[:26]) / 26, "Y100": sum(r["Ystar"] for r in rows) / len(rows),
"V10": rows[10]["VFAC"], "V100": f["VFAC"], "G": f["G"], "A": f["A"], "P": f["P"],
"Sig": f["Sig"], "floors": ",".join(viol) or "none"}
def verdict(d: dict) -> str:
"""J1 − J2 differences → reading for the 'lagging' hypothesis."""
good = sum(1 for k in ("V100", "G", "A") if d[k] > 0.005)
bad = sum(1 for k in ("V100", "G", "A") if d[k] < -0.005)
rich = "J2 richer early" if d["Y25"] < -0.005 else "J1 not poorer early"
if good == 3:
return f"lagging better on all three; {rich}"
if bad == 3:
return f"lagging worse on all three; {rich}"
return f"mixed ({good} better, {bad} worse); {rich}"
def main() -> None:
OUT.mkdir(exist_ok=True)
var = {a: gr.variant(gr.BAND[a]) for a in ANCHORS}
res = {(a, j, lp): run(a, j, lp, var) for a in ANCHORS for j in ARCHETYPES for lp in LOOP}
lines = [f"# {g.MODEL_VERSION}: jurisdiction archetypes across the band", "",
"Generated by `simulations/gadm02/jurisdictions.py` (ASSEMBLY-WP-0009-T04).",
"Baseline scenario; archetype levers from t = 0 at each anchor's equilibrium.",
"Y̅ 0–25 / 0–100 = mean potential output Y* over the period (prosperity proxy).", "",
"## Archetype levers", "", "| Archetype | Levers | Initial Σ |", "|---|---|---|"]
for j, (t, lev, s0) in ARCHETYPES.items():
lines.append(f"| {j} {t} | " + ", ".join(f"{k} {v}" for k, v in lev.items()) + f" | {s0} |")
for lp in LOOP:
lines += ["", f"## Legitimacy loop {lp}", "",
"| Anchor | Arch. | K_Ω t10 / t100 | Y̅ 0–25 / 0–100 | VFAC t10 / t100 | G | A | P | Σ end | floors broken |",
"|---|---|---|---|---|---|---|---|---|---|"]
for a in ANCHORS:
for j in ARCHETYPES:
r = res[(a, j, lp)]
lines.append(f"| {a} | {j} | {r['Ko10']:.2f} / {r['Ko100']:.2f} | {r['Y25']:.3f} / {r['Y100']:.3f}"
f" | {r['V10']:.3f} / {r['V100']:.3f} | {r['G']:.2f} | {r['A']:.2f} | {r['P']:.2f}"
f" | {r['Sig']:.2f} | {r['floors']} |")
lines += ["", "## Hypothesis test: J1 (lagging) − J2 (deregulated fast-adopter)", "",
"| Anchor | Loop | Δ Y̅ 0–25 | Δ Y̅ 0–100 | Δ VFAC t100 | Δ G | Δ A | Δ P | reading |",
"|---|---|---|---|---|---|---|---|---|"]
for a in ANCHORS:
for lp in LOOP:
r1, r2 = res[(a, "J1", lp)], res[(a, "J2", lp)]
d = {k: r1[k] - r2[k] for k in ("Y25", "Y100", "V100", "G", "A", "P")}
lines.append(f"| {a} | {lp} | {d['Y25']:+.3f} | {d['Y100']:+.3f} | {d['V100']:+.3f} | {d['G']:+.3f}"
f" | {d['A']:+.3f} | {d['P']:+.3f} | {verdict(d)} |")
lines += ["", "## Decomposition: speed (perm) vs. rights levers", "",
"Each lever regime run at slow (perm 0.6) and fast (perm 1.5) permissiveness, other levers",
"and initial Σ as in the archetype. Δ = slow − fast: the effect of lagging alone.", "",
"| Anchor | Loop | Regime | Δ Y̅ 0–25 | Δ Y̅ 0–100 | Δ VFAC t100 | Δ G | Δ A | Δ P |",
"|---|---|---|---|---|---|---|---|---|"]
for a in ANCHORS:
for lp in LOOP:
for j in ("J1", "J2"):
slow = run(a, j, lp, var, perm=0.6)
fast = run(a, j, lp, var, perm=1.5)
d = {k: slow[k] - fast[k] for k in ("Y25", "Y100", "V100", "G", "A", "P")}
lines.append(f"| {a} | {lp} | {j} levers | {d['Y25']:+.3f} | {d['Y100']:+.3f} | {d['V100']:+.3f}"
f" | {d['G']:+.3f} | {d['A']:+.3f} | {d['P']:+.3f} |")
lines += ["", "## Best archetype by VFAC t100", "", "| Anchor | loop off | loop on |", "|---|---|---|"]
for a in ANCHORS:
best = {lp: max(ARCHETYPES, key=lambda j: res[(a, j, lp)]["V100"]) for lp in LOOP}
lines.append(f"| {a} | {best['off']} | {best['on']} |")
lines.append("")
(OUT / "jurisdictions.md").write_text("\n".join(lines))
print("\n".join(lines))
if __name__ == "__main__":
main()

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simulations/gadm02/results/jurisdictions.md Normal file
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@ -0,0 +1,102 @@
# GADM-0.4: jurisdiction archetypes across the band
Generated by `simulations/gadm02/jurisdictions.py` (ASSEMBLY-WP-0009-T04).
Baseline scenario; archetype levers from t = 0 at each anchor's equilibrium.
Y̅ 0–25 / 0–100 = mean potential output Y* over the period (prosperity proxy).
## Archetype levers
| Archetype | Levers | Initial Σ |
|---|---|---|
| J1 rights-first / precautionary | perm 0.6, m 0.7, expl 0.7, L 0.6, lin_open 0.6, std 0.6, x_att 0.3 | 0.45 |
| J2 deregulated fast-adopter | perm 1.5, m 0.2, expl 0.2, L 0.2, lin_open 0.2, x_att 0.7 | 0.65 |
| J3 platform-led | perm 1.3, m 0.1, expl 0.4, L 0.1, lin_open 0.1, disc 0.6, x_att 0.8 | 0.5 |
| J4 state-coordinated | perm 1.3, m 0.2, expl 0.4, L 0.2, lin_open 0.2, x_att 0.5, sig_gain 0.5, leg_lag 10.0 | 0.6 |
## Legitimacy loop off
| Anchor | Arch. | K_Ω t10 / t100 | Y̅ 0–25 / 0–100 | VFAC t10 / t100 | G | A | P | Σ end | floors broken |
|---|---|---|---|---|---|---|---|---|---|
| B1 | J1 | 0.07 / 0.12 | 0.423 / 0.516 | 0.551 / 0.642 | 0.65 | 0.55 | 0.13 | 0.45 | none |
| B1 | J2 | 0.15 / 0.08 | 0.422 / 0.322 | 0.504 / 0.318 | 0.42 | 0.41 | 0.81 | 0.65 | P |
| B1 | J3 | 0.13 / 0.06 | 0.403 / 0.279 | 0.493 / 0.296 | 0.41 | 0.39 | 0.85 | 0.50 | P |
| B1 | J4 | 0.14 / 0.07 | 0.424 / 0.338 | 0.519 / 0.331 | 0.42 | 0.49 | 0.81 | 0.60 | P |
| B2 | J1 | 0.17 / 0.28 | 0.454 / 0.533 | 0.544 / 0.609 | 0.62 | 0.46 | 0.17 | 0.45 | none |
| B2 | J2 | 0.38 / 0.23 | 0.478 / 0.366 | 0.466 / 0.286 | 0.39 | 0.31 | 0.83 | 0.65 | P |
| B2 | J3 | 0.32 / 0.19 | 0.456 / 0.332 | 0.458 / 0.272 | 0.39 | 0.31 | 0.86 | 0.50 | P |
| B2 | J4 | 0.34 / 0.21 | 0.476 / 0.366 | 0.489 / 0.303 | 0.40 | 0.39 | 0.83 | 0.60 | P |
| B3 | J1 | 0.34 / 0.38 | 0.504 / 0.546 | 0.533 / 0.508 | 0.53 | 0.42 | 0.44 | 0.45 | none |
| B3 | J2 | 0.60 / 0.44 | 0.542 / 0.472 | 0.422 / 0.254 | 0.36 | 0.23 | 0.85 | 0.65 | P |
| B3 | J3 | 0.56 / 0.41 | 0.525 / 0.448 | 0.415 / 0.243 | 0.35 | 0.22 | 0.88 | 0.50 | P |
| B3 | J4 | 0.58 / 0.42 | 0.540 / 0.470 | 0.450 / 0.273 | 0.37 | 0.30 | 0.84 | 0.60 | P |
| B4 | J1 | 0.66 / 0.61 | 0.585 / 0.599 | 0.517 / 0.402 | 0.46 | 0.34 | 0.65 | 0.45 | none |
| B4 | J2 | 0.84 / 0.72 | 0.629 / 0.616 | 0.379 / 0.222 | 0.33 | 0.17 | 0.87 | 0.65 | P,A |
| B4 | J3 | 0.83 / 0.71 | 0.621 / 0.610 | 0.368 / 0.211 | 0.31 | 0.15 | 0.89 | 0.50 | P,A |
| B4 | J4 | 0.84 / 0.73 | 0.628 / 0.621 | 0.407 / 0.240 | 0.33 | 0.22 | 0.86 | 0.60 | P |
## Legitimacy loop on
| Anchor | Arch. | K_Ω t10 / t100 | Y̅ 0–25 / 0–100 | VFAC t10 / t100 | G | A | P | Σ end | floors broken |
|---|---|---|---|---|---|---|---|---|---|
| B1 | J1 | 0.06 / 0.17 | 0.424 / 0.519 | 0.551 / 0.631 | 0.64 | 0.52 | 0.14 | 0.60 | none |
| B1 | J2 | 0.16 / 0.02 | 0.412 / 0.314 | 0.497 / 0.343 | 0.43 | 0.46 | 0.79 | 0.29 | P |
| B1 | J3 | 0.11 / 0.02 | 0.393 / 0.274 | 0.495 / 0.316 | 0.42 | 0.44 | 0.83 | 0.26 | P |
| B1 | J4 | 0.16 / 0.05 | 0.425 / 0.323 | 0.513 / 0.337 | 0.42 | 0.50 | 0.80 | 0.44 | P |
| B2 | J1 | 0.15 / 0.28 | 0.450 / 0.533 | 0.546 / 0.607 | 0.62 | 0.46 | 0.18 | 0.51 | none |
| B2 | J2 | 0.26 / 0.05 | 0.426 / 0.286 | 0.463 / 0.334 | 0.43 | 0.43 | 0.79 | 0.25 | P |
| B2 | J3 | 0.20 / 0.03 | 0.408 / 0.257 | 0.470 / 0.312 | 0.42 | 0.42 | 0.83 | 0.24 | P |
| B2 | J4 | 0.37 / 0.13 | 0.467 / 0.326 | 0.477 / 0.320 | 0.41 | 0.44 | 0.81 | 0.41 | P |
| B3 | J1 | 0.28 / 0.32 | 0.483 / 0.547 | 0.537 / 0.556 | 0.57 | 0.44 | 0.36 | 0.40 | none |
| B3 | J2 | 0.32 / 0.08 | 0.436 / 0.284 | 0.434 / 0.328 | 0.42 | 0.41 | 0.80 | 0.22 | P |
| B3 | J3 | 0.26 / 0.05 | 0.416 / 0.256 | 0.446 / 0.308 | 0.41 | 0.40 | 0.83 | 0.21 | P |
| B3 | J4 | 0.55 / 0.25 | 0.512 / 0.380 | 0.439 / 0.303 | 0.40 | 0.37 | 0.82 | 0.38 | P |
| B4 | J1 | 0.48 / 0.35 | 0.523 / 0.546 | 0.527 / 0.520 | 0.54 | 0.43 | 0.42 | 0.34 | none |
| B4 | J2 | 0.49 / 0.08 | 0.454 / 0.275 | 0.394 / 0.327 | 0.42 | 0.41 | 0.80 | 0.21 | P |
| B4 | J3 | 0.39 / 0.06 | 0.427 / 0.248 | 0.412 / 0.308 | 0.41 | 0.40 | 0.83 | 0.20 | P |
| B4 | J4 | 0.76 / 0.38 | 0.576 / 0.453 | 0.400 / 0.287 | 0.38 | 0.32 | 0.83 | 0.34 | P |
## Hypothesis test: J1 (lagging) − J2 (deregulated fast-adopter)
| Anchor | Loop | Δ Y̅ 0–25 | Δ Y̅ 0–100 | Δ VFAC t100 | Δ G | Δ A | Δ P | reading |
|---|---|---|---|---|---|---|---|---|
| B1 | off | +0.000 | +0.195 | +0.324 | +0.237 | +0.139 | -0.685 | lagging better on all three; J1 not poorer early |
| B1 | on | +0.012 | +0.205 | +0.288 | +0.211 | +0.063 | -0.647 | lagging better on all three; J1 not poorer early |
| B2 | off | -0.024 | +0.167 | +0.322 | +0.232 | +0.151 | -0.659 | lagging better on all three; J2 richer early |
| B2 | on | +0.024 | +0.247 | +0.273 | +0.197 | +0.027 | -0.617 | lagging better on all three; J1 not poorer early |
| B3 | off | -0.038 | +0.074 | +0.253 | +0.163 | +0.187 | -0.417 | lagging better on all three; J2 richer early |
| B3 | on | +0.047 | +0.263 | +0.228 | +0.148 | +0.026 | -0.437 | lagging better on all three; J1 not poorer early |
| B4 | off | -0.044 | -0.017 | +0.180 | +0.131 | +0.171 | -0.222 | lagging better on all three; J2 richer early |
| B4 | on | +0.069 | +0.271 | +0.193 | +0.118 | +0.022 | -0.378 | lagging better on all three; J1 not poorer early |
## Decomposition: speed (perm) vs. rights levers
Each lever regime run at slow (perm 0.6) and fast (perm 1.5) permissiveness, other levers
and initial Σ as in the archetype. Δ = slow − fast: the effect of lagging alone.
| Anchor | Loop | Regime | Δ Y̅ 0–25 | Δ Y̅ 0–100 | Δ VFAC t100 | Δ G | Δ A | Δ P |
|---|---|---|---|---|---|---|---|---|
| B1 | off | J1 levers | -0.031 | -0.017 | +0.033 | +0.029 | +0.083 | -0.045 |
| B1 | off | J2 levers | -0.022 | +0.072 | +0.059 | +0.030 | +0.036 | -0.059 |
| B1 | on | J1 levers | -0.026 | -0.014 | +0.023 | +0.019 | +0.058 | -0.034 |
| B1 | on | J2 levers | -0.012 | +0.074 | +0.044 | +0.020 | +0.005 | -0.047 |
| B2 | off | J1 levers | -0.071 | -0.016 | +0.132 | +0.120 | +0.062 | -0.327 |
| B2 | off | J2 levers | -0.055 | -0.044 | +0.032 | +0.024 | +0.095 | -0.019 |
| B2 | on | J1 levers | -0.045 | -0.013 | +0.068 | +0.070 | +0.016 | -0.207 |
| B2 | on | J2 levers | -0.014 | +0.028 | +0.009 | +0.005 | +0.022 | -0.004 |
| B3 | off | J1 levers | -0.087 | -0.050 | +0.104 | +0.066 | +0.077 | -0.212 |
| B3 | off | J2 levers | -0.082 | -0.128 | +0.042 | +0.037 | +0.109 | -0.024 |
| B3 | on | J1 levers | -0.053 | -0.002 | +0.046 | +0.037 | +0.022 | -0.079 |
| B3 | on | J2 levers | -0.013 | +0.005 | +0.008 | +0.004 | +0.030 | -0.003 |
| B4 | off | J1 levers | -0.074 | -0.080 | +0.038 | +0.019 | +0.061 | -0.058 |
| B4 | off | J2 levers | -0.100 | -0.210 | +0.054 | +0.055 | +0.117 | -0.031 |
| B4 | on | J1 levers | -0.064 | -0.022 | +0.042 | +0.023 | +0.046 | -0.087 |
| B4 | on | J2 levers | -0.026 | -0.001 | +0.008 | +0.005 | +0.032 | -0.003 |
## Best archetype by VFAC t100
| Anchor | loop off | loop on |
|---|---|---|
| B1 | J1 | J1 |
| B2 | J1 | J1 |
| B3 | J1 | J1 |
| B4 | J1 | J1 |

6
simulations/gadm02/test_gadm04.py
View file

@ -92,6 +92,12 @@ class TestGrounding(unittest.TestCase):
on = max(r["Ko"] for r in g.simulate({**p, "sent": 1.0}, i, T=30))
self.assertLess(on, off)
def test_permissiveness_scales_adoption(self):
self.assertEqual(g.PARAMS["perm"], 1.0)
ko = [g.simulate({"perm": v}, T=10)[-1]["Ko"] for v in (0.6, 1.0, 1.5)]
self.assertEqual(ko, sorted(ko))
self.assertLess(ko[0], ko[1])
if __name__ == "__main__":
unittest.main()

9
workplans/ASSEMBLY-WP-0009-adoption-band-jurisdictions.md
View file

@ -70,7 +70,7 @@ run, and sentiment ends negative everywhere.
```task
id: ASSEMBLY-WP-0009-T04
status: todo
status: done
priority: medium
state_hub_task_id: "863d6962-597f-5303-8e9b-675dfc8da5c3"
```
@ -81,6 +81,13 @@ example the EU AI Act, deregulation programmes). Test the owner's hypotheses:
under which band anchors and sentiment dynamics does lagging (J1) or
fast deregulated adoption (J2) do better or worse?
**Done 2026-10-13:** `gadm04` `perm` lever, `jurisdictions.py`,
`models/AdoptionBand.md` § 4 with a provisional mapping of EU, US, AR. J1 is
best on VFAC at every anchor. The gap comes mainly from the rights levers;
lagging by itself costs early output. J2's early lead needs sentiment that
stays positive. The open-economy gap (lineage import) is now the main
structural limitation for both hypotheses.
## Global sensitivity per band anchor
```task