| # Experiment Protocol: Disentangling Models from Personas in LLM Social Simulations |
|
|
| ## Overview |
|
|
| This document is the canonical reproducibility narrative for the dyadic, triadic, |
| and four-way (tetradic) sweep experiments and the lexical/content analyses reported |
| in the paper. It complements the code in `sweep/`, `oasis/`, `launch/`, and |
| `analyses/` by explaining the experimental design decisions that are not obvious |
| from code alone. |
|
|
| --- |
|
|
| ## Simulator |
|
|
| We adapt the OASIS simulator (v0.2.5, https://github.com/camel-ai/oasis) to |
| support heterogeneous multi-model populations. The key modifications are in |
| `oasis/run_v9.py`: |
|
|
| - `hetero` mode: 2 model families, 10 agents each |
| - `hetero3` mode: 3 model families, 10 agents each |
| - Plackett-Luce feed ranking with Gumbel noise (T=5.0) |
| - Post-share balancer: prevents one model family from dominating posts |
| - Warm-up phase: all agents post once before the main loop; any run where a |
| family has < 50% participation is aborted |
| - SIGTERM-safe checkpoint/resume for preemptible SLURM allocations |
|
|
| --- |
|
|
| ## Models |
|
|
| Five models are tested. All are served via vLLM 0.19.0 on 8× NVIDIA L40S 48 GB. |
| See `env/vllm_version.md` for exact launch parameters. |
|
|
| | Model | HF ID | TP | Port | |
| |-------|-------|----|------| |
| | Qwen | Qwen/Qwen3-32B | 2 | 8002 | |
| | gpt-oss | openai/gpt-oss-20b | 1 | 8003 | |
| | Magistral | mistralai/Magistral-Small-2509 | 2 | 8004 | |
| | GLM-4 | zai-org/GLM-4-32B-0414 | 2 | 8007 | |
| | Gemma | google/gemma-4-31B-it | 2 | 8009 | |
|
|
| --- |
|
|
| ## Persona initialization |
|
|
| Personas are sampled from the Moltbook community archive (HF dataset). The |
| seed database (`oasis/seed_db/hf-zero-50.sqlite`) contains 50 real agent |
| profiles: display names, bios, and prior post histories (up to 5 posts per |
| agent, ~500 chars each). |
|
|
| A diversity filter ensures at most 1 HK CLAW minter persona per run (a narrow |
| cluster that otherwise over-represents in random samples). The filter is |
| deterministic given the RNG seed. |
|
|
| Each seed value produces a different persona sample. Seeds: [42, 137, 271, 314, 999] |
| for dyadic runs; [42, 271, 999] for triadic runs. |
|
|
| --- |
|
|
| ## Rotating assignments |
|
|
| For **dyadic** runs: 20 agents (10 per family), two orderings per seed (A=X,B=Y |
| and A=Y,B=X). This gives 2 × C(5,2) × 5 = 100 runs. |
|
|
| For **triadic** runs: 30 agents (10 per family), 3 of 6 orderings per seed |
| (selected by taking permutations at indices 0, 2, 4 for maximum positional |
| diversity). This gives 3 × C(5,3) × 3 = 90 runs. |
|
|
| --- |
|
|
| ## Simulation parameters |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Steps | 100 | |
| | Activation probability | 0.3 per agent per step | |
| | Feed size (K) | 20 | |
| | Gumbel noise temperature | T=5.0 | |
| | Post-share tolerance | δ=0.05 | |
| | Input token limit | 12,000 | |
| | Max output tokens | 1536 (512 for Gemma) | |
| | Temperature | 0.85 | |
| | Max tool-call attempts per turn | 6 | |
|
|
| --- |
|
|
| ## Metrics |
|
|
| ### Directed pairwise engagement homophily (H) |
|
|
| For families X and Y: |
|
|
| ``` |
| H_{X→Y} = obs(X→Y) / exp(X→Y) - 1 |
| ``` |
|
|
| where `obs(X→Y)` = fraction of all directed comment edges from X to Y, and |
| `exp(X→Y)` = null expectation under random mixing proportional to group sizes. |
|
|
| Two edge types are counted: |
| 1. commenter → post_author |
| 2. replier → parent_comment_author (threaded replies) |
| |
| Positive H = X engages with Y more than random; negative = avoidance. |
| Diagonal (X=X) = in-group self-preference. |
| |
| ### Swap stability |
| |
| For dyadic runs: mean |H_{X→Y}^(AB) - H_{X→Y}^(BA)| across seeds. |
| For triadic runs: mean max-|ΔH| across the 3 orderings per (triplet, seed). |
| Lower = more stable across persona-to-model assignment rotations. |
| |
| --- |
| |
| ## Validity criteria |
| |
| A run is included in the paper analysis if: |
| |
| - **Dyadic**: ≥ 18 distinct agents commented, ≥ 200 total comments |
| - **Triadic**: ≥ 26 distinct agents commented, ≥ 200 total comments |
| |
| These thresholds filter out runs that were aborted (e.g., server failure, |
| warm-up failure) before completing enough interaction to yield reliable H estimates. |
| |
| Note: The H-matrix figure scripts (`analyses/plot_matrices.py`) apply only |
| `nu ≥ 26` for triadic runs (no nc threshold), matching the original data |
| selection used for the paper's H tables. The variance decomposition scripts |
| (the variance scripts) apply `nc ≥ 200` for both |
| dyadic and triadic, which excludes one additional triadic run with nc=169. |
| The paper's stated 182-run pool (100 dyadic + 82 triadic) was computed at an |
| earlier snapshot; the current pool is 100 dyadic + 89 triadic = 189 runs (see |
| `data/MANIFEST.md`). |
|
|
| --- |
|
|
| ## Run identification convention |
|
|
| Database file names encode the run configuration: |
|
|
| ``` |
| sweep_{modelA[:3]}_{modelB[:3]}_s{seed}.db # dyadic |
| sweep_tri_{A[:3]}_{B[:3]}_{C[:3]}_s{seed}.db # triadic |
| ``` |
|
|
| Each `.db` file has a corresponding `.metadata.json` sidecar with the |
| agent-to-model mapping, and `.metadata.json` + `_chats.jsonl` + `_reasoning.jsonl` |
| side-car files. |
|
|
| --- |
|
|
| ## Variance decomposition methodology |
|
|
| The variance decomposition (Appendix D) uses **comments per post** (CPP = |
| in-degree / number of posts) as the dependent variable, restricted to agents with |
| at least two posts. Raw in-degree conflates per-post appeal with posting *volume* |
| (an agent that posts more mechanically receives more comments); CPP removes that |
| exposure confound. Earlier drafts used raw in-degree, under which persona appears |
| to dominate two-way mixtures purely as a posting-volume artifact. |
|
|
| | Script | What it produces | |
| |--------|-----------------| |
| | `analyses/variance_cpp.py` | Hierarchical OLS R² decomposition + leave-one-model-out, on CPP (Tables D.1–D.2) | |
| | `analyses/absolute_variance.py` | Variance components across dyadic / triadic / four-way regimes | |
| | `analyses/plot_fig_variance_overview.py` | Figure: proportional CPP variance (model vs persona) + per-model H by mixture size | |
|
|
| Model selection is the agent's own base model plus the unordered partner-pair |
| (the other models present). In four-way runs all four models are present in every |
| run, so the partner composition is constant and model selection reduces to |
| own-model identity. |
|
|
| --- |
|
|
| ## Reproducibility notes |
|
|
| 1. **Stochastic elements**: Two sources of randomness per run, both controlled |
| by `--rng-seed`: |
| - Persona selection (which agents from the seed DB) |
| - Group allocation (which agents go to which model family) |
| - Feed noise is per-agent Gumbel, not globally seeded; exact reproduction of |
| feed orderings would require seeding each model call, which is not done. |
| |
| 2. **Model-side non-determinism**: vLLM with temperature=0.85 is stochastic. |
| Running the same configuration twice will produce different comments. The |
| paper reports means ± std across seeds and rotations; individual runs are |
| not expected to be identical. |
|
|
| 3. **Checkpoint/resume**: Partial DBs can be resumed with `--resume`. The |
| `_checkpoint.json` sidecar records the exact last completed step. |
|
|
| 4. **Warm-up ordering**: Agents post in a seeded-random order during warm-up, |
| ensuring the initial post distribution across model families is |
| approximately uniform by construction. |
|
|
| --- |
|
|
| ## Four-way (tetradic) runs |
|
|
| The four-way sweep uses the pool {gpt-oss, Qwen, Magistral, GLM-4}, 40 agents |
| (10 per family), 100 steps, post-share balancer on, activation prob 0.3 |
| (`run_v9.py --mode hetero4`). Two seeds (42, 271) each cover 12 distinct |
| model->block rotations (a Latin-square-style set), for 24 valid runs. |
|
|
| - **Validity:** >= 200 comments and all four models present. |
| - **Run identification:** `tetradic/tetra_s{seed}_r{rotation}.db`. |
| - **Metrics:** same directed engagement homophily (H) as dyadic/triadic; for the |
| variance decomposition and content analyses, CPP as above. |
|
|
| Driver: `launch/run_tetradic.sh`. Per-model H over the 24 runs and the four-way |
| H matrix/heatmap: `analyses/tetradic_recompute_H.py`, `analyses/tetradic_results.py`. |
|
|
| --- |
|
|
| ## Content analysis |
|
|
| Finding 3 analyzes the text agents produce. Build the corpus once with |
| `analyses/extract_texts.py` (-> `data/texts.jsonl`: one row per post/comment with |
| its model and persona). Then: |
|
|
| - `analyses/lexical_table.py` — TF-IDF (word 1–2-grams, English stop words removed, |
| + char 3–5-grams) -> logistic regression recovering model / persona (Table 1). |
| - `analyses/svd_axes.py` — agent-level TF-IDF -> truncated SVD (30 components); the |
| first axis (document language) is dropped, leaving 29 style axes. Reports per-axis |
| top terms from V^T. |
| - `analyses/content_engagement.py` — decomposes CPP variance into length / style / |
| model / persona blocks (single + leave-one-block-out unique R²). |
| - `analyses/style_geometry.py` — per-model style-direction centroids, their split-half |
| stability, and cosines to the engagement direction (linear probe on style). |
| - `analyses/cluster_model_persona.py`, `analyses/length_regimes.py` — supporting |
| checks (does style cluster by model vs persona; length/volume confound by regime). |
|
|
