Deep Research

Gate 3: Deep Research

Purpose: Gather evidence to fill knowledge gaps and ground the decision in facts.

Announce: "Moving to Research Gate - let's gather evidence."

Entry Criteria

• Landscape Gate completed
• Unknown-Knowables identified

Research Sources

Use all available sources:

1. Conversation - Ask human for internal knowledge, context, documents
2. Documents - Analyze provided reports, financials, memos
3. Web research - Search for market data, competitor info, industry analysis

Process

1. Prioritize Research Questions

Review Unknown-Knowables from Landscape Gate. Prioritize by:

• Impact on decision (high impact first)
• Feasibility of finding answer
• Time available

2. Gather Evidence

For each research question:

Ask human first:

• "Do you have internal data on [X]?"
• "Is there a document that covers [Y]?"

Then search externally:

• Market data and trends
• Competitor analysis
• Industry reports
• Academic research
• News and recent developments

3. Find Reference Class (Kahneman)

Critical for countering overconfidence.

Ask:

• What similar decisions have been made before?
• What's the base rate of success for decisions like this?
• What happened to others who made this choice?

Example: "M&A deals in this sector have a 60% failure rate in achieving projected synergies."

4. Evaluate Evidence Quality

For each piece of evidence, assess:

• Source credibility: Who produced this? What's their bias?
• Recency: Is this current or outdated?
• Relevance: Does this directly apply to our situation?
• Methodology: How was this data gathered?

Use deliberate-decisions:evidence-evaluation for detailed assessment.

Depth by Weight

Light: Rely on user's domain knowledge. Ask if they know the reference class. Skip external research unless user lacks knowledge.

Medium: 1-2 targeted web searches for key unknowns. Find basic reference class data. Standard evidence assessment.

Complete: Thorough external research. Multiple sources for key questions. Deep reference class analysis. Full evidence quality evaluation.

Upgrade Detection

Suggest upgrading if:

• Reference class reveals higher risk than expected
• Research uncovers surprising/contradictory information
• Key unknowns remain after Light research
• User's domain knowledge has significant gaps

Upgrade prompt:

⚠️ Research is revealing [unexpected findings]:
- [Finding 1]
- [Finding 2]

This suggests the decision may be higher-stakes than initially framed.

Current: [Weight]
Suggested: [Higher Weight] - would allow [deeper research / more sources]

Continue at current depth, or upgrade?

Output

Create research notes:

# Research Notes: [Decision]

## Research Questions

| Question | Priority | Status |
|----------|----------|--------|
| [Unknown-Knowable 1] | High | Researched |
| [Unknown-Knowable 2] | Medium | Partial |

## Findings

### [Topic 1]

**Finding:** [what we learned]
**Source:** [citation]
**Quality:** [high/medium/low]
**Relevance:** [direct/indirect]

### [Topic 2]
...

## Reference Class

**Similar decisions:** [examples]
**Base rate:** [success rate for this type of decision]
**Key lessons:** [what others learned]

## Remaining Gaps

- [Questions we couldn't answer]
- [Areas needing more research]

Save to: docs/decisions/YYYY-MM-DD-<decision-slug>/research-notes.md

Exit Criteria

• High-priority Unknown-Knowables researched (depth per weight)
• Reference class identified (depth per weight)
• Evidence quality assessed (depth per weight)
• Remaining gaps documented

Bias Watch

Watch for:

• Confirmation bias - Only seeking evidence that supports preferred option
• Availability bias - Over-weighting easily found information
• Authority bias - Accepting claims because of source prestige

Actively search for disconfirming evidence.

Next Gate

Proceed to: deliberate-decisions:calibration

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