assembly-economics/models/GADM-0.3.md

Generative Assembly Dynamics Model — GADM-0.3

Status: in progress, exploratory. Implemented so far: power channels (§ 2, ASSEMBLY-WP-0005-T03), wealth separated from power (§ 3, T04), attention recalibration and the organism grid (§ 4, T05). Planned: cohorts (T06).

GADM-0.3 builds on GADM-0.2 (GADM-0.2.md). Everything not described here is unchanged; the implementation reuses gadm02.py. Time unit: one year.

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1. Why channels

GADM-0.2's sensitivity analysis showed that the membrane-control and lineage-diffusion levers dominate outcomes. A single P stock cannot show which channel drives domination, or whether channels behave differently. GADM-0.3-Candidates.md § 2.2 proposed splitting P into channel sub-stocks.

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2. Power channels (T03)

2.1 Channels

Code	Channel	Driver d_k	Counter C_k
Pm	market	p₁ V_sel (1 − B): private returns of success	B + N: broad claims and entry
Pi	infrastructure	p_i K (1 − std): capacity under closed standards	X · std + 0.1: interoperable commons
Pd	discovery / attention	p₂ disc (0.5 + 0.5 K_Ω)	(1 − disc) Q + 0.1: plural discovery
Pmem	membrane	p₃ K_Ω (1 − m)	m + 0.1: user/community control
Plin	lineage	p₄ K_Ω (1 − L)	L + 0.1: lineage diffusion
Ppol	political / institutional	p_pol · P_econ · (1 − B)	Q S: checks

P_econ aggregates the five economic channels. The political channel converts economic control into political influence.

2.2 Dynamics

[ \dot P_k = (d_k + r_k P_k^2)(1-P_k) - c_k C_k P_k ]

The superlinear term r_k P_k^2 represents increasing returns to control. It allows thresholds: with drivers held fixed, a channel can have a low and a high stable equilibrium separated by an unstable one.

For the lineage channel, self-reinforcement grows with learning with use (loop R7): r_{lin} = r(1 + lw \cdot K_\Omega Q).

2.3 Aggregation

[ P = 1 - \prod_k (1-P_k)^{1/6} ]

One fully dominant channel gives P = 1, and equal channels give P equal to that common value.

Rejected alternative: the plain product 1-\prod_k(1-P_k) compounds moderate channels (six channels at 0.5 give P = 0.98) and drove P to 1 in almost every scenario.

2.4 Calibration

• Initial channels: market 0.50, infrastructure 0.45, discovery 0.50, membrane 0.05, lineage 0.05, political 0.50 (total P ≈ 0.37; GADM-0.2: 0.40). Membrane and lineage start low because organism capability starts at 0.05.
• r_k = 0.3 for all channels (0.4 for discovery). This is a uniform assumption, not an estimate.
• The counter coefficients c_k are computed by calibrate() so that every channel starts in equilibrium in the baseline.

A calibration pitfall, found and guarded against. The first calibration put the market, membrane and lineage channels exactly on their unstable root, so small drift decided which way each went. A channel's starting equilibrium is stable when P_0 \ge 0.5 or r < d/(P_0^2(1-2P_0)). The initial values above satisfy this, and a unit test now checks that every calibrated channel starts at a stable equilibrium.

2.5 Scenario results

Final values at t = 100 years:

Scenario	VFAC (0.2 → 0.3)	P (0.2 → 0.3)	market	infra	discovery	membrane	lineage	political
Baseline	0.461 → 0.345	0.52 → 0.79	0.80	0.74	0.69	0.79	0.84	0.83
Automated capitalism	0.317 → 0.248	0.73 → 0.89	0.90	0.78	0.81	0.91	0.93	0.92
Dividend abundance	0.445 → 0.338	0.54 → 0.79	0.76	0.76	0.70	0.80	0.86	0.82
Universal venture access	0.487 → 0.371	0.52 → 0.78	0.79	0.74	0.69	0.79	0.84	0.80
Commons-rich	0.544 → 0.466	0.48 → 0.70	0.73	0.05	0.61	0.79	0.86	0.75
Attention oligopoly	0.372 → 0.296	0.69 → 0.86	0.81	0.76	0.91	0.91	0.83	0.87
Generative regime	0.650 → 0.692	0.31 → 0.14	0.39	0.05	0.08	0.09	0.14	0.04
— concentrated lineages	0.530 → 0.452	0.50 → 0.70	0.68	0.08	0.08	0.86	0.95	0.60
— attention extraction	0.575 → 0.599	0.41 → 0.32	0.27	0.06	0.80	0.08	0.12	0.11

2.6 Readings

These are model behaviour under assumed structure, not claims about economies.

1. Domination polarizes. With channels and increasing returns, every non-generative regime ends with P between 0.70 and 0.89 (GADM-0.2: 0.48–0.73), while the generative regime falls to 0.14. The VFAC spread widens accordingly. This sharpening comes directly from the assumed self-reinforcement r.
2. Each regime defeats only the channels it targets. The commons-rich regime removes infrastructure control (0.74 → 0.05) but leaves the other five channels high. Dividends move none of them. Only the generative regime, which acts on several levers at once, keeps all channels low.
3. Organism channels spill into politics. In the concentrated-lineages variant, membrane and lineage control (0.86, 0.95) raise political control to 0.60, even though market, infrastructure and discovery channels stay low.
4. Single-channel domination is visible, not hidden. In the attention-extraction variant, discovery control reaches 0.80 while total P is only 0.32. The channel view shows a concentration that the aggregate understates. Report channels, not just P.

2.7 Hypotheses P1 and P2

Full tables: simulations/gadm02/results/power_channels.md.

P1: channels become self-sustaining above a threshold. Mostly not supported over 100 years. Sweeping each channel's initial value, the final value is the same for most channels, whatever the start: drifting drivers dominate. There are two exceptions:

• Lineage, generative regime: hysteresis. If lineage control starts above about 0.25–0.30, it locks in at 0.68, despite high lineage diffusion (L = 0.8). Below that, it stays at about 0.15.
• Market, baseline: only a market that starts near zero stays low (0.22). Any start above 0.15 ends at about 0.8.

P2: membrane/lineage control plus learning with use forms a natural-monopoly regime unless L exceeds a critical value. Supported. The critical L (final lineage control below 0.5) rises with learning amplification lw:

lw	Baseline	Generative regime
0	0.50	0.60
2 (default)	0.60	0.80
4	0.70	0.85

2.8 Implications for the research program

• Timing matters (WS-12, transition dynamics). The lineage hysteresis means that lineage governance has to come before concentration. A generative regime introduced after lineages concentrate does not undo it in this model.
• Stronger organism learning raises the governance bar. The more organisms learn from use, the more lineage diffusion is needed (P2). This ties WS-13 (organism lens) to WS-07 (power).
• Report channels, not just P (reading 4). Added to the metrics catalog as a requirement for the domination bundle.

2.9 Limitations

• r_k is uniform and assumed. The channel counters include ad-hoc floors (+0.1) so that no counter vanishes entirely.
• The baseline market channel starts in its high basin (start 0.50, threshold about 0.42). The initial split across channels is a modeling choice.
• Thresholds were computed with other stocks moving. Frozen-state roots (channel_roots()) differ from long-run behaviour.
• Python pitfall seen during development: a stale bytecode cache can mask quick same-size edits. Use python3 -B after rapid edits.

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3. Wealth separated from power (T04)

3.1 Wealth stock

[ \dot W = a_w (1-B),Y^*,(1-W) - (d_w + t_w,wtax),W ]

W is wealth concentration: it accumulates from privately captured returns (the part of output not reaching the broad population, 1 − B) and dissipates through d_w (inheritance splitting, failure, competition) and a wealth tax wtax ∈ [0, 1]. Initial W = 0.5.

3.2 Conversion into power

Wealth converts into the market and political channels only:

[ d_m \mathrel{+}= \kappa W,\qquad d_{pol} \mathrel{+}= \kappa W,\qquad \kappa = k_w (1 - Q S) ]

Epistemic capacity and cohesion (checks) reduce conversion. Membrane, lineage, infrastructure and discovery control do not depend on wealth: they come from organism ownership, capacity and discovery structure. This is the "wealth is not power" separation that GADM-0.1 § 13 asks for.

W is not a VFAC factor. Inequality of wealth matters in this model only through what it converts into.

Setting wealth_on = 0 (helper gadm03.t03_params()) reproduces the T03 model exactly. This was verified: power_channels.py output is identical before and after T04.

3.3 Hypothesis P3: supported

P3: wealth taxation alone does not bound P when the membrane channel dominates. Full tables: simulations/gadm02/results/wealth.md.

Setting	W (wtax 0 → 1)	market	political	membrane	P
Membrane/lineage-dominated	0.37 → 0.12	0.62 → 0.44	0.54 → 0.38	0.86 → 0.86	0.68 → 0.64
Baseline	0.37 → 0.12	0.77 → 0.75	0.80 → 0.79	0.79 → 0.79	0.78 → 0.77
Automated capitalism	0.51 → 0.19	0.89 → 0.88	0.91 → 0.90	0.91 → 0.91	0.88 → 0.88

Robustness to the conversion strength (calibration recomputed for each value):

k_w	Setting	P at wtax 0 → 1	market at wtax 0 → 1
0.04 (default)	baseline	0.78 → 0.77	0.77 → 0.75
0.20	baseline	0.75 → 0.71	0.68 → 0.45
0.04 (default)	membrane/lineage-dominated	0.68 → 0.64	0.62 → 0.44
0.20	membrane/lineage-dominated	0.66 → 0.61	0.51 → 0.21

3.4 Readings

1. P3 holds and is robust. A full wealth tax lowers total domination by only 0.01–0.05 across settings and conversion strengths, because membrane and lineage control do not run on wealth. Bounding organism-era domination needs organism-specific levers (membrane control m, lineage diffusion L), consistent with the GADM-0.2 sensitivity results.
2. Wealth taxation does reduce the channels it reaches, but how much depends on the conversion strength. At the default k_w, channels already locked in their high state (baseline, automated capitalism) barely respond. At five times the conversion, the tax pulls the market channel down substantially even at baseline. The earlier statement "taxing wealth cannot unlock entrenched channels" therefore depends on k_w and is not claimed.
3. Adding wealth changes the scenario results only slightly (P −0.00 to −0.03 against the T03 stage). Part of this is a calibration effect: the counters were calibrated at W = 0.5, while W settles at about 0.25, so the wealth driver weakens over time. The within-model tax sweep is not affected by this.

3.5 Limitations

• W is not initialised at its own equilibrium (see reading 3).
• k_w is assumed; reading 2 shows a qualitative dependence on it.
• Wealth does not buy discovery or infrastructure control in this model, although it plausibly could (through acquisitions). This is a deliberate simplification to make P3 testable, and it biases the result toward P3.

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4. Attention recalibration and the organism grid (T05)

4.1 Attention extraction against time-use evidence

GADM-0.2 flagged attention dynamics as too weak: under its aext = 0.12, baseline attention headroom A rose from 0.50 to 0.65.

Evidence used as the calibration anchor:

• American Time Use Survey 2023: about 5.2 hours of leisure per day, of which TV takes about 2.7 hours, over half (BLS).
• Social media: roughly 2 h 20 min to 2 h 25 min per day for the average internet user worldwide (DataReportal/GWI, Statista).

Together these suggest that engagement-funded media already claim around half of discretionary time. Target: baseline A ≈ 0.45–0.5 (current state), falling to about 0.3 under an extraction regime.

Solving the A equilibrium for these targets gives aext = 0.275:

Scenario	A (GADM-0.2 aext)	A (recalibrated)
Baseline	0.69	0.49
Attention oligopoly	0.57	0.37
Generative regime	0.77	0.60
Generative, attention extraction	0.44	0.26

VFAC falls by 0.015–0.04 across scenarios. P and the scenario ordering are unchanged. This is an order-of-magnitude anchor, not an estimate: the mapping from hours of engagement media to "unclaimed attention" is itself an assumption (see the Goodhart note in metrics/MetricsCatalog.md, A).

The T03 and T04 stages remain reproducible (t03_params(), t04_params()), verified by identical output.

4.2 The 2 × 2 × 2 organism grid

Axes (from InformationOrganismsInGADM.md § 5): lineage ownership (concentrated: L = lin_open = 0.1 / diffuse: 0.8), membrane control (operator: m = 0.2 / user-community: 0.8), attention regime (extraction: x_att = 0.9 / protected: 0.2). Each grid is run on the baseline and the generative background. Full tables: simulations/gadm02/results/organism_grid.md.

Factorial effects (average change from the first to the second level):

Effect	VFAC, baseline bg.	VFAC, generative bg.	P, baseline bg.	P, generative bg.
lineage diffuse	+0.130	+0.127	−0.242	−0.340
membrane user-controlled	+0.090	+0.089	−0.175	−0.216
attention protected	+0.046	+0.060	−0.009	+0.002
lineage × membrane	+0.040	−0.005	−0.093	−0.032
other interactions	≤ 0.011	≤ 0.006	≤ 0.006	≤ 0.005

Consistency check: the (diffuse, user, protected) corner on the generative background reproduces the generative regime exactly (VFAC 0.672).

4.3 Readings

1. Lineage ownership is the strongest organism lever, ahead of membrane control, on both backgrounds. This agrees with the GADM-0.2 Sobol results only partly: there, m ranked above L. In the channel model, lineage concentration feeds a self-reinforcing channel with learning with use (P2), which strengthens its effect.
2. Lineage and membrane governance are complements where governance is otherwise weak. On the baseline background, doing both is worth +0.04 VFAC beyond the sum of the parts. On the generative background, the interaction vanishes.
3. Attention protection improves VFAC but not power. It acts through attention headroom A, not through domination. In this model the attention regime and discovery concentration are separate levers. The attention-oligopoly scenario sets both.
4. Better organism governance increases attention pressure unless attention is protected. Under extraction, A is lowest in the best-governed corner (0.29 versus 0.36 at baseline), because better-governed organisms grow larger, and extraction scales with K_Ω. Organism governance and attention protection have to go together.
5. Organism governance alone does not reach the generative regime. On the baseline background, the best organism corner (VFAC 0.560) closes about 70 % of the gap to the full generative regime (0.672). The discovery channel stays at about 0.6, because it depends on discovery concentration, which is not an organism lever.

Reproduce

python3 -B -m unittest discover -s simulations/gadm02
cd simulations/gadm02 && python3 -B power_channels.py   # T03 stage (wealth off)
cd simulations/gadm02 && python3 -B wealth.py           # T04: P3 and scenarios
cd simulations/gadm02 && python3 -B organism_grid.py    # T05: 2 × 2 × 2 organism grid