4.1 KiB
Phase Transition
Record that the project has moved from:
Interface Design → Facilitated Investigation → Behavioural Architecture → System Architecture
Future work should validate these layers rather than introduce new ones.
Emerging Direction — Graph as Source of Truth
The first UX experiments focused on workspace structure.
The next series will focus on investigation rhythm and behaviour.
Future experiments should explore:
- how conversations unfold (behavioural, not visual)
- how understanding evolves across turns
- how the facilitator selects its behavioural response
- how confidence is gradually built through action, not description
- what state assessment enables better question selection
The objective is no longer to arrange cards or translate panels.
The objective is to make each turn of the investigation feel like a natural step in a guided thinking process.
The objective is to make the investigation feel like a natural facilitated conversation.
Experiment 21 — Question Relevance Against Decision Target
Hypothesis
Does giving the classifier an explicit decision target allow it to distinguish questions that could change the decision from questions that are merely useful or incidental?
This is one question. Nothing else matters until this is answered.
Scope
A pure function assessQuestionRelevanceToDecision({ decisionTarget, unknown, graph }) implementing four deterministic rules:
- could_change_decision — The question directly mirrors the decision's core action (e.g., "whether to enter", "should we launch", "whether there is [demand/market/need]") AND the decision target contains a matching action keyword. Answering could reasonably reverse the proposed action.
- supports_decision — Necessary precondition (e.g., compliance, cost feasibility) OR supporting context (e.g., differentiation, competitive position). The answer would improve confidence or evidence but is less likely to reverse the decision alone.
- unlikely_to_change_decision — Background detail or comparative reference that does not affect the decision conditions.
- cannot_determine — Decision target or unknown is missing, empty, or too unclear to compare honestly.
The classifier is passive — validated only against mock scenario fixtures. No changes to: graph construction, question importance classifier, unknown selection, question selection, prompts, Ollama integration, APIs, UI, state assessment, behaviour selection, conversation output, or engine behaviour in any way.
Decision Target
For the long-investigation scenario, use an explicit target from the fixture:
Should we enter the European market with our SaaS analytics platform?
Do not attempt to discover the decision target automatically. For this experiment, the decision target is supplied by the test fixture.
Evaluation
Run the classifier passively across the same long-investigation turns used in Experiment 20 (turns 0–3). Record per-turn classification. Compare with Experiment 20 results. Expect at least two distinct categories — not a collapse to one.
Questions
- Does providing an explicit decision target enable more useful distinctions than keyword-only matching?
- Do the four categories map intuitively to how a human evaluator would judge relevance?
- Or does the deterministic rule set still miss cases that appear obviously important?
Experiment 22 — Question Relevance Against Explicit Decision Conditions
Explicit decision conditions were supplied:
- Credible customer demand exists in Europe
- European compliance is achievable
- The expected market value justifies the cost of entry
- The product offers sufficient competitive differentiation
Each long-investigation unknown matched a different deciding condition. All four correctly classified as tests_deciding_condition.
Category variety is not automatically a measure of quality — here, uniformity (all four as decisive) is correct because each question directly tests a required condition.
The classifier remains passive and is not in the active reasoning path.