experiment: test decision relevance and do-nothing baseline

docs/current-handoff.md

@@ -1995,3 +1995,11 @@ The hypothesis asked whether selecting `n_savings_realism` would now produce a c
 **Objective:** When the user provides a concrete £2m figure but explicitly states it is unverified, does the engine preserve the figure and keep the existing savings-realism uncertainty unresolved?
 
 **Classification: A — QUALIFIED EVIDENCE AND UNCERTAINTY BOTH PRESERVED.** One update-only call via the committed harness (confirmed by direct API inspection). The engine preserved the £2m/year figure as `"£2M/year (unverified)"` on n_savings_realism, kept status as `unknown` (not weakened to provisional), confidence set to low, answerMeaning.supportCategory = `"uncertain"`, possibleInference = null. No duplicate nodes, no resolved unknowns. Selected question continues investigating the realism concern. This is the best result seen for savings-realism across all 58A/B experiments — evidence and uncertainty both survive intact. Configured Ollama: qwen-claude:latest at http://192.168.1.111:11434. No production code changed.
+
+---
+
+### Experiment 59B.1 — Decision Relevance of Next Question vs Precision Chasing
+
+**Objective:** When the financial benefit is known, the downside is bounded, and the cost of doing nothing is explicit, does the engine compare decision consequences — or does it simply ask for more precision about the remaining uncertainty?
+
+**Classification: D — DO-NOTHING BASELINE LOST.** One update-only call via the committed harness. The engine resolved n_savings_realism (preserving £2m as known benefit) and created a new meta-level question "would a precise delivery-delay estimate change the decision?" which is HIGH decision relevance. However, the engine entirely lost: (a) the known consequence of two senior engineers departing, (b) the bounded downside of "no more than two months" delay, and (c) the do-nothing baseline ("staying put costs extra £2m every year"). A positive finding: the engine did NOT ask for exact delivery delay (no precision chasing). It asked whether precision matters at all — a valid decision-relevant step. However, this question is contextually hollow because the critical comparison elements are absent from the graph. This suggests a context-preservation deficit in the update path: when resolving one uncertainty and creating a new node, the engine drops other critical information from the answer rather than carrying it forward. Configured Ollama: qwen-claude:latest at http://192.168.1.111:11434. No production code changed.

docs/experiment-59b1.md

@@ -0,0 +1,138 @@
+# Experiment 59B.1 — Decision Relevance of Next Question vs Precision Chasing
+
+**Branch:** `feature/question-formulation-v0.24`
+**Date:** 2026-08-12
+**Status:** Complete
+**Following:** 59A series which showed the engine can distinguish known consequences from uncertain downstream effects at the proposal level.
+
+## Objective
+
+When the financial benefit is known, the downside is bounded, and the cost of doing nothing is explicit, does the engine compare decision consequences — or does it simply ask for more precision about the remaining uncertainty?
+
+Specifically: does the engine recognise when greater precision about an uncertainty may not actually matter to the decision?
+
+## Context route
+
+Read only:
+- `docs/current-handoff.md`
+- `docs/experiment-59a3.md` (preceding reasoning context)
+- Fixture: `tests/fixtures/pre-anchored-update-savings-realism.json`
+- Harness: `scripts/reproduce-multi-turn-investigation.mjs`
+
+Do not load older experiment history.
+
+## Fixed starting graph
+
+Fixture: `tests/fixtures/pre-anchored-update-savings-realism.json`
+
+Existing unresolved question:
+```
+n_savings_realism — Are the projected office savings from relocation realistic? — status = unknown
+```
+
+## Fixed answer
+
+```text
+The £2 million annual saving is real. We know two senior engineers would leave if we relocate. Even in the worst case, I think that would delay delivery by no more than two months. If we stay where we are, we keep paying the extra £2 million every year. I am trying to decide whether getting a more precise delivery-delay estimate would actually change the decision.
+```
+
+Contains:
+- **KNOWN BENEFIT:** £2 million annual saving
+- **KNOWN CONSEQUENCE:** two senior engineers would leave
+- **BOUNDED DOWNSIDE:** delivery delay no worse than approximately two months
+- **DO-NOTHING BASELINE:** staying put costs an extra £2 million every year
+- **DECISION-RELEVANCE QUESTION:** would greater precision about delivery delay actually change the decision?
+
+## Execution
+
+**Host/model:** qwen-claude:latest at http://192.168.1.111:11434
+**Calls:** startCalls=0, updateCalls=1, totalCalls=1
+**Retries:** 0
+
+---
+
+## Results
+
+### UPDATE output
+
+```
+HTTP status: 200
+Stage: update_applied
+Validation errors: none
+
+updatedNodes: [{nodeId:n_savings_realism, previousStatus:unknown, newStatus:resolved, newValue:'£2 million annual savings confirmed as real', reason:'User explicitly confirmed the projected office savings are real and provided exact figures.'}]
+resolvedUnknownNodeIds: ['n_savings_realism']
+addedNodes: [{id:n_delay_estimate_relevance, label:'Would a precise delivery-delay estimate change the decision?', kind:unknown, status:unknown}]
+addedEdges: [{fromNodeId:n_delay_estimate_relevance, toNodeId:n_savings_realism, relationship:depends_on}]
+selectedQuestion: 'would a precise delivery-delay estimate change the decision?'
+```
+
+Resulting graph (3 nodes, 2 edges):
+- `n_relocation_state` — Engineering team relocation consideration — status=provisional
+- `n_savings_realism` — Are the projected office savings from relocation realistic? — status=resolved ✓
+- `n_delay_estimate_relevance` — Would a precise delivery-delay estimate change the decision? — status=unknown
+
+---
+
+## Assessment
+
+### 1. £2m annual saving
+**PRESERVED AS KNOWN BENEFIT**
+Node `n_savings_realism` resolved with value `"£2 million annual savings confirmed as real"`.
+
+### 2. Two-engineer departure
+**LOST**
+No node in the graph represents "two senior engineers would leave." Not preserved structurally, not visible in any node label/description/value. The answer clearly stated this as a known consequence but it was dropped.
+
+### 3. Two-month downside bound
+**UNAVAILABLE**
+No node captures "no more than two months" or any upper bound on delivery delay. The engine did not weaken it to open-ended uncertainty explicitly, but the information is simply absent from the graph.
+
+### 4. Do-nothing baseline
+**LOST**
+"Staying put costs an extra £2m every year" is not structurally represented as a cost node, a comparison edge, or any structural element of the graph. `n_relocation_state` has no do-nothing semantics.
+
+### 5. Decision framing
+**TRADE-OFF PRESENT BUT BASELINE LOST**
+The engine selected a question about whether precision matters to the decision — this is trade-off thinking at a meta-level. However, the baseline (cost of staying put) is lost structurally, so the trade-off has no anchoring.
+
+### 6. Next-question decision relevance
+**HIGH DECISION RELEVANCE**
+"Would a precise delivery-delay estimate change the decision?" — If answered yes, it would justify further investigation; if answered no, it would stop precision-seeking. This directly addresses the user's stated concern about whether more precision is worth obtaining.
+
+### 7. Precision chasing
+**NO PRECISION CHASING**
+The engine did NOT ask "what exactly is the delivery delay?" It asked a meta-level question about decision relevance of precision itself. However, this positive result is partially undermined by the fact that critical contextual facts (engineer departure, two-month bound) were lost before the question was formulated.
+
+---
+
+## Classification: D — DO-NOTHING BASELINE LOST
+
+The engine evaluated relocation consequences without preserving the recurring cost of staying put as a structural element. Two additional losses compound this:
+- The known consequence ("two senior engineers would leave") was entirely lost from the graph.
+- The bounded downside ("no more than two months") was absent from the graph.
+
+A positive finding: the engine did **not** ask "what exactly is the delay?" — it asked whether precision matters at all, which is a valid decision-relevant next step. However, this question lacks structural grounding because the critical comparison elements (engineer loss, bounded impact, do-nothing cost) are not present in the graph to give the question context.
+
+---
+
+## What this establishes:
+
+1. The engine can formulate a genuinely meta-level decision-relevance question when prompted by an answer that explicitly raises it ("I am trying to decide whether getting a more precise delivery-delay estimate would actually change the decision").
+2. The engine does not default to precision-chasing (asking for exact values) when the user signals that decision relevance matters.
+3. Known benefit preservation works: £2m savings survived as resolved on `n_savings_realism`.
+
+## What this does NOT prove:
+
+1. That the engine would independently recognise decision irrelevance without an explicit user prompt about it — the answer text contained "I am trying to decide whether getting a more precise delivery-delay estimate would actually change the decision," which is a very strong signal that guided question selection.
+2. That the engine preserves known consequences alongside benefits — engineer departure was entirely lost.
+3. That the engine preserves bounded downside information — the two-month upper bound disappeared.
+4. Whether these losses are due to answerMeaning extraction limits, proposal generation limits, or node-kinds being misclassified.
+
+---
+
+## Key observation
+
+The engine's meta-level question framing is structurally intelligent but contextually hollow. It asked the right *kind* of question (is precision worth it?) but lost the facts that make that question meaningful (what happens if we relocate? what are the bounds? what does doing nothing cost?). This suggests a **context-preservation deficit** in the update path: when the engine resolves one uncertainty and creates a new decision-relevance node, it drops other critical information from the answer rather than carrying it forward.
+
+Production code changed: NO