# 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.