| """Tetradic (four-way) results: pooled 4x4 H heatmap + descriptive stats + variance decomposition. |
| Over all COMPLETE tetradic runs (no checkpoint file). Persistent; re-run as more land. |
| Outputs: |
| - figures/fig_h_tetradic.png (heatmap, style matched to plot_matrices.py) |
| - _paper/analyses/tetradic_results.md (numbers to hand to DR) |
| """ |
| import sqlite3, json |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| import matplotlib; matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from matplotlib.font_manager import FontProperties, fontManager |
|
|
| HERE = Path(__file__).parent |
| FIGDIR = Path(".") |
| OUTMD = Path("out/tetradic_results.md") |
| SHORT = {"Qwen/Qwen3-32B":"Qwen","openai/gpt-oss-20b":"gpt-oss", |
| "mistralai/Magistral-Small-2509":"Mag","zai-org/GLM-4-32B-0414":"GLM-4"} |
| MODELS = ["GLM-4","Mag","Qwen","gpt-oss"] |
| try: |
| for ttf in (Path.home()/"fonts").rglob("*.ttf"): fontManager.addfont(str(ttf)) |
| FAM = "Ubuntu Mono" |
| except Exception: |
| FAM = "monospace" |
|
|
| perH = defaultdict(list) |
| matruns = [] |
| lead = last = 0; rho = []; nrun = 0 |
| agents = [] |
|
|
| for db in sorted(HERE.glob("tetra_s*_r*.db")): |
| base = db.stem |
| if (HERE/f"{base}_checkpoint.json").exists(): continue |
| mp = str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists(): continue |
| fam = {int(k)+1: SHORT.get(v["model"]) for k,v in json.load(open(mp))["agent_model_map"].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() |
| users = dict(c.execute("SELECT user_id,user_name FROM user").fetchall()); c.close() |
| except Exception: 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) |
| if tot < 100: continue |
| nrun += 1 |
| obs = defaultdict(int) |
| for x,y in edges: obs[(x,y)] += 1 |
| |
| Mx = np.full((4,4), np.nan) |
| for i,X in enumerate(MODELS): |
| for j,Y in enumerate(MODELS): |
| exp = (nf[X]/ntot)*((nf[Y]-(1 if X==Y else 0))/(ntot-1)) |
| if exp>0: Mx[i,j] = obs.get((X,Y),0)/tot/exp - 1 |
| matruns.append(Mx) |
| |
| for Y in MODELS: |
| perH[Y].append(np.nanmean([Mx[i,MODELS.index(Y)] for i,X in enumerate(MODELS) if X!=Y])) |
| |
| ind = defaultdict(int) |
| for p,cu in coms: |
| o = owner.get(p) |
| if o in fam and o!=cu: ind[o]+=1 |
| fam_in = defaultdict(list) |
| for u,f in fam.items(): fam_in[f].append(ind.get(u,0)) |
| mean_in = {f: np.mean(v) for f,v in fam_in.items()} |
| if max(mean_in, key=mean_in.get)=="gpt-oss": lead+=1 |
| if min(mean_in, key=mean_in.get)=="Mag": last+=1 |
| tot_in = sum(sum(v) for v in fam_in.values()) |
| share = sum(fam_in["gpt-oss"])/tot_in if tot_in else 0 |
| rho.append(share/(nf["gpt-oss"]/ntot)) |
| for u,f in fam.items(): |
| agents.append({"run":base,"model":f,"persona":users.get(u,f"u{u}"),"indeg":ind.get(u,0)}) |
|
|
| |
| Mpool = np.nanmean(np.stack(matruns), axis=0) |
| fig, ax = plt.subplots(figsize=(4.2,3.6)) |
| vmax = np.nanmax(np.abs(Mpool)) |
| im = ax.imshow(Mpool, cmap="RdBu_r", vmin=-vmax, vmax=vmax) |
| ax.set_xticks(range(4)); ax.set_yticks(range(4)) |
| ax.set_xticklabels(MODELS, fontproperties=FontProperties(family=FAM,size=9), rotation=30, ha="right") |
| ax.set_yticklabels(MODELS, fontproperties=FontProperties(family=FAM,size=9)) |
| ax.set_xlabel("post author (target)", fontproperties=FontProperties(family=FAM,size=9)) |
| ax.set_ylabel("commenter (source)", fontproperties=FontProperties(family=FAM,size=9)) |
| for i in range(4): |
| for j in range(4): |
| v = Mpool[i,j] |
| ax.text(j,i,f"{v:+.2f}",ha="center",va="center", |
| fontproperties=FontProperties(family=FAM,size=8), |
| color="white" if abs(v)>0.55*vmax else "black") |
| cb = fig.colorbar(im, fraction=0.046, pad=0.04); cb.ax.tick_params(labelsize=7) |
| ax.set_title(f"Four-way $H_{{X\\to Y}}$ (pooled, n={nrun} runs)", |
| fontproperties=FontProperties(family=FAM,size=10)) |
| fig.tight_layout(); fig.savefig(FIGDIR/"fig_h_tetradic.png", dpi=300, bbox_inches="tight") |
| print("wrote", FIGDIR/"fig_h_tetradic.png") |
|
|
| |
| y = np.array([a["indeg"] for a in agents], float) |
| def dummies(keys): |
| levels = sorted(set(keys)); idx = {l:i for i,l in enumerate(levels)} |
| D = np.zeros((len(keys), len(levels))) |
| for r,k in enumerate(keys): D[r, idx[k]] = 1 |
| return D[:,1:] |
| def r2(*blocks): |
| blocks = [b for b in blocks if b is not None and b.shape[1]>0] |
| X = np.column_stack([np.ones(len(y))] + blocks) if blocks else np.ones((len(y),1)) |
| b,*_ = np.linalg.lstsq(X, y, rcond=None); pred = X@b |
| return 1 - ((y-pred)**2).sum()/((y-y.mean())**2).sum() |
| P = dummies([a["persona"] for a in agents]) |
| Mod = dummies([a["model"] for a in agents]) |
| R = dummies([a["run"] for a in agents]) |
| ossind = np.array([[1.0 if a["model"]=="gpt-oss" else 0.0] for a in agents]) |
| vd = {"persona_only":r2(P), "model_only":r2(Mod), "oss_only":r2(ossind), |
| "run_only":r2(R), "persona+model":r2(P,Mod), "persona+run":r2(P,R), "full":r2(P,Mod,R)} |
| dmodel = vd["full"] - vd["persona+run"] |
|
|
| |
| o = [f"# Tetradic (four-way) results — {nrun} complete runs\n", |
| "## Incoming H per model (mean over commenter families, across runs)\n", |
| "| model | mean H | SD | min | max |", "|---|---:|---:|---:|---:|"] |
| for m in ["gpt-oss","Qwen","GLM-4","Mag"]: |
| v = np.array(perH[m]); o.append(f"| {m} | {v.mean():+.2f} | {v.std():.2f} | {v.min():+.2f} | {v.max():+.2f} |") |
| o += [f"\n- gpt-oss is the **most-commented family in {lead}/{nrun} runs** ({100*lead/nrun:.0f}%).", |
| f"- Magistral is the **least-commented in {last}/{nrun} runs** ({100*last/nrun:.0f}%).", |
| f"- gpt-oss over-representation rho = comment-share / fair-share: **mean {np.mean(rho):.2f}, median {np.median(rho):.2f}**.", |
| "\n## Variance decomposition (per-agent raw in-degree, in-sample R^2)\n", |
| "| block | R^2 |", "|---|---:|"] |
| for k in ["persona_only","model_only","oss_only","run_only","persona+model","persona+run","full"]: |
| o.append(f"| {k.replace('_',' ')} | {vd[k]:.3f} |") |
| o.append(f"| **Delta R^2 model** (full - persona+run) | **{dmodel:+.3f}** |") |
| o.append(f"\nn agents = {len(agents)}. Compare dyad Delta R^2_model +.044, triad +.071.") |
| OUTMD.write_text("\n".join(o)+"\n"); print("\n".join(o)); print("wrote", OUTMD) |
|
|
