assembly-economics/simulations/gadm02/selection.py

"""Endogenous selection weights across the adoption band (ASSEMBLY-WP-0007-T03).

Tests (GADM-Sectors-Scope.md § 4; ParameterGrounding.md § 3):
  1. Does domination capture selection itself (market + organism weight share rising with P)?
  2. Is capture slow and incremental (no flip of the weights within a few years)?
  3. Does a plural selection mix make the generative regime more stable against a
     domination shock?

Usage: python3 -B simulations/gadm02/selection.py
"""

from __future__ import annotations

import sys
from pathlib import Path

sys.dont_write_bytecode = True

import band_probe as bp  # noqa: E402
import gadm05 as g5  # noqa: E402
import grounding as gr  # noqa: E402
from scenarios import load_all  # noqa: E402

OUT = Path(__file__).resolve().parent / "results"
ANCHORS = list(gr.BAND)
SCEN = ("baseline", "attention_oligopoly", "generative_concentrated_lineages", "generative_regime")
SHOCK = {"Pd": 0.25, "Pm": 0.15}   # domination shock added to the starting state


def max_rate(en: list[dict], fx: list[dict]) -> float:
    """Fastest change of the *endogenous part* of capture (endogenous minus fixed weights) per year.
    Capture itself also moves with K_Omega (organism weight x K_Omega), even with fixed weights."""
    diff = [(e["t"], e["capture"] - f["capture"]) for e, f in zip(en, fx)]
    return max(abs(b - a) / (tb - ta) for (ta, a), (tb, b) in zip(diff, diff[1:]))


def main() -> None:
    OUT.mkdir(exist_ok=True)
    scs = load_all()
    var = {a: gr.variant(gr.BAND[a]) for a in ANCHORS}
    res = {}
    for a in ANCHORS:
        for sc in SCEN:
            p = {**scs[sc]["params"], **var[a][0]}
            i0 = {**scs[sc]["init"], **var[a][1]}
            fx = g5.simulate_sel(p, i0, endogenous=False)
            en = g5.simulate_sel(p, i0, endogenous=True)
            shock = {k: min(1.0, i0.get(k, g5.g4.INIT[k]) + v) for k, v in SHOCK.items()}
            fxs = g5.simulate_sel(p, {**i0, **shock}, endogenous=False)
            ens = g5.simulate_sel(p, {**i0, **shock}, endogenous=True)
            res[(a, sc)] = {"Vfx": fx[-1]["VFAC"], "Ven": en[-1]["VFAC"], "c0": en[0]["capture"],
                            "c100": en[-1]["capture"], "rate": max_rate(en, fx),
                            "cfx100": fx[-1]["capture"],
                            "loss_fx": fxs[-1]["VFAC"] - fx[-1]["VFAC"], "loss_en": ens[-1]["VFAC"] - en[-1]["VFAC"],
                            "sig_fx": fx[-1]["sigma"], "sig_en": en[-1]["sigma"]}
    lines = ["# GADM-0.5-dev: endogenous selection weights across the band", "",
             "Generated by `simulations/gadm02/selection.py` (ASSEMBLY-WP-0007-T03). Priors: weights relax at",
             f"{g5.SEL_PARAMS['sel_lam']}/year; domination strength {g5.SEL_PARAMS['k_cap']}, civic strength "
             f"{g5.SEL_PARAMS['k_civ']}. Capture = share of selection weight held by market + organism channels.",
             f"Shock = {SHOCK} added to the starting state. t = 100.", "",
             "| Scenario | Anchor | VFAC fixed → endogenous | σ fixed → endogenous | capture t0 → t100 (fixed weights at t100) | max endogenous capture change/yr | shock VFAC loss fixed / endogenous |",
             "|---|---|---|---|---|---|---|"]
    for sc in SCEN:
        for a in ANCHORS:
            r = res[(a, sc)]
            lines.append(f"| {scs[sc]['title']} | {a} | {r['Vfx']:.3f} → {r['Ven']:.3f} | {r['sig_fx']:.3f} → "
                         f"{r['sig_en']:.3f} | {r['c0']:.2f} → {r['c100']:.2f} ({r['cfx100']:.2f}) | {r['rate']:.4f} | "
                         f"{r['loss_fx']:+.3f} / {r['loss_en']:+.3f} |")
    readings = []
    for sc in SCEN:
        readings.append((f"endogenous capture at t100 (endogenous − fixed weights): {scs[sc]['title']}",
                         [res[(a, sc)]["c100"] - res[(a, sc)]["cfx100"] for a in ANCHORS]))
        readings.append((f"ΔVFAC endogenous − fixed: {scs[sc]['title']}", [res[(a, sc)]["Ven"] - res[(a, sc)]["Vfx"] for a in ANCHORS]))
    readings.append(("shock loss difference (endogenous − fixed), generative regime",
                     [res[(a, 'generative_regime')]["loss_en"] - res[(a, 'generative_regime')]["loss_fx"] for a in ANCHORS]))
    lines += ["", "## Readings and robustness class", "", "| Reading | " + " | ".join(ANCHORS) + " | class |",
              "|---|" + "---|" * (len(ANCHORS) + 1)]
    for name, vals in readings:
        lines.append(f"| {name} | " + " | ".join(f"{v:+.3f}" for v in vals) + f" | {bp.classify(vals, 'sign')} |")
    rate = max(r["rate"] for r in res.values())
    lines += ["", f"Largest change of the endogenous part of capture in any run: {rate:.4f} per year "
              f"({'slow and incremental' if rate < 0.02 else 'FAST'}; grounding test: no flip within a few years).",
              "Rows with ΔVFAC of ±0.000 are classified by sign noise; read them as 'no effect'.", ""]
    (OUT / "selection.md").write_text("\n".join(lines))
    print("\n".join(lines))


if __name__ == "__main__":
    main()