ASSEMBLY-WP-0005-T06: life-cycle cohorts; finish WP-0005; add WP-0006

- gadm03c.py: four cohorts with demography, cohort capability, mentoring,
  care load on attention; stationary at replacement fertility (tested)
- life_cycle.py: L1 supported after fixing an inert mentoring term; L2
  conditional on who carries care per person, care relief via participation
  claims protects carers' attention; L3 not supported (H lacks a durable
  component)
- GADM-0.3.md complete; ASSEMBLY-WP-0006 (GADM-0.4, proposed residual)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Assistant: claude-code
Assistant-Model: opus
Assistant-Process: 1494246@bnt-lap001
Assistant-Session: caf6de6f-f9e4-47ce-a584-49baa271fa4b

models/GADM-0.3.md

@@ -6,10 +6,10 @@ the code is the reference if the two ever disagree.
 
 # 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).
+**Status:** complete for ASSEMBLY-WP-0005, exploratory. Power channels
+(§ 2, T03), wealth separated from power (§ 3, T04), attention recalibration
+and organism grid (§ 4, T05), life-cycle cohorts (§ 5, T06). Open items are
+in § 6 and ASSEMBLY-WP-0006.
 
 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.
@@ -343,6 +343,106 @@ background reproduces the generative regime exactly (VFAC 0.672).
    stays at about 0.6, because it depends on discovery concentration, which
    is not an organism lever.
 
+---
+
+## 5. Life-cycle cohorts (T06)
+
+Implemented in `simulations/gadm02/gadm03c.py` on top of the full GADM-0.3.
+
+### 5.1 Structure
+
+| Cohort | Ages | Carries | Notes |
+|---|---|---|---|
+| children `ch` | 0–19 | H | education, adult attention as care input, seniors' mentoring |
+| working age `w` | 20–64 | H, A | GADM-0.2 learning; deskilling; mentoring inflow T_g; main care provider |
+| seniors `s` | 65–79 | H, A | half learning rate, 1.5× depreciation; provides a share of care |
+| very old `d` | 80+ | H | 3× depreciation; main care need |
+
+Population counts follow births (`birth` × N_w) and ageing rates of 1/20,
+1/45 and 1/15 per year, with deaths of the very old at 1/8 per year. At
+`birth` = 1/45 (replacement), the age structure is stationary at about
+23 / 51 / 17 / 9 %. A unit test checks this.
+
+Aggregates passed to the rest of the model: H is population-weighted over all
+cohorts; A is weighted over working age and seniors. G remains a single
+aggregate.
+
+Care need = 0.5 × share of children + 1.0 × share of very old. Its attention
+cost is split between working age and seniors (`care_sen`), and reduced by
+participation claims: relief = 1 − 3 × cp.
+
+### 5.2 Effect on the scenarios
+
+With cohorts, the baseline VFAC is 0.323 (0.334 without cohorts) and the
+generative regime 0.656 (0.672). Capability and attention are a little lower,
+because of senior depreciation and care load. The scenario ordering is
+unchanged.
+
+### 5.3 Hypotheses L1–L3
+
+Full tables: `simulations/gadm02/results/life_cycle.md`.
+
+**L1: strong intergenerational transfer lets an ageing society keep H.
+Supported.**
+
+| Demography | T_g = 0 | T_g = 0.05 | T_g = 0.15 |
+|---|---|---|---|
+| stable (ΔH over 100 years) | −0.043 | −0.032 | −0.011 |
+| ageing, births at 75 % of replacement | −0.053 | −0.040 | −0.018 |
+
+An ageing society with strong mentoring loses less capability than a stable
+society without it. A first formulation of T_g, which transferred only
+max(0, H_s − H_w), was inert because senior capability decays below
+working-age capability. It was replaced by an inflow proportional to senior
+capability and working-age headroom.
+
+**L2: without recognized care participation, A falls fastest in the
+working-age cohort. Conditionally supported.** Attention is lowest in
+whichever cohort carries the higher care load *per person*:
+
+- with seniors providing 10 % of care, working-age A is lowest (0.40 vs. 0.44);
+- with 30 %, senior A is lowest (0.40 vs. 0.42), because there are fewer
+  seniors to share it.
+
+Care relief through participation claims (cp = 0.15) raises working-age
+attention by 0.034–0.042 in every setting. So the post-work participation
+channel directly protects the attention of carers.
+
+**L3: education reaches working-age capability with a 15–20 year lag. Not
+supported in this model.** Raising education intensity by 50 % raises
+children's capability by +0.067, but working-age capability only by +0.010,
+with half of the effect reached after about 9 years. The cause is structural:
+working-age capability relaxes to its own conditions within roughly 8 years
+(adult learning and depreciation rates). That washes out the cohort inflow,
+so H has almost no memory of childhood. Human-capital research generally
+finds persistent effects of early education, so this points to a missing
+mechanism, not an empirical result. The fix belongs in GADM-0.4: split H
+into a durable, education-formed component and a current component.
+
+### 5.4 Further observations
+
+- **Ageing is slow in this demography.** At 75 % of replacement fertility,
+  the share of seniors and very old rises only from 0.26 to 0.29 in 100 years,
+  and total care need barely changes (fewer children offset more very old at
+  the chosen weights).
+- **Aggregate H declines at baseline even with stationary demography**
+  (−0.043), because of senior and very-old depreciation, which the
+  single-population model did not have.
+
+---
+
+## 6. Open items (handed to ASSEMBLY-WP-0006)
+
+- Split H into durable and current components (L3).
+- Agency G per cohort.
+- Initialise W and the cohort attention stocks at their own equilibria (§ 3.4
+  reading 3, § 5.3 L2 note).
+- Estimates instead of assumptions for the channel self-reinforcement r_k, the
+  wealth conversion k_w, and organism depreciation `dO` (the top driver in the
+  GADM-0.2 Sobol analysis).
+- Global sensitivity analysis on GADM-0.3 (the Sobol run so far covers
+  GADM-0.2).
+
 ## Reproduce
 
 ```bash
@@ -350,4 +450,5 @@ 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
+cd simulations/gadm02 && python3 -B life_cycle.py       # T06: cohorts, L1–L3
 ```

models/README.md

@@ -11,7 +11,7 @@ exploratory: they generate hypotheses and are not established results.
 | [InformationOrganismsInGADM.md](InformationOrganismsInGADM.md) | mapping note v0.1 | ASSEMBLY-WP-0003-T03; input for GADM-0.2 (ASSEMBLY-WP-0004) |
 | [GADM-CLD.md](GADM-CLD.md) | v0.1 | causal-loop diagram, loops, signed link table (ASSEMBLY-WP-0004-T01/T02) |
 | [GADM-0.2.md](GADM-0.2.md) | GADM-0.2 | extensions decision, equations, scenario and sensitivity findings (ASSEMBLY-WP-0004) |
-| [GADM-0.3.md](GADM-0.3.md) | in progress | power channels (P1/P2) wealth separated from power (P3), attention recalibration and organism grid (ASSEMBLY-WP-0005-T03–T05) |
+| [GADM-0.3.md](GADM-0.3.md) | GADM-0.3 | power channels (P1/P2) wealth separated from power (P3), attention recalibration, organism grid, life-cycle cohorts (ASSEMBLY-WP-0005) |
 | [GADM-0.3-Candidates.md](GADM-0.3-Candidates.md) | outline | life-cycle and power extensions, deferred items (ASSEMBLY-WP-0004-T08) |
 
 ## Conventions