tegwick
c8329c6965
- Wealth-concentration stock W with wealth-tax lever; converts into market and political channels only, moderated by checks (Q*S) - wealth_on switch reproduces the T03 model exactly (verified) - wealth.py: P3 supported and robust to conversion strength (full wealth tax lowers P by only 0.01-0.05 because membrane/lineage control does not run on wealth); effect on reachable channels depends on k_w - Tests for wealth switch and tax direction 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
71 lines
3.1 KiB
Python
71 lines
3.1 KiB
Python
"""Hypothesis tests P1 and P2 for the GADM-0.3 power channels.
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P1: each channel becomes self-sustaining above a threshold. Test: sweep the
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channel's initial value with everything else at the scenario initial state,
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and look for a jump in its final value (bistability).
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P2: membrane/lineage channels plus learning with use (R7) form a natural-
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monopoly regime unless lineage diffusion L exceeds a critical value. Test:
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sweep L for several learning amplifications lw.
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Runs the T03 stage of GADM-0.3 (wealth_on = 0, via gadm03.t03_params()) so the
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results in models/GADM-0.3.md § 2 stay reproducible after T04 added wealth.
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Usage: python3 -B simulations/gadm02/power_channels.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 gadm03 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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GRID = [i / 20 for i in range(21)]
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def p1(sc: dict) -> list[str]:
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lines = ["| Channel | Final value for initial 0.0, 0.2, …, 1.0 | Largest jump (initial) | Path-dependent |",
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"|---|---|---|---|"]
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for k in g.CHANNELS:
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finals = [g.simulate(sc["params"], {**sc["init"], k: x})[-1][k] for x in GRID]
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jumps = [(GRID[i], GRID[i + 1], finals[i + 1] - finals[i]) for i in range(len(GRID) - 1)]
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big = max(jumps, key=lambda j: j[2])
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bistable = max(finals) - min(finals) > 0.3 # end state depends on the start
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thr = f"{big[0]:.2f}–{big[1]:.2f}" if bistable else "—"
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spark = " ".join(f"{v:.2f}" for v in finals[::4])
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lines.append(f"| {g.CHANNEL_NAMES[k]} (`{k}`) | {spark} | {thr} | {'yes' if bistable else 'no'} |")
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return lines
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def p2(sc: dict) -> list[str]:
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lines = ["| lw (learning amplification) | final `Plin` for L = 0.0, 0.2, …, 1.0 | critical L (Plin < 0.5) |",
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"|---|---|---|"]
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for lw in (0.0, 2.0, 4.0):
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finals = [g.simulate({**sc["params"], "L": L, "lw": lw}, sc["init"])[-1]["Plin"] for L in GRID]
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crit = next((L for L, v in zip(GRID, finals) if v < 0.5), None)
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lines.append(f"| {lw:.0f} | " + " ".join(f"{v:.2f}" for v in finals[::4])
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+ f" | {crit:.2f} |" if crit is not None else f"| {lw:.0f} | "
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+ " ".join(f"{v:.2f}" for v in finals[::4]) + " | none ≤ 1 |")
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return lines
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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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lines = [f"# {g.MODEL_VERSION}: power-channel hypotheses P1 and P2", "",
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"Generated by `simulations/gadm02/power_channels.py` (T03 stage, wealth_on = 0).",
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"Final values at t = 100 years.", ""]
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for name in ("baseline", "generative_regime"):
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sc = {**scs[name], "params": {**scs[name]["params"], **g.t03_params()}}
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lines += [f"## {sc['title']}", "", "### P1: thresholds per channel", ""] + p1(sc) + [""]
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lines += ["### P2: lineage diffusion vs. learning with use", ""] + p2(sc) + [""]
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(OUT / "power_channels.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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