README.md

---
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 |