assembly-economics/models/AdoptionBand.md

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).

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.