tegwick
c03cf98756
- gadm04.py: cohort capability split into durable (education-formed, slow depreciation, birth dilution) and current components; learning begets learning; starts identical to GADM-0.3 (tested) - L3 now supported: education reaches working-age capability with a 17-year half-time and 3x the effect; L1 still holds, more weakly - life_cycle.py selectable per model; GADM-0.3 results unchanged 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
85 lines
4 KiB
Python
85 lines
4 KiB
Python
"""Life-cycle hypotheses L1-L3 for GADM-0.3 with cohorts (gadm03c).
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L1: strong intergenerational transfer T_g lets an ageing society keep H.
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L2: care demand competes with attention; without recognized care participation
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(C_p), attention headroom falls fastest in the working-age cohort.
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L3: children's capability formation reaches working-age capability with a lag
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of 15-20 years.
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Usage: python3 -B simulations/gadm02/life_cycle.py [--model gadm04]
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(default model: gadm03c, the GADM-0.3 cohort model)
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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sys.dont_write_bytecode = True
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import importlib # noqa: E402
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MODEL = sys.argv[sys.argv.index("--model") + 1] if "--model" in sys.argv else "gadm03c"
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c = importlib.import_module(MODEL)
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from scenarios import load_all # noqa: E402
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OUT = Path(__file__).resolve().parent / "results"
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STABLE, AGEING = 1 / 45, 0.75 / 45
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def run(params: dict, base: dict) -> list[dict]:
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return c.simulate({**base["params"], **params}, base["init"])
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def main() -> None:
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OUT.mkdir(exist_ok=True)
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sc = load_all()["baseline"]
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lines = [f"# {c.MODEL_VERSION}: life-cycle hypotheses", "",
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"Generated by `simulations/gadm02/life_cycle.py`. Baseline background,",
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"t = 100 years. Ageing demography: births at 75 % of replacement.", ""]
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lines += ["## L1: intergenerational transfer in an ageing society", "",
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"| Demography | T_g (tg) | seniors + very old (share, t=100) | H (t=0) | H (t=100) | ΔH |",
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"|---|---|---|---|---|---|"]
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for demo, b in (("stable", STABLE), ("ageing", AGEING)):
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for tg in (0.0, 0.05, 0.15):
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r = run({"birth": b, "tg": tg}, sc)
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old = r[-1]["n_s"] + r[-1]["n_d"]
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lines.append(f"| {demo} | {tg:.2f} | {old:.2f} | {r[0]['H']:.3f} | {r[-1]['H']:.3f} | "
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f"{r[-1]['H'] - r[0]['H']:+.3f} |")
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lines += ["", "## L2: care load and attention", "",
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"Levels at t = 100 are compared across settings (not against t = 0, because",
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"initial cohort attention is not at its own equilibrium).", "",
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"| Demography | care_sen (seniors' care share) | cp | A working age | A seniors | care load per working-age person | per senior |",
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"|---|---|---|---|---|---|---|"]
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for demo, b in (("stable", STABLE), ("ageing", AGEING)):
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for cs in (0.1, 0.3):
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for cp in (0.0, 0.15):
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prm = {"birth": b, "cp": cp, "care_sen": cs}
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r = run(prm, sc)[-1]
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pp = {**c.PARAMS, **sc["params"], **prm}
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load = pp["care_load"] * r["care_need"] * max(0.0, 1 - pp["care_relief"] * cp)
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lines.append(f"| {demo} | {cs:.1f} | {cp:.2f} | {r['Aw']:.3f} | {r['As']:.3f} | "
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f"{load * (1 - cs) / r['n_w']:.4f} | {load * cs / r['n_s']:.4f} |")
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lines += ["", "## L3: lag from education to working-age capability", "",
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"Run difference: education intensity 1.5 vs. 1.0, everything else equal.", "",
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"| t | ΔH children | ΔH working age | ΔH aggregate |", "|---|---|---|---|"]
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lo, hi = run({"edu": 1.0}, sc), run({"edu": 1.5}, sc)
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diffs = [(a["t"], b["Hch"] - a["Hch"], b["Hw"] - a["Hw"], b["H"] - a["H"]) for a, b in zip(lo, hi)]
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for t, dc, dw, dh in diffs:
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if t in (0, 5, 10, 15, 20, 25, 30, 40, 50, 75, 100):
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lines.append(f"| {t:.0f} | {dc:+.3f} | {dw:+.3f} | {dh:+.3f} |")
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final_w, final_c = diffs[-1][2], diffs[-1][1]
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t_w = next(t for t, _dc, dw, _dh in diffs if dw >= 0.5 * final_w)
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t_c = next(t for t, dc, _dw, _dh in diffs if dc >= 0.5 * final_c)
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lines += ["", f"Half of the final effect is reached after {t_c:.0f} years for children and "
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f"{t_w:.0f} years for working-age capability.", ""]
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name = "life_cycle.md" if MODEL == "gadm03c" else f"life_cycle_{MODEL}.md"
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(OUT / name).write_text("\n".join(lines))
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print("\n".join(lines))
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if __name__ == "__main__":
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main()
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