assembly-economics/research/ScenarioBandMethod.md

Scenario Bands, Jurisdictions and Reality Checkpoints

Status: method v0.1. It governs how GADM results are produced and reported from ASSEMBLY-WP-0009 onward.

1. Premise

There is no historical precedent for AI adoption. Its speed comes from an inner self-enhancing loop (organisms learn with use, AI accelerates its own development, investment follows returns). Whether that loop accelerates, stalls or breaks depends on things outside economics proper:

• legislation: what may be deployed, who controls membranes, what must be explainable;
• adoption decisions: by firms, public bodies and individuals;
• sentiment: whether the change is framed and experienced as good or bad for society. Sentiment then feeds back into legislation and adoption.

These differ strongly between jurisdictions, and they change over time. Hence:

The program does not forecast one adoption path. It works with a band of adoption paths, probes every scenario across that band, checks which results hold across it, and compares the band with real data at regular checkpoints.

2. The adoption band

Four anchors, all expressed through K_Ω (the share of information tasks carried out by information organisms; 0.05 today):

Anchor	K_Ω at 10 years	Basis	Model
B1 conservative	≈ 0.10	Acemoglu (2024): about 4.6 % of tasks cost-effectively affected within 10 years	grounding.variant("slow")
B2 central	≈ 0.27	between LLM-only exposure (15 % of tasks faster) and the tooling ceiling (Eloundou et al. 2023)	grounding.variant("central")
B3 fast	≈ 0.50	exposure ceiling with tooling (47–56 %) reached within a decade	GADM-0.4 adoption defaults
B4 aggressive / self-enhancing	→ 0.8+ within 10–15 years	AI accelerating its own development: self-replicating and self-improving capital (Trammell & Korinek), automated AI research (Davidson et al.)	adds a self-enhancement loop: organism capability raises its own investment and learning rates

B1 and B2 rest on the evidence collected in ASSEMBLY-WP-0008. B3 and B4 are not "unlikely tails" to be discarded. B4 is the case where the inner loop dominates, and it is the case most likely to need a qualitatively different model.

3. Modulators: legislation and sentiment

Adoption is not exogenous. Two modulators act on the inner loop.

Legislation sets, per jurisdiction:

• deployment permissiveness: scales organism investment and seeding;
• membrane rights (m): user/community control, portability;
• explainability and provenance duties (expl);
• lineage rules (L, lin_open): open inheritance, diffusion;
• attention protection (x_att).

Sentiment Σ is a slow state variable. It moves toward perceived net benefit: broad claims, agency and attention headroom versus domination, displacement and loss of attention. Σ scales adoption (people and firms adopt what they trust) and, with a lag, shifts legislation (tightening when Σ falls, loosening when Σ rises).

This gives a legitimacy loop:

adoption → outcomes → sentiment → adoption and legislation → adoption

The loop can stabilize or destabilize the inner self-enhancing loop. It is a model hypothesis in its own right and must be checked against data.

4. Jurisdiction archetypes

Archetypes are defined by mechanism (parameter settings), not by country names. INTENT.md asks that assumptions be made explicit rather than hidden behind political labels. Real jurisdictions are mapped onto archetypes only with policy evidence, and the mapping is revisited at every checkpoint.

Archetype	Permissiveness	Membrane rights	Explainability	Lineage rules	Initial sentiment
J1 rights-first / precautionary	low	high	high	open	mixed
J2 deregulated fast-adopter	high	low	low	concentrated	positive
J3 platform-led	high	low (operator)	medium	strongly concentrated	mixed
J4 state-coordinated	high	low for users, state-held	medium	state-concentrated	managed

The owner's hypotheses to test (not model inputs): Europe will lag in adoption (J1-like), and Argentina may thrive (J2-like). The model should be able to say under which band anchor and which sentiment dynamics "lagging" is better or worse for VFAC, agency and attention, and the checkpoints should show which jurisdictions actually follow which archetype.

5. Probing scenarios across the band

Every reading is computed for every scenario × band anchor (× archetype, where relevant), and classified:

Class	Criterion	Consequence
robust	same sign, same ranking across the band	can be stated without qualification
quantitative shift	same sign and ranking; magnitude changes by more than 50 %	state with the band range
qualitative break	sign flip, ranking change, a different dominant channel, a floor violation appearing, or a tipping/hysteresis regime appearing in part of the band	flag: the model or the reading may need to be qualitatively different in that part of the band; record in a break register

Global sensitivity analysis is run per band anchor too: do the dominant drivers change across the band?

6. Reality checkpoints

Checkpoints compare the band with observed data, per jurisdiction where data allow.

Cadence:

• quarterly (light): adoption indicators only (AI use shares, AI investment, usage indices);
• annual (full): sensor bundles for all stocks (metrics/MetricsCatalog.md), sentiment surveys, legislation changes;
• event-driven: major legislation, major capability jumps, market shocks.

Prediction register. Before each checkpoint, the current model version writes down pre-registered predictions: indicator, jurisdiction, horizon, band range per anchor. At the checkpoint:

1. Record the observation and its source.
2. Locate it in the band. Which anchor does reality track, per jurisdiction?
3. Classify it: inside the band / outside the band (model failure).
4. Decide on model changes explicitly: parameter re-grounding through models/ParameterGrounding.md, or structural change through a workplan. Never re-tune silently. Earlier predictions stay in the register unchanged.

Over time this measures which band anchor each jurisdiction follows, whether the legitimacy loop behaves as modeled, and whether a qualitatively different model is needed for some jurisdictions.

7. How this changes ongoing work

• Every new model reading is reported across the band (at least B1, B2, B3, and B4 where implemented), with its robustness class.
• ASSEMBLY-WP-0007 (goods, ecology, selection) reports its tests across the band.
• ASSEMBLY-WP-0009 implements the band anchors, the self-enhancement loop, sentiment, legislation and archetypes.
• ASSEMBLY-WP-0010 runs the checkpoints and keeps the prediction register.