assembly-economics/models/AdoptionBand.md

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Created 2026-10-12 under ASSEMBLY-WP-0009-T01/T02. Implements
research/ScenarioBandMethod.md §§ 2 and 5 for GADM-0.4.
-->

# Adoption Band — Anchors and Probe Results

**Status:** v0.1. GADM-0.4 with grounded power, care and attention
parameters (`grounding.py`). The band anchors differ only in adoption
parameters. Each starts at its own equilibrium, with recalibrated channels.

## 1. Anchors (T01)

| Anchor | Adoption parameters | K_Ω at t 5 / 10 / 25 / 100 | Basis |
|---|---|---|---|
| **B1 conservative** | `aO` 0.10, `ell` 0.166 | 0.09 / 0.10 / 0.12 / 0.07 | Acemoglu (2024) |
| **B2 central** | `aO` 0.25, `ell` 0.415 | 0.20 / 0.27 / 0.28 / 0.16 | between LLM exposure and the tooling ceiling (Eloundou et al. 2023) |
| **B3 fast** | `aO` 0.50, `ell` 0.83 | 0.39 / 0.49 / 0.46 / 0.34 | tooling ceiling reached within a decade |
| **B4 aggressive** | B3 + self-enhancement `selfenh` 3.0 | 0.70 / 0.80 / 0.72 / 0.64 | AI accelerating its own development (Trammell & Korinek; Davidson et al.) |

**Self-enhancement loop (new in GADM-0.4, default off):** organism investment
and learning are scaled by \((1 + selfenh \cdot K_\Omega)\), so the more
capable organisms are, the faster they improve. `selfenh` = 3.0 is tuned so
B4 reaches about 0.8 within ten years. At `selfenh` ≥ 6, K_Ω saturates instead
of declining, which is a different trajectory regime. That variant is not an
anchor yet.

All anchors show a peak-and-decline path for K_Ω under baseline governance:
rising domination and eroding demand slow organism investment after the
first decade or two. This is a model property, to be checked against data
(ASSEMBLY-WP-0010).

## 2. Probe results (T02)

Full tables: `simulations/gadm02/results/band_probe.md`. 36 readings:
**22 robust, 7 quantitative shifts, 7 qualitative breaks.**

### 2.1 Robust across the whole band

- Generative regime best, automated capitalism worst, generative regime
  lowest domination.
- Generative regime gain over baseline: +0.29 to +0.34 VFAC.
- Commons-rich economy gain over baseline: +0.10 to +0.12.
- Organism lever order lineage > membrane > attention.
- P3: a wealth tax alone barely moves domination.
- Dominant domination channel in all non-generative regimes: lineage
  (membrane in the attention oligopoly).

### 2.2 Quantitative shifts

| Reading | B1 → B4 | Reading |
|---|---|---|
| Dividend abundance gain over baseline | +0.038 → +0.013 | dividends matter less as adoption accelerates |
| Automated capitalism loss vs. baseline | −0.099 → −0.047 | largest at B1, where the baseline itself does best |
| Lineage lever effect | +0.164 → +0.098 | the lever weakens as learning with use accelerates (P2: the critical lineage diffusion rises above the tested L = 0.8) |
| Membrane lever effect | +0.109 → +0.066 | as above |
| P3 effect size | −0.018 … −0.055 | small throughout |

### 2.3 Qualitative breaks

Recorded in `research/BreakRegister.md`.

1. **Regimes that bound domination only under conservative adoption.**
   Dividend abundance and the commons-rich economy keep P under its 0.7
   ceiling at B1, and breach it from B2 onward.
2. **Thresholds crossed with faster adoption.** Concentrated lineages breach
   the domination ceiling from B3. Attention extraction breaches the attention
   floor from B3, and the attention oligopoly breaches it at B4.
3. **Aggressive self-enhancement changes the structure of domination.** At
   B4, lineage control becomes the dominant domination channel even in the
   generative regime and its attention-extraction variant (B1–B3: market,
   or discovery).

### 2.4 Readings

1. **The program's core claims hold across the band.** What changes is
   *which policies are sufficient*: under conservative adoption, moderate
   reforms (dividends, commons) keep domination bounded. Under central to
   aggressive adoption, only the full generative package does.
2. **This bears directly on the "Europe lags" hypothesis.** A jurisdiction
   that adopts slowly (B1-like) can get by with moderate reforms. A fast
   adopter needs organism-specific governance early. Whether lagging is
   worse or better then depends on whether the slow adopter later imports
   B3/B4-speed organisms through foreign lineages. That needs open-economy
   structure the model does not have (ASSEMBLY-WP-0009-T04 / later).
3. **B4 needs lineage governance beyond the current lever range.** Under
   self-enhancement, even L = 0.8 and m = 0.8 leave lineage control dominant.
   Candidate mechanisms that GADM does not yet represent: public or commons
   lineages, compute commons, mandated lineage interoperability. This is a
   structural question, and the first candidate for "qualitatively different
   model" (break register entry BR-003).

## 3. Sentiment and legitimacy loop (T03)

### 3.1 Structure

`gadm04` gains two slow stocks, switched on by `sent = 1` (default off, so
all earlier results are unchanged):

- **Sentiment Σ (`Sig`)** moves at rate `sig_rate` (≈ 5 years) toward
  `0.5 + sig_gain × (pb − pb₀)`. Perceived net benefit
  `pb = 0.3 B + 0.3 G + 0.2 A + 0.2 (1 − P) − sig_disp × K_Ω (1 − m)` is judged
  against its value at the start: people compare with the situation they
  know.
- **Legislative stance `Lg`** follows Σ with lag `leg_lag` (5 years).

Σ and `Lg` scale the organism inflow (investment, learning, seeding) by
`(1 + 2 sig_adopt (Σ − 0.5)) (1 + 2 leg_perm (Lg − 0.5))`. A stance below
neutral also tightens membrane rights `m` and explainability `expl` by
`leg_m`, `leg_expl` × (0.5 − Lg). The parameters are uncalibrated; they set a
mechanism, not a measured strength.

Reproduce: `cd simulations/gadm02 && python3 -B legitimacy.py`
(`results/legitimacy.md`).

### 3.2 Results (baseline scenario, t = 100)

| Anchor | K_Ω peak off → on | K_Ω t 100 off → on | Σ end | VFAC off → on | loop behaviour |
|---|---|---|---|---|---|
| B1 | 0.12 → 0.10 | 0.07 → 0.06 | 0.40 | 0.396 → 0.480 | damping |
| B2 | 0.29 → 0.21 | 0.16 → 0.05 | 0.30 | 0.328 → 0.374 | damping |
| B3 | 0.50 → 0.35 | 0.34 → 0.08 | 0.26 | 0.298 → 0.357 | damping |
| B4 | 0.80 → 0.56 | 0.64 → 0.08 | 0.25 | 0.263 → 0.353 | oscillating (3 reversals ≥ 0.01) |

Variants: a stronger, faster response (gain 4, lag 2) also oscillates at B2
and B3. A slower legislature (lag 10) changes little, because Σ already
scales adoption directly. Starting sentiment matters only for the first
decade. At B4 a positive start (Σ = 0.7) gives a boom to 0.77 by year 5 and a
bust to 0.11 by year 25. A negative start (Σ = 0.3) gives a lower, later
peak (0.40 at year 10).

### 3.3 Readings

1. **The legitimacy loop is a brake on adoption throughout the band.** It
   lowers the K_Ω peak by 0.02 (B1) up to 0.24 (B4), and raises VFAC by
   0.05–0.09 through more membrane rights, more explainability and less
   attention extraction.
2. **At B4 the brake overshoots.** Fast self-enhancement drives sentiment
   down faster than legislation follows, and the system oscillates: boom,
   backlash, partial recovery. The loop does not stop the inner loop
   smoothly. It turns a fast rise into a boom-bust cycle (BR-005).
3. **The loop compresses the band.** With the loop, B2–B4 end at nearly the
   same K_Ω (0.05–0.08) and VFAC (0.35–0.37). The adoption anchor decides
   the first decade; legitimacy decides the long run. This holds only if
   sentiment really tracks perceived benefit, which is the main empirical
   question for the checkpoints (L6).
4. **Sentiment ends negative in every anchor, even B1.** Part of this is not
   about organisms: the baseline drifts toward higher domination P, and Σ
   reads that as lost benefit. The reading "AI adoption causes backlash" is
   therefore not separated from "rising domination causes backlash" in this
   model. A decomposition of `pb` per term belongs to the prediction register
   (T06), so that observed sentiment can tell the two apart.
5. **For the jurisdiction hypotheses (T04):** a positive starting sentiment
   (J2-like) buys early adoption but not a better end state. A negative start
   (J1-like) costs early adoption and ends at about the same place. Under
   this loop, "lagging" is not worse in the long run. This depends on the
   closed-economy assumption (§ 2.4, reading 2).

**Update 2026-10-13 (BR-009):** the loop as specified here relaxes Σ toward
the neutral 0.5. The uncalibrated results in this section keep that default.
Calibrated runs anchor Σ at its measured level (`sig_ref`,
`models/Calibration.md`), so measured low sentiment does not pull toward 0.5
and does not brake adoption by its level alone.

### 3.4 Limitations

- Σ is a single national mood. There is no polarization, no split by cohort
  and no media dynamics.
- Legislation responds symmetrically and without capture. Domination P
  does not weaken the legislative response, although the political channel
  `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 |

**Mapping update 2026-10-13 (review, L7):** the EU's drift toward higher
permissiveness is now law: the AI Omnibus (Reg. 2026/1744, in force 27 July
2026) delays Annex III high-risk duties to December 2027, while transparency
and GPAI enforcement started in August 2026. The EU stays J1 for rights
levers, with perm moving up. The US federal stance moved further toward J2
(EO 14365, December 2025: preemption of state AI laws), but no federal
statute exists, and state rights levers (California SB 53, Colorado) remain.
US: J3 market structure with a J2 federal stance, unchanged as a label but
stronger. Argentina: J2 confirmed. No AI law; the executive promotes
AI-operated companies ("Sociedad Automatizada" bill, May 2026). Sources:
`checkpoints/2026/2026-10-13-review.md`.

### 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.

## 5. Global sensitivity per band anchor (T05)

`gsa.py --model gadm04 --anchor Bn` runs Morris screening and Sobol indices
for final VFAC around each anchor (its parameters, equilibrium start and
recalibrated channel counters). The legitimacy-loop and permissiveness
parameters are held fixed. `gsa_band.py` compares the four runs
(`results/gsa_band.md`; per anchor `results/gsa_gadm04_B1..B4.md`). About 15
minutes per anchor with `--jobs 8`.

| Scenario | B1 | B2 | B3 | B4 |
|---|---|---|---|---|
| Baseline: top 3 by ST | `dO` 0.54, `aO` 0.15, `q0` 0.11 | `IX` 0.26, `dO` 0.17, `lX` 0.12 | `IX` 0.26, `dO` 0.21, `lX` 0.09 | `dO` 0.21, `IX` 0.21, `ell` 0.17 |
| Generative regime: top 3 by ST | `dO` 0.26, `m` 0.16, `r_lin` 0.16 | `q0` 0.24, `dO` 0.16, `IX` 0.13 | `expl` 0.21, `IX` 0.16, `q0` 0.13 | `expl` 0.27, `IX` 0.16, `std` 0.08 |

### Readings

1. **The dominant drivers change across the band.** At B1, organism
   parameters dominate: depreciation `dO` alone has ST 0.54. How fast AI
   capability depreciates matters most when there is little of it. From B2
   on, commons investment `IX` leads the baseline. At B4, learning `ell` and
   attention (`arec`, `x_att`) enter the top five.
2. **Under fast adoption, governance levers lead the generative regime.**
   Explainability `expl` is the largest driver at B3 and B4 (ST 0.21, 0.27).
   Membrane rights `m` rank first in Morris screening at every anchor.
   Lineage self-reinforcement `r_lin` matters only at B1–B2.
3. **Attention protection gains weight with speed.** `x_att` moves from
   outside the Morris top 15 (B1) to rank 5 (B4, baseline).
4. **Investment speed `aO` matters only at B1.** At faster anchors, adoption
   is limited by learning and depreciation, not by investment.
5. **Policy relevance.** The calibrated present lies between B2 and B3
   (`models/Calibration.md` § 3). In that range, the leading drivers are
   policy-relevant: commons investment `IX` and explainability `expl`. The
   most uncertain technical parameter is depreciation `dO`, which grounding
   (WP-0008) has narrowed to 0.15–0.35.

Caveat: these runs vary parameters around the *uncalibrated* anchors, with
domination starting at P ≈ 0.37. The calibrated start has P ≈ 0.6; GSA around
it is ASSEMBLY-WP-0015-T04.