Persona Sim

Persona Sim

Simulate feedback from census-grounded virtual populations. Five layers, each independently replaceable:

L5 Scenario adapters (product-feedback-sim / vote-predict / social-sandbox)   thin wrappers
L4 Simulation engine (sim_engine.py — SGO: panel -> score -> persuadable middle -> anchored gradient)
L3 LLM router        (llm_router.py — config-driven; default Haiku 4.5 + prompt cache; Sonnet for gradient)
L2 Persona sampler   (sampler.py    — unified sample_personas(n, filters, source, mode))
L1 Persona store     (~/.claude/data/personas/ + manifest.json)

First-time setup (not optional)

Dependencies and config live outside the skill under ~/.claude/data/personas/ so sharing the skill never leaks keys. See SETUP.md for the full walk-through. Short version:

mkdir -p ~/.claude/data/personas
cp ~/.claude/skills/persona-sim/data/config.example.json ~/.claude/data/personas/config.json
cp ~/.claude/skills/persona-sim/data/manifest.json ~/.claude/data/personas/manifest.json
# Edit config.json — set `provider` and fill api_key / base_url for your chosen block

python3 -m venv ~/.claude/data/personas/.venv
~/.claude/data/personas/.venv/bin/pip install -r ~/.claude/skills/persona-sim/requirements.txt

Always invoke with the venv interpreter:

~/.claude/data/personas/.venv/bin/python <script>

When to use

• "Score this copy/feature with 100 virtual target users" → sim_engine.panel_score()
• "Find the persuadable middle for this tweet" → sim_engine.sgo()
• "Predict the US opinion distribution for this policy" → panel_score() + segment breakdown
• "Which of these two pricing variants wins?" → sgo() with anchored counterfactual probes

In-code API

from persona_sim import sampler, sim_engine

# Sample 30 targeted personas (streaming — no local download needed)
panel = sampler.sample_personas(
    n=30,
    filters={"occupation_isco": "software", "age": (22, 55)},  # see filter semantics below
    source="nemotron_usa",
    mode="stream",
)

# Single-version scoring
result = sim_engine.panel_score(panel, target="Copy or product description here")
# -> {"n", "results", "aggregate": {"histogram", "median", "iqr", "entropy",
#    "multi_modal", "disagreement_flag", "by_gender", "by_age", "by_region"},
#    "warning": "Simulation only..."}

# SGO gradient — rank candidates by how much they shift the persuadable middle
ranked = sim_engine.sgo(
    panel,
    target="original version",
    candidates=["variant A", "variant B"],
    goal="maximize paid conversions",
)
# -> {"base", "persuadable_middle", "ranking": [{"candidate", "avg_score_lift", ...}]}

Filter semantics (commonly misused)

sample_personas(filters={...}) interprets values by Python type:

Missing field on row (rv is None) → row rejected.

Default adults_only=True injects age=(18, 120) unless you pass your own age filter. Nemotron data includes children with synthetic professional_persona fields — always filter them out for work/product panels.

Six non-obvious rules (READ BEFORE ANY CHANGE)

1. Narrative > demographic stub. Park et al. 2024 proved demographic-only prompts amplify stereotypes. _persona_card puts Nemotron's long professional_persona narrative FIRST; demographics are a footnote.
2. Never output a single mean. Output distribution + segment breakdown + disagreement_flag. High variance = signal, not noise.
3. Prompt caching is mandatory for panels. System + task are identical across N personas; with cache_read at 10% of base, a 50-persona panel costs ~1/10 of naive. Cache breakpoint goes BEFORE the persona card (which varies per row).
4. SGO uses ANCHORED counterfactuals. The probe re-injects the persona's original score + reason so the delta is causal (attribution to the change), not two independent scorings. See _anchored_delta_one in sim_engine.py.
5. Persuadable middle = score 4–7 only. 1–3 never buys; 8+ already sold. Don't waste LLM calls on extremes.
6. Calibration disclaimer is required. Tjuatja 2311.04076 + Bisbee 2024 proved LLM synthetic samples lack human response biases (acquiescence, anchoring, framing). Every public-facing result must include the warning field.

Empirical validation

Smoke-tested on Nemotron-USA + Haiku 4.5:

Same product, different panel → median shifts from 2 to 7. Personas play roles faithfully; single dissenter at 3 had concrete reasoning. SGO has enough signal when targeting is correct.

LLM routing

Active provider set in ~/.claude/data/personas/config.json → provider field. Three shapes supported:

• anthropic (native) — Anthropic SDK with ANTHROPIC_API_KEY; supports prompt caching
• anthropic with base_url override — for custom Anthropic-compatible gateways (enterprise proxies, Vertex-routed gateways, etc.)
• openai (gateway-compatible) — OpenRouter, vLLM, Together, Groq, etc.

Escalate to gradient_model (default Sonnet 4.5) for:

• SGO anchored counterfactual (fine-grained semantic judgment)
• Bias audit probes (framing / order / authority)
• Long-narrative fidelity eval

See data/config.example.json for template config blocks.

Data fetch flow (streaming works without any download)

MVP runs entirely on HF streaming — no local copy needed. Only fetch when you're running many panels and want the speed:

~/.claude/data/personas/.venv/bin/python -m persona_sim.fetch nemotron_usa

manifest.json per-dataset default_mode:

• local → prompts to download on first use
• stream → direct HF streaming (default for MVP)
• ask → shows license + manual-download URL

redistribute: false entries (most Nemotron / PersonaHub / WVS / CGSS) are NEVER bundled when sharing the skill — each user fetches them per license terms.

Bias audit

lib/bias_audit.py tests whether LLM personas reproduce four known human biases:

Results flagged with calibration warnings. Run before publishing any survey-style prediction.

Subagent pattern (for n > 200)

Task(subagent_type="general-purpose",
     prompt=f"Run persona_sim.panel_score on ids {ids[0:50]}, target={target!r}. Return distribution only.")

Main agent aggregates per-slice distributions to avoid context bloat.

Known gaps

• Chinese persona data pipeline is scaffolded (references/chinese_pipeline.md) but not implemented — see that file for the 3-source recipe (census + WVS + LLM narrative).
• demo_corr suite requires persona income field (not in Nemotron); currently skips that dimension.
• ANES demographic correlation reference values are approximations — re-verify against the official codebook before publishing.

Resources

• lib/{fetch,sampler,llm_router,sim_engine,aggregator,bias_audit,ipf}.py — core layers
• prompts/persona_system.md — narrative-first system prompt with anti-agreeable guardrails
• prompts/sgo_gradient.md — anchored counterfactual template
• eval/run_eval.py + eval/*.json — 6-suite score card (gss_20q, bfi44, test_retest, diversity, demo_corr, bias_audit)
• references/methodology.md — Park 2024, Santurkar, SGO, CoBRA, Tjuatja synthesis
• references/datasets.md — dataset inventory + field maps
• references/chinese_pipeline.md — scaffold for Chinese persona synthesis
• scripts/smoke_test.py, scripts/smoke_test2.py — minimal working examples
• SETUP.md — venv + config bootstrap
• requirements.txt — pinned deps
• data/manifest.json — dataset registry (safe to share)
• data/config.example.json — sanitized LLM config template

Eval loop

cd ~/.claude/skills/persona-sim
~/.claude/data/personas/.venv/bin/python eval/run_eval.py \
    --suite gss_20q,bfi44,test_retest,diversity,demo_corr,bias_audit \
    --n 30
# -> eval/reports/score_card_YYYYMMDD.json

Target: overall >= 0.75. If any dimension fails, that dimension is the highest-priority edit target for the next iteration.

Scenario skills

Three thin scenario wrappers ship alongside the core. They route into persona-sim's API with opinionated defaults for common use cases:

• product-feedback-sim — SGO ranking for product/copy/pricing candidates
• vote-predict — categorical opinion prediction with IPF post-stratification
• social-sandbox — what-if experiments with open-ended narrative clustering

Each has its own SKILL.md with a full recipe.