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H temporal dynamics analysis: decay, rotation stability, seed stability.
Splits each simulation's comment history into quartiles by comment_id and
computes directed-pair homophily H_{src->dst} per quartile. Aggregates across
runs to characterize each model's temporal signature (stable attractor, transient
attractor, delayed attractor, repeller), and measures rotation/seed stability
of the temporal trajectories.
Configuration:
RUNS_DIR — directory containing sweep_*.db files (env var or --runs-dir)
Usage:
python temporal_dynamics.py # dyadic decay (all targets)
python temporal_dynamics.py --triadic # triadic decay
python temporal_dynamics.py --target gpt-oss # specific target only
python temporal_dynamics.py --runs-dir /path/to/runs
"""
import argparse
import json
import os
import sqlite3
from collections import defaultdict
from pathlib import Path
import numpy as np
SHORT = {
'Qwen/Qwen3-32B': 'Qwen',
'openai/gpt-oss-20b': 'gpt-oss',
'mistralai/Magistral-Small-2509': 'Mag',
'zai-org/GLM-4-32B-0414': 'GLM-4',
'google/gemma-4-31B-it': 'Gemma',
}
ALL_TARGETS = ['gpt-oss', 'Qwen', 'Mag', 'GLM-4', 'Gemma']
def H_in_window(db_path: Path, cmin: int, cmax: int) -> dict | None:
"""Compute H for comments with comment_id in [cmin, cmax)."""
meta = str(db_path).replace('.db', '.metadata.json')
if not Path(meta).exists():
return None
try:
amm = json.load(open(meta)).get('agent_model_map', {})
uf = {int(k)+1: SHORT.get(v['model'], '?') for k, v in amm.items()}
except Exception:
return None
fc = defaultdict(int)
for fam in uf.values():
fc[fam] += 1
n = sum(fc.values())
conn = sqlite3.connect(db_path)
comments = conn.execute(
f'SELECT c.user_id, p.user_id FROM comment c JOIN post p ON c.post_id=p.post_id '
f'WHERE c.user_id!=p.user_id AND c.comment_id >= {cmin} AND c.comment_id < {cmax}'
).fetchall()
conn.close()
ec = defaultdict(int)
for cu, pu in comments:
sf, df = uf.get(cu), uf.get(pu)
if sf and df:
ec[(sf, df)] += 1
total = sum(ec.values())
if total < 5:
return None
H = {}
for s in fc:
for d in fc:
exp = (fc[s]/n) * ((fc[d] - (1 if s == d else 0)) / (n-1))
if exp > 0:
H[(s, d)] = ec.get((s, d), 0) / total / exp - 1
return H
def trajectory(db_path: Path, target: str, n_bins: int = 4) -> list | None:
"""Return list of incoming-to-target mean H per quartile."""
conn = sqlite3.connect(db_path)
max_id = conn.execute('SELECT MAX(comment_id) FROM comment').fetchone()[0]
conn.close()
if max_id is None or max_id < 40:
return None
bs = max_id // n_bins
traj = []
for i in range(n_bins):
lo = i*bs + 1
hi = (i+1)*bs + 1 if i < n_bins-1 else max_id + 1
H = H_in_window(db_path, lo, hi)
if H is None:
traj.append(None)
continue
inc = [v for (s, d), v in H.items() if d == target and s != target]
traj.append(np.mean(inc) if inc else None)
return traj
def discover_dyadic_runs(runs_dir: Path, target: str | None = None) -> list:
"""Return list of (db_path, group_A_model, partner, seed) for dyadic runs."""
out = []
for db in runs_dir.glob('sweep_*.db'):
if 'tri_' in db.name or 'pilot_' in db.name:
continue
meta = str(db).replace('.db', '.metadata.json')
if not Path(meta).exists():
continue
try:
amm = json.load(open(meta)).get('agent_model_map', {})
uid_model = {int(k): SHORT.get(v['model']) for k, v in amm.items()}
except Exception:
continue
if any(v is None for v in uid_model.values()):
continue
fams = set(uid_model.values())
if len(fams) != 2:
continue
if target and target not in fams:
continue
group_A = uid_model.get(0)
seed = next((s for s in [42, 137, 271, 314, 999] if f'_s{s}.' in db.name), None)
if seed is None:
continue
partner = (fams - {group_A}).pop()
out.append((str(db), group_A, partner, seed))
return out
def discover_triadic_runs(runs_dir: Path, target: str | None = None) -> list:
"""Return list of (db_path, group_A_model, triplet_tuple, seed) for triadic runs."""
out = []
for db in runs_dir.glob('sweep_tri_*.db'):
meta = str(db).replace('.db', '.metadata.json')
if not Path(meta).exists():
continue
try:
amm = json.load(open(meta)).get('agent_model_map', {})
uid_model = {int(k): SHORT.get(v['model']) for k, v in amm.items()}
except Exception:
continue
if any(v is None for v in uid_model.values()):
continue
fams = sorted(set(uid_model.values()))
if len(fams) != 3:
continue
if target and target not in fams:
continue
group_A = uid_model.get(0)
seed = next((s for s in [42, 271, 999] if f'_s{s}.' in db.name), None)
if seed is None:
continue
out.append((str(db), group_A, tuple(fams), seed))
return out
def dyadic_stability_report(runs_dir: Path, target: str):
"""Per-pair Q0..Q3 trajectory + rotation + seed stability for a target family."""
runs = discover_dyadic_runs(runs_dir, target=target)
by_pair_ord = defaultdict(lambda: defaultdict(list))
for db, group_A, partner, seed in runs:
traj = trajectory(Path(db), target)
if traj is None or any(v is None for v in traj):
continue
ordering = 'target_A' if group_A == target else 'partner_A'
by_pair_ord[partner][ordering].append((seed, traj))
print(f"\n{'='*78}")
print(f"DYADIC stability for incoming-to-{target}")
print(f"{'='*78}")
for partner in sorted(by_pair_ord):
print(f"\n {target} x {partner}")
order_means = {}
for ord_name, lst in by_pair_ord[partner].items():
if not lst:
continue
trajs = np.array([t for _, t in lst])
order_means[ord_name] = trajs.mean(axis=0)
print(f" [{ord_name}] n={len(trajs)} seeds | "
f"Q0..Q3: {' '.join(f'{v:+.2f}' for v in trajs.mean(axis=0))} | "
f"seed-SD: {' '.join(f'{s:.2f}' for s in trajs.std(axis=0))}")
if len(order_means) == 2:
keys = list(order_means.keys())
spreads = [abs(order_means[keys[0]][q] - order_means[keys[1]][q]) for q in range(4)]
print(f" rotation spread per Q: {' '.join(f'{s:.2f}' for s in spreads)} "
f"max={max(spreads):.2f}")
def triadic_stability_report(runs_dir: Path, target: str):
"""Per-triplet Q0..Q3 trajectory + rotation + seed stability for a target family."""
runs = discover_triadic_runs(runs_dir, target=target)
by_trip_ord = defaultdict(lambda: defaultdict(list))
for db, group_A, trip, seed in runs:
if target not in trip:
continue
traj = trajectory(Path(db), target)
if traj is None or any(v is None for v in traj):
continue
by_trip_ord[trip][group_A].append((seed, traj))
print(f"\n{'='*78}")
print(f"TRIADIC stability for incoming-to-{target}")
print(f"{'='*78}")
for trip in sorted(by_trip_ord):
if sum(len(v) for v in by_trip_ord[trip].values()) < 3:
continue
print(f"\n Triplet: {' + '.join(trip)}")
per_ord_mean = {}
for grpA, lst in by_trip_ord[trip].items():
if not lst:
continue
trajs = np.array([t for _, t in lst])
per_ord_mean[grpA] = trajs.mean(axis=0)
print(f" [group-A={grpA}] n={len(trajs)} seeds | "
f"Q0..Q3: {' '.join(f'{v:+.2f}' for v in trajs.mean(axis=0))} | "
f"seed-SD: {' '.join(f'{s:.2f}' for s in trajs.std(axis=0))}")
if len(per_ord_mean) >= 2:
spreads = [max(t[q] for t in per_ord_mean.values()) -
min(t[q] for t in per_ord_mean.values())
for q in range(4)]
print(f" rotation spread per Q: {' '.join(f'{s:.2f}' for s in spreads)} "
f"max={max(spreads):.2f}")
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--runs-dir', default=os.environ.get("RUNS_DIR", "./runs"),
help="Directory containing sweep_*.db files.")
parser.add_argument('--triadic', action='store_true',
help="Run triadic stability report instead of dyadic.")
parser.add_argument('--target', default=None,
help="Restrict to one model family (e.g. gpt-oss). Default: all.")
args = parser.parse_args()
runs_dir = Path(args.runs_dir)
targets = [args.target] if args.target else ALL_TARGETS
for t in targets:
if args.triadic:
triadic_stability_report(runs_dir, t)
else:
dyadic_stability_report(runs_dir, t)
if __name__ == '__main__':
main()
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