| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - digital-twins |
| - survey-simulation |
| - ablation-study |
| - llm-agents |
| pretty_name: Pricing AutoPipeline Outer-Loop Ablations |
| size_categories: |
| - n<1K |
| --- |
| |
| # Pricing AutoPipeline — outer-loop ablations (v2r, v4, v5, v6) |
|
|
| Full run artifacts for four component ablations of the pricing AutoPipeline described in |
| [`MikeDeng2002/from-survey-histories-to-interpretable-digital-twins`](https://github.com/MikeDeng2002/from-survey-histories-to-interpretable-digital-twins), branch [`outer-loop-ablations`](https://github.com/MikeDeng2002/from-survey-histories-to-interpretable-digital-twins/tree/outer-loop-ablations). |
|
|
| Each version freezes the entire procedure, changes exactly **one** component, and measures the |
| effect on the same endpoint. The question is which part of an "interpretable mechanism" pipeline |
| actually carries its accuracy. |
|
|
| ## Result |
|
|
| | version | component ablated | Δ vs baseline (W3 / W4) | verdict | |
| |---|---|---:|---| |
| | **v2r** | Layer 1B + Layer 2's selection judgement — keeps the two channels R1 ranked *lowest* instead of its two highest | −.0069 / −.0208 | no difference | |
| | **v4** | Layer 3's compiled runtime rule | −.0023 / −.0023 | no difference | |
| | **v5** | Layer 1A's *choice* of anchors — random draw from the answer-blind catalog, 3 seeds | **−.0625 / −.0563** | **worse** | |
| | **v6** | Layer 1A's *semantic frame* — construct names, definitions, directional hypotheses, scopes | +.0185 / +.0139 | no difference | |
|
|
| **One component of four carries the effect.** Swapping which channels are kept, deleting the |
| compiled mechanism, and stripping every semantic label all leave the endpoint where it was. |
| Replacing the chosen anchors with random source questions costs six points and falls *below* the |
| raw-transcript baseline. |
|
|
| Removing the compiled rule does not even change the simulator's directional behaviour: mean |
| absolute anchor-to-prediction correlation runs .297 → .284 → .276 across v0, v4 and v6, while the |
| matching correlation in the human data is near zero and sign-unstable. |
|
|
| So in this testbed the pipeline performs **evidence location and presentation**, not mechanism |
| learning. The minimal method that reproduces it is: pick roughly seven consumption-relevant survey |
| items, extract this respondent's answers to them, show the simulator those answers. |
|
|
| ### Endpoint definition |
|
|
| Channel-only exact purchase accuracy on 18 validation respondents × 12 validation products = 216 |
| cells, scored as the pair mean of two byte-identical repeats, judged at δ = .03 with a |
| persona-clustered bootstrap CI (10,000 draws, resampling respondents rather than cells). |
|
|
| **Power:** SE ≈ .027. A "no difference" verdict rules out effects of about .05 or larger; it does |
| not rule out smaller ones. Twelve versions have been compared against this validation set, so the |
| pure-noise expected maximum gain is ≈ .060 — none of these results should be read as an |
| improvement. |
|
|
| ## Models |
|
|
| | role | model | |
| |---|---| |
| | simulator | `gpt-5.4-nano`, `reasoning_effort=high`, OpenAI Batch | |
| | discovery layers (1A / critic / 2 / 3) | `gpt-5.6-terra` | |
|
|
| Held fixed across every version so that cross-version comparisons stay meaningful. |
|
|
| ## ⚠️ Sensitive content — read before use |
|
|
| This dataset contains material the GitHub release deliberately excludes: |
|
|
| - **full persona transcripts** — the RAW+ADD arms carry each respondent's complete survey history |
| (~96,000 characters per prompt) inside `requests.jsonl` |
| - **respondent identifiers** — `persona_id` throughout |
| - **human answers** — `sidecar.json` items carry `wave3_answer` and `wave4_answer`; `cells_*.csv` |
| carries `predicted` plus `correct_wave3/4`, from which the human answer is recoverable |
| - **model outputs** — `batch_output.jsonl` |
|
|
| These are pseudonymous respondents from a public research panel, not identified individuals, and |
| this release inherits the CC BY 4.0 terms of the upstream dataset. Handle accordingly. |
|
|
| ## Provenance and licence |
|
|
| Derived from [`LLM-Digital-Twin/Twin-2K-500`](https://huggingface.co/datasets/LLM-Digital-Twin/Twin-2K-500) |
| (CC BY 4.0) by way of |
| [`Mike-deng-2002/pricing_and_cognitive_bias_experiment`](https://huggingface.co/datasets/Mike-deng-2002/pricing_and_cognitive_bias_experiment), |
| which holds the v0 baseline run these ablations are built from. Every prompt here is a v0 archived |
| prompt with one component swapped or removed. Released under **CC BY 4.0**; please cite the |
| upstream dataset. |
|
|
| ## Sealed final test |
|
|
| The split manifest reserves **12 respondents × 8 products** as an untouched final test. **No cell |
| of it appears anywhere in this dataset.** The guards were mechanical rather than by convention: |
| builders assert on sealed ids, the reporting script exits if the validation sets intersect them, |
| and the input bundle omits their labels entirely so no script had anything to score against. |
|
|
| If you evaluate on it, say so — once its results inform a change it stops being a final test. |
|
|
| ## Files |
|
|
| ``` |
| shared/ SPLIT_MANIFEST_v2.json the frozen three-way split (ids only, self-hashed) |
| ENDPOINTS_v0/v1/v2.json aggregate endpoint tables with bootstrap CIs |
| channel_registry_v0.json Layer 1A's six channels (also public on GitHub) |
| evidence_bank_v0.json 20 development respondents' answers to the anchors |
| evidence_states_dev.csv parsed numeric values per respondent per anchor |
| labels_prices.json prices and both waves' answers, D_dev ∪ D_val only |
| |
| v2r_v3r/ requests.jsonl sidecar.json cells_rerun.csv batch_output.jsonl batch_state.json |
| l3_compile_result.json ×2 terra's Layer 3 compiles for both channel pairs |
| v4/ requests.jsonl sidecar.json cells_v4.csv batch_output.jsonl batch_state.json |
| v5/ requests.jsonl sidecar.json cells_v5.csv batch_output.jsonl batch_state.json |
| draws.json 3 seeded anchor draws + terra's labels and compiles |
| v6/ requests.jsonl sidecar.json cells_v6.csv batch_output.jsonl batch_state.json |
| ``` |
|
|
| `cells_*.csv` is the scored unit: one row per respondent × product × arm, with `correct_wave3`, |
| `correct_wave4` and `valid`. |
|
|
| ## Reproducing |
|
|
| Code, protocol, per-version hypothesis cards, freeze specifications and ledgers live on branch |
| [`outer-loop-ablations`](https://github.com/MikeDeng2002/from-survey-histories-to-interpretable-digital-twins/tree/outer-loop-ablations); start from |
| [`runs/RUNNING.md`](https://github.com/MikeDeng2002/from-survey-histories-to-interpretable-digital-twins/tree/outer-loop-ablations/runs/RUNNING.md). The builders are deterministic — it records the expected |
| `input_sha256` for each version, so a fresh checkout can be verified before spending anything on |
| the API. |
|
|
| | version | `input_sha256` | requests | |
| |---|---|---:| |
| | v2r + v3r | `e62a771f05823e5b48e598817b81433a477a29c3038c0a20daaeb98d3a8beb00` | 144 | |
| | v4 | `b92ccc91d483bc6be2b58960a94127111d39a11a8e972ba5e025998a9d6ac658` | 72 | |
| | v5 | `b0fa632e150025c0bda6e1e51ba01da24108e743dc765fffccfab29743a0f489` | 144 | |
| | v6 | `3088329d096644f46e0dd5a34c3cdd1a8150a3259b040410c0698ba3ab61c7ee` | 72 | |
|
|
