""" 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()