| """ |
| Variance overview (moved deeper; WORKING mock, house style). |
| (a) ABSOLUTE variance in agent engagement explained by persona vs model selection, |
| by regime -- total grows and the model-selection component scales. |
| (b) per-model attractiveness = mean incoming excess cross-model engagement H. |
| """ |
| import sys |
| from pathlib import Path |
| from collections import defaultdict |
| import json, sqlite3 |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| sys.path.insert(0, str(Path(__file__).parent)) |
| from housestyle import serif, mono, MODEL_COLOR |
|
|
| RUNS = Path("runs") |
| OUT = Path(__file__).parent / "fig_variance_overview.png" |
| 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"} |
|
|
| |
| |
| absvar = { |
| "Two-way": {"persona": 8.5, "model": 9.9}, |
| "Three-way": {"persona": 7.3, "model": 13.3}, |
| "Four-way": {"persona": 5.8, "model": 25.4}, |
| } |
|
|
| |
| def attractiveness(): |
| inc = defaultdict(list) |
| for db in sorted(RUNS.glob("sweep_*.db")): |
| if any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]): continue |
| mp = str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists(): continue |
| try: amm = json.load(open(mp)).get("agent_model_map",{}) |
| except: continue |
| if not amm: continue |
| fam = {int(k)+1: SHORT.get(v["model"]) for k,v in amm.items()} |
| if any(v is None for v in fam.values()): continue |
| try: |
| c = sqlite3.connect(db) |
| posts = c.execute("SELECT post_id,user_id FROM post").fetchall() |
| coms = c.execute("SELECT post_id,user_id FROM comment").fetchall(); c.close() |
| except: continue |
| if len(coms) < 200: continue |
| owner = {p:u for p,u in posts}; F = sorted(set(fam.values())) |
| nf = {f: sum(1 for v in fam.values() if v==f) for f in F}; ntot = sum(nf.values()) |
| edges = [(fam[cu],fam[owner[p]]) for p,cu in coms if owner.get(p) and owner[p]!=cu and cu in fam and owner[p] in fam] |
| tot = len(edges) or 1 |
| obs = defaultdict(int) |
| for x,y in edges: obs[(x,y)] += 1 |
| for Y in F: |
| hs=[obs.get((X,Y),0)/tot/((nf[X]/ntot)*((nf[Y])/(ntot-1)))-1 for X in F if X!=Y and nf[X]] |
| if hs: inc[Y].append(np.mean(hs)) |
| return {m:(np.mean(v), np.std(v)/np.sqrt(len(v))) for m,v in inc.items()} |
|
|
| def temporal_incoming_H(n_bins=4): |
| """Per-model incoming H over simulation quartiles (comment-order bins), pooled over runs.""" |
| acc = defaultdict(lambda: defaultdict(list)) |
| for db in sorted(RUNS.glob("sweep_*.db")): |
| if any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]): continue |
| mp = str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists(): continue |
| try: amm = json.load(open(mp)).get("agent_model_map",{}) |
| except: continue |
| if not amm: continue |
| fam = {int(k)+1: SHORT.get(v["model"]) for k,v in amm.items()} |
| if any(v is None for v in fam.values()): continue |
| try: |
| c = sqlite3.connect(db) |
| posts = c.execute("SELECT post_id,user_id FROM post").fetchall() |
| coms = c.execute("SELECT post_id,user_id FROM comment ORDER BY comment_id").fetchall(); c.close() |
| except: continue |
| if len(coms) < 200: continue |
| owner = {p:u for p,u in posts}; F = sorted(set(fam.values())) |
| nf = {f: sum(1 for v in fam.values() if v==f) for f in F}; ntot = sum(nf.values()) |
| edges = [(fam[cu],fam[owner[p]]) for p,cu in coms |
| if owner.get(p) and owner[p]!=cu and cu in fam and owner[p] in fam] |
| if len(edges) < n_bins*20: continue |
| for b in range(n_bins): |
| seg = edges[b*len(edges)//n_bins:(b+1)*len(edges)//n_bins] |
| tot = len(seg) or 1 |
| obs = defaultdict(int) |
| for xx,yy in seg: obs[(xx,yy)] += 1 |
| for Y in F: |
| hs=[obs.get((X,Y),0)/tot/((nf[X]/ntot)*((nf[Y])/(ntot-1)))-1 for X in F if X!=Y and nf[X]] |
| if hs: acc[Y][b].append(np.mean(hs)) |
| return acc |
|
|
| plt.rcParams.update({"axes.spines.top":False,"axes.spines.right":False,"figure.dpi":300}) |
| fig,(axA,axB) = plt.subplots(1,2, figsize=(7.2,2.6), gridspec_kw={"width_ratios":[1,1.05]}) |
|
|
| |
| regimes=["Two-way","Three-way","Four-way"]; x=np.arange(3); w=0.38 |
| pc="#c0392b"; mc="#2c6fbb" |
| for i,key in enumerate(["persona","model"]): |
| vals=[absvar[r][key] for r in regimes]; off=(-w/2 if i==0 else w/2) |
| axA.bar(x+off, vals, w, color=(pc if i==0 else mc), |
| label=("persona" if i==0 else "base model"), edgecolor="white", lw=0.5) |
| for xi,vv in zip(x+off,vals): |
| axA.text(xi, vv+0.4, f"{vv:.1f}", ha="center", va="bottom", fontproperties=mono(8)) |
| axA.set_xticks(x); axA.set_xticklabels(regimes, fontproperties=mono(9)) |
| axA.set_ylabel("CPP variance explained ($R^2$, \\%)", fontproperties=mono(8.5)) |
| axA.set_ylim(0, 29) |
| axA.legend(prop=mono(8), frameon=False, loc="upper left") |
| axA.set_title("(a) Per-post engagement: model overtakes persona", fontproperties=serif(9.5, bold=True)) |
| axA.tick_params(labelsize=8) |
|
|
| |
| TETDIR = RUNS / "tetratic" |
| def regime_H(): |
| acc = defaultdict(lambda: defaultdict(list)) |
| def proc(globpat, tet=False): |
| for db in sorted(globpat): |
| if not tet and any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]): continue |
| if tet and (TETDIR/f"{db.stem}_checkpoint.json").exists(): continue |
| mp = str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists(): continue |
| try: amm = json.load(open(mp)).get("agent_model_map",{}) |
| except: continue |
| if not amm: continue |
| fam = {int(k)+1: SHORT.get(v["model"]) for k,v in amm.items()} |
| if any(v is None for v in fam.values()): continue |
| try: |
| c = sqlite3.connect(db) |
| posts = c.execute("SELECT post_id,user_id FROM post").fetchall() |
| coms = c.execute("SELECT post_id,user_id FROM comment").fetchall(); c.close() |
| except: continue |
| if len(coms) < (100 if tet else 200): continue |
| F = sorted(set(fam.values())); reg = {2:"dyad",3:"triad",4:"tetrad"}.get(len(F)) |
| if not reg: continue |
| owner = {p:u for p,u in posts}; nf = {f: sum(1 for v in fam.values() if v==f) for f in F}; N = sum(nf.values()) |
| edges = [(fam[cu],fam[owner[p]]) for p,cu in coms if owner.get(p) and owner[p]!=cu and cu in fam and owner[p] in fam] |
| tot = len(edges) or 1; obs = defaultdict(int) |
| for a,b in edges: obs[(a,b)] += 1 |
| for Y in F: |
| hs = [obs.get((X,Y),0)/tot/((nf[X]/N)*(nf[Y]/(N-1)))-1 for X in F if X!=Y] |
| if hs: acc[Y][reg].append(np.mean(hs)) |
| proc(RUNS.glob("sweep_*.db")); proc(TETDIR.glob("tetra_s*_r*.db"), tet=True) |
| return acc |
| rH = regime_H() |
| regs = ["dyad","triad","tetrad"]; xr = np.arange(3) |
| axB.axhline(0, color="#666", lw=0.9, ls=":") |
| for m in ["gpt-oss","Qwen","GLM-4","Gemma","Mag"]: |
| if m not in rH: continue |
| ys = [np.mean(rH[m][r]) if rH[m].get(r) else np.nan for r in regs] |
| axB.plot(xr, ys, marker="o", ms=4.5, lw=1.8, color=MODEL_COLOR[m], label=m) |
| allv = [np.mean(rH[m][r]) for m in rH for r in regs if rH[m].get(r)] |
| M = max(abs(v) for v in allv) * 1.18 |
| axB.set_ylim(-M, M) |
| axB.set_xticks(xr); axB.set_xticklabels(["dyad","triad","tetrad"], fontproperties=mono(9)) |
| axB.set_xlim(-0.15, 2.15) |
| axB.set_ylabel("incoming $H$ (excess engagement)", fontproperties=mono(8.5)) |
| axB.set_title("(b) Attraction sharpens with mixture size", fontproperties=serif(9.5, bold=True)) |
| axB.legend(prop=mono(7), frameon=False, ncol=2, loc="lower left", handlelength=1.3) |
| axB.tick_params(labelsize=8) |
|
|
| plt.tight_layout(w_pad=1.5) |
| fig.savefig(OUT, dpi=300, bbox_inches="tight") |
| print(f"saved {OUT.name}") |
|
|
