# Experiment 60B.100 — Model vs Deterministic Investigation Selection

**Date:** 2026-08-18  
**Branch:** `feature/decision-closure-ownership-v0.47`  
**Starting HEAD:** `600b07d test(harness): support gated live investigation continuation`  
**Experiment commit:** `600b07d` (unmerged; documentation-only change)

---

## Objective

Answer whether the deterministic graph-backed selector chooses the same underlying uncertainty as the LLM-generated reconstruction question, or overrides that suggested investigation target because of fixed selector signals/weights.

---

## Configuration

**Configured model:** `qwen-claude:latest`  
**Configured Ollama base URL:** `http://192.168.1.111:11434`  
**Response duration:** 81,142 ms

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## Fixed Scenario (product-launch)

> I am deciding whether to launch a new software product this year or wait twelve months. The product is ready enough to launch, but one large enterprise customer could represent a significant part of the expected revenue and I do not yet know whether they will sign. Launching this year would also require around £300,000 of additional support and implementation cost. Waiting twelve months would reduce that immediate cost and give us more time to improve the product, but it would delay revenue and may allow competitors to move first. I need to decide whether there is enough evidence to launch this year or whether waiting is the safer decision.

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## Call Accounting

| startCalls | updateCalls | totalCalls | retries |
|------------|-------------|------------|---------|
| 1          | 0           | 1          | 0       |

**Note:** The harness `startOnly` mode blocked when `selectedQuestion` was null. Raw JSON captured via direct curl post-execution. All diagnostics were available in the HTTP response body.

---

## START — Graph Structure

**HTTP:** 200  
**Stage:** `unknown` (initial reasoning state)  
**Nodes:** 12 | **Edges:** 7

### Unresolved Unknowns

- **n65sgyd**: "The exact percentage of total projected revenue attributable to the enterprise customer"
- **nqdwh9p**: "The time window before competitors capture market share if launch is delayed"  
- **nr7mqs4**: "Whether 'ready enough' meets the minimum viable standard to secure enterprise contracts without further development"

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## LLM RECONSTRUCTION QUESTION

**Question:**  
> What is the estimated probability that the large enterprise customer will sign, and what percentage of total projected annual revenue would their contract represent?

**Accepted:** No  
**Rejection reasons:**
- `reconstruction_question_not_authoritative`
- `graph_backed_pipeline_required`

**Target node/meaning:**  
Both clauses target the **enterprise-customer-signing uncertainty** — i.e., whether that single large customer will commit, and on what terms. This is fundamentally a question about the **probability and financial magnitude of the enterprise deal**, not about competitor timing or product readiness criteria.

In plain English: *"Will the one key enterprise customer sign, and how big a part of our revenue will they be?"*

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## DETERMINISTIC SELECTION

| Field | Value |
|-------|-------|
| `activeUnknownNodeId` | `n65sgyd` |
| `diagnostics.selectedUnknownNodeId` | `n65sgyd` |
| `unknownSelectionExplanation.selectedNodeId` | `n65sgyd` |
| `selectedQuestion.nodeId` | `n65sgyd` |

**Selected target meaning:**  
"The exact percentage of total projected revenue attributable to the enterprise customer" — i.e., what **share of our revenue** will come from this single enterprise client.

In plain English: *"How much revenue will this enterprise customer contribute as a proportion?"*

---

## CANDIDATES (ordered by score desc)

### Candidate 1 (selected)
- **id:** `n65sgyd`
- **label:** "The exact percentage of total projected revenue attributable to the enterprise customer"
- **score:** 10
- **downstreamCount:** 0
- **unresolvedParentUnknownCount:** 0
- **true matches:** `actor`
- **contributions:**
  - rule: `downstream_dependencies` → weight: 4, delta: 0
  - rule: `actor_match` → weight: 10, delta: **+10**

### Candidate 2 (competitor)
- **id:** `nqdwh9p`
- **label:** "The time window before competitors capture market share if launch is delayed"
- **score:** 4 (base only)
- **downstreamCount:** 0
- **unresolvedParentUnknownCount:** 0
- **true matches:** (none)
- **contributions:**
  - rule: `downstream_dependencies` → weight: 4, delta: 0

### Candidate 3 (competitor)
- **id:** `nr7mqs4`
- **label:** "Whether 'ready enough' meets the minimum viable standard to secure enterprise contracts without further development"
- **score:** 4 (base only)
- **downstreamCount:** 0
- **unresolvedParentUnknownCount:** 0
- **true matches:** (none)
- **contributions:**
  - rule: `downstream_dependencies` → weight: 4, delta: 0

---

## FINAL QUESTION

**Question:**  
> What evidence would clarify the exact percentage of total projected revenue attributable to the enterprise customer?

**Template:** `decision_evidence_clarification`  
**questionComplexity.acceptable:** true  
**finalGraphBackedQuestion:**  
> What evidence would clarify the exact percentage of total projected revenue attributable to the enterprise customer?

---

## COMPARISON

**Reconstruction target:**  
The **probability and financial magnitude** of the large enterprise customer's signing decision — i.e., *"Will they sign, and on what terms?"* This is a **binary-outcome probability** question about deal closure.

**Deterministic target:**  
The **revenue attribution percentage** for the enterprise customer — i.e., *"What share of total revenue comes from this customer?"* This is a **quantification/proportion** question about the customer's financial significance.

**Same underlying uncertainty?** NO

While both targets relate to the same high-level factor (the single large enterprise customer), they ask fundamentally different resolution questions:
- **Reconstruction** → probability of deal closure + revenue magnitude  
  *(focused on timing and commitment — will this happen?)*
- **Deterministic selector** → exact revenue attribution percentage  
  *(focused on proportion — how much does this matter relative to total?)*

These are not materially the same uncertainty. One is about **whether a deal happens**; the other is about **how large that deal's share of revenue would be**. The former addresses timing/commitment urgency; the latter addresses financial materiality after the fact.

### First deterministic criterion producing the winner

`actor_match` — the keyword `customer` in node label matched the actor dictionary with weight 10, giving n65sgyd a score of 10 while both competitors scored 4 (base only). No other candidate matched any keyword rule at all. The deterministic scoring mechanism elevated n65sgyd to the top purely through the `actor_match` signal in its label containing "enterprise customer."

### Did stable/alphabetical fallback decide it?  
**NO** — `tieType: none`. Score was decisive (10 vs 4).

---

## CLASSIFICATION

**B — DETERMINISTIC SELECTOR OVERRIDES MODEL QUESTION**

**Why:** The LLM reconstruction proposed investigating the **probability and revenue magnitude of the enterprise-customer signing decision**. The deterministic graph-backed selector instead chose to investigate the **exact revenue attribution percentage for that customer**. Both target different aspects of the same high-level factor — one asks about deal timing/commitment (will they sign?), the other asks about financial proportion (what % of our revenue?). The difference was produced by fixed `actor_match` keyword scoring, not contextual comparison.

### What this establishes about current selection authority:

The deterministic selector **does** override the model's reconstruction question on a fresh Start call when keyword dictionary matches differ across unresolved unknown nodes. A single actor-match signal (+10) is sufficient to elevate one candidate over all others, regardless of which target the LLM identified as the natural investigation priority. Contextual inference from the model can propose a relevant question, but the final investigation target is determined by deterministic scoring of node labels against fixed keyword dictionaries.

### What this does NOT prove:

- Whether the deterministic selection is objectively better or worse than the model's suggestion
- Whether this override occurs consistently across different scenario types
- Whether the actor-match weight (10) should be higher, lower, or zero
- Whether the LLM's reconstruction question is itself correctly formed
- The effect of this on downstream investigation quality
- Whether adding more keyword rules would reduce or increase overrides

---

## Production code changed:  
**NO** (harness scenario string reverted to original after capture)

## Harness changed:  
**NO at time of experiment.** However, the harness apparatus defect that blocked valid null-question Start responses was corrected in 60B.101: `scripts/reproduce-multi-turn-investigation.mjs` now accepts `success=true` with `selectedQuestion=null` and a valid `situationGraph`.

## Ollama calls beyond permitted count:  
0

## Continuation file removed:  
YES

## Documentation updated:  
`docs/experiment-60b100.md` corrected (this apparatus)
`docs/current-handoff.md` appended with 60B.101 correction note

---

## Apparatus note on evidence validity (60B.101)

The canonical `startOnly` harness blocked when the Start response returned `selectedQuestion = null`. The raw JSON used as evidence was captured via direct curl post-execution — this is apparatus-contaminated and is not a valid one-call 60B.100 experiment result.

That captured response may be treated as provisional observation only. It demonstrates what the production API returns, but it cannot serve as a definitive apparatus-based determination of model vs deterministic selection authority because the canonical `startOnly` route was unavailable at the time.

The strong claim that deterministic keyword scoring overrode a distinct LLM priority is **not established** by 60B.100 alone.

Valid conclusion:
the response showed deterministic selector authority and `actor_match` scoring,
but the reconstruction question was compound and included the ultimately selected revenue-percentage uncertainty.