tegwick
509a50eb04
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assistant: claude-code Assistant-Model: opus Assistant-Process: 14913@bnt-lap001 Assistant-Session: add7ef69-b901-4756-9a67-34cf1125d96f
93 lines
4.6 KiB
Python
93 lines
4.6 KiB
Python
"""Two-good demand split: dematerialization and decoupling (ASSEMBLY-WP-0007-T04).
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Tests (workplan T04):
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1. Can VFAC rise while physical throughput falls, for reasons other than organism decline?
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2. Does the generative regime's sink breach move out of the 100-year horizon?
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Calibration check: throughput intensity per unit of output should fall by roughly the observed
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dematerialization (EU resource productivity +1.6 %/year, 2000-2022, Eurostat).
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Usage: python3 -B simulations/gadm02/demand.py
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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 band_probe as bp # noqa: E402
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import gadm05 as g5 # noqa: E402
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import grounding as gr # noqa: E402
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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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ANCHORS = list(gr.BAND)
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CASES = {"no demand split": {"dm_lam": 0.0}, "split (λ 0.05, ε 2)": {"dm_lam": 0.05, "dm_eps": 2.0},
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"split, strong (λ 0.1, ε 4)": {"dm_lam": 0.1, "dm_eps": 4.0}}
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def decoupled_windows(rows: list[dict], step: int = 1) -> float:
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"""Share of 10-year windows with VFAC up, throughput down and K_Omega not falling."""
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hits = n = 0
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for i in range(0, len(rows) - step, step):
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a, b = rows[i], rows[i + step]
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n += 1
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hits += b["VFAC"] > a["VFAC"] and b["thru"] < a["thru"] and b["Ko"] >= a["Ko"] - 1e-6
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return hits / n
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def intensity_trend(rows: list[dict], years: int = 10) -> float:
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"""Average yearly change of throughput per unit of output over the first `years`."""
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a, b = rows[0], rows[years // 10]
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ia, ib = a["thru"] / a["Ystar"], b["thru"] / b["Ystar"]
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return (ib / ia) ** (1 / years) - 1
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def main() -> None:
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OUT.mkdir(exist_ok=True)
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scs = load_all()
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var = {a: gr.variant(gr.BAND[a]) for a in ANCHORS}
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res = {}
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for sc in ("baseline", "generative_regime"):
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for a in ANCHORS:
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for cn, cd in CASES.items():
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rows = g5.simulate_eco({**scs[sc]["params"], **var[a][0]}, {**scs[sc]["init"], **var[a][1]},
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eco={"rm": "rest"}, demand=cd)
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sink = next((r["t"] for r in rows if r["Es"] < g5.ECO_PARAMS["eco_floor"]), None)
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res[(sc, a, cn)] = {"V": rows[-1]["VFAC"], "Cr": rows[-1]["Cr"], "dec": decoupled_windows(rows),
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"sink": sink, "Es": rows[-1]["Es"], "it": intensity_trend(rows),
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"thru": rows[-1]["thru"]}
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lines = ["# GADM-0.5-dev: two-good demand split (dematerialization)", "",
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"Generated by `simulations/gadm02/demand.py` (ASSEMBLY-WP-0007-T04). Ecological classes with",
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"material replenishment at rest. Decoupled windows: 10-year windows with VFAC rising, throughput",
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"falling and K_Ω not falling. Intensity trend: yearly change of throughput per unit of output,",
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"years 0–10 (observed EU dematerialization ≈ −1.6 %/year).", "",
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"| Scenario | Anchor | Case | VFAC t100 | rival demand share Cr t100 | intensity trend | decoupled windows | sinks breach | Es t100 |",
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"|---|---|---|---|---|---|---|---|---|"]
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for sc in ("baseline", "generative_regime"):
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for a in ANCHORS:
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for cn in CASES:
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r = res[(sc, a, cn)]
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lines.append(f"| {scs[sc]['title']} | {a} | {cn} | {r['V']:.3f} | {r['Cr']:.2f} | {100 * r['it']:+.1f} % | "
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f"{r['dec']:.2f} | {('t ' + format(r['sink'], '.0f')) if r['sink'] is not None else '—'} | {r['Es']:.2f} |")
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readings = []
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for sc in ("baseline", "generative_regime"):
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t = scs[sc]["title"]
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readings.append((f"decoupled windows, split − none: {t}",
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[res[(sc, a, 'split (λ 0.05, ε 2)')]["dec"] - res[(sc, a, 'no demand split')]["dec"] for a in ANCHORS]))
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readings.append((f"ΔVFAC split − none: {t}",
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[res[(sc, a, 'split (λ 0.05, ε 2)')]["V"] - res[(sc, a, 'no demand split')]["V"] for a in ANCHORS]))
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readings.append((f"ΔEs t100 split − none: {t}",
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[res[(sc, a, 'split (λ 0.05, ε 2)')]["Es"] - res[(sc, a, 'no demand split')]["Es"] for a in ANCHORS]))
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lines += ["", "## Readings and robustness class", "", "| Reading | " + " | ".join(ANCHORS) + " | class |",
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"|---|" + "---|" * (len(ANCHORS) + 1)]
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for name, vals in readings:
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lines.append(f"| {name} | " + " | ".join(f"{v:+.3f}" for v in vals) + f" | {bp.classify(vals, 'sign')} |")
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lines.append("")
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(OUT / "demand.md").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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