tegwick
ee7b17da5e
- dO grounded at 0.20/yr (software, R&D capital, AI hardware evidence); investment/learning rescaled so the baseline organism path is unchanged - robustness.py: key readings hold for grounded dO and brackets on channel self-reinforcement and wealth conversion; P3 effect size grows at strong self-reinforcement - models/ParameterGrounding.md: register of grounded / bracketed / assumed / calibrated parameters, open grounding needs 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
91 lines
4 KiB
Python
91 lines
4 KiB
Python
"""Robustness of key GADM readings to grounded and bracketed parameters (T02).
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Variants: GADM-0.4 with the old organism depreciation (dO = 0.06), with the
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grounded value (dO = 0.20, rescaled), and the grounded value with brackets on
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the ungrounded channel self-reinforcement r and wealth conversion k_w. Channel
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counters are recalibrated for every variant so each starts in equilibrium.
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Usage: python3 -B simulations/gadm02/robustness.py
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"""
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from __future__ import annotations
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import itertools
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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 gadm03 as g3 # noqa: E402
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import gadm04 as g # 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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R_KEYS = ("r_m", "r_i", "r_mem", "r_lin", "r_pol")
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OLD_DO = {"dO": 0.06, "aO": 0.15, "ell": 0.25, "oseed": 0.01}
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def variant(extra: dict) -> dict:
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return {**extra, **g3.calibrate(extra)}
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VARIANTS = [
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("old dO = 0.06", variant(OLD_DO)),
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("grounded dO = 0.20", variant({})),
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("grounded, r = 0.15", variant({**{k: 0.15 for k in R_KEYS}, "r_d": 0.2})),
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("grounded, r = 0.45", variant({**{k: 0.45 for k in R_KEYS}, "r_d": 0.6})),
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("grounded, k_w = 0.02", variant({"k_w": 0.02})),
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("grounded, k_w = 0.20", variant({"k_w": 0.20})),
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]
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GRID = {"lineage": ({"L": 0.1, "lin_open": 0.1}, {"L": 0.8, "lin_open": 0.8}),
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"membrane": ({"m": 0.2}, {"m": 0.8}),
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"attention": ({"x_att": 0.9}, {"x_att": 0.2})}
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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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names = list(scs)
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ref_rank = None
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lines = [f"# {g.MODEL_VERSION}: robustness of key readings", "",
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"Generated by `simulations/gadm02/robustness.py`. t = 100 years.",
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"Readings checked: (1) scenario VFAC ranking (Spearman ρ against the grounded",
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"variant), (2) generative regime lowest P, (3) organism lever order on the",
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"baseline background, (4) P3: full wealth tax lowers P by < 0.06 in the",
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"membrane/lineage-dominated case.", "",
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"| Variant | best scenario | worst scenario | ρ ranking | generative P | lineage / membrane / attention effect | lever order holds | P3 ΔP | P3 holds |",
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"|---|---|---|---|---|---|---|---|---|"]
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rows = []
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for label, prm in VARIANTS:
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res = {n: g.simulate({**scs[n]["params"], **prm}, scs[n]["init"])[-1] for n in names}
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order = sorted(names, key=lambda n: -res[n]["VFAC"])
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rank = {n: i for i, n in enumerate(order)}
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rows.append((label, prm, res, order, rank))
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ref_rank = rows[1][4]
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k = len(names)
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for label, prm, res, order, rank in rows:
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rho = 1 - 6 * sum((rank[n] - ref_rank[n]) ** 2 for n in names) / (k * (k * k - 1))
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gen_lowest = min(names, key=lambda n: res[n]["P"]) == "generative_regime"
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base = scs["baseline"]
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out = {}
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for lv in itertools.product((0, 1), repeat=3):
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p = {**base["params"], **prm}
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for ax, l in zip(GRID, lv):
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p.update(GRID[ax][l])
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out[lv] = g.simulate(p, base["init"])[-1]["VFAC"]
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eff = [sum((1 if lv[i] else -1) * v for lv, v in out.items()) / 4 for i in range(3)]
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order_ok = eff[0] > eff[1] > eff[2] > 0
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mem = scs["generative_concentrated_lineages"]
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p0 = g.simulate({**mem["params"], **prm, "wtax": 0.0}, mem["init"])[-1]["P"]
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p1 = g.simulate({**mem["params"], **prm, "wtax": 1.0}, mem["init"])[-1]["P"]
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lines.append(f"| {label} | {scs[order[0]]['title']} ({res[order[0]]['VFAC']:.3f}) | "
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f"{scs[order[-1]]['title']} ({res[order[-1]]['VFAC']:.3f}) | {rho:.2f} | "
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f"{res['generative_regime']['P']:.2f}{' (lowest)' if gen_lowest else ''} | "
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f"{eff[0]:+.3f} / {eff[1]:+.3f} / {eff[2]:+.3f} | {'yes' if order_ok else 'no'} | "
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f"{p1 - p0:+.3f} | {'yes' if p0 - p1 < 0.06 else 'no'} |")
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(OUT / "robustness.md").write_text("\n".join(lines) + "\n")
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print("\n".join(lines))
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if __name__ == "__main__":
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main()
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