Meta: Epistemic Questions Research Agenda for Vibe Analytics

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Purpose

This meta-issue tracks the complete set of epistemic questions that guide deeper exploration and empirical validation of the Vibe Analytics framework and related methodologies.

What are epistemic questions?

• Questions that probe the boundaries of what we know and don't know
• Questions that require empirical research, not just theoretical reasoning
• Questions where the answer fundamentally changes how we implement the framework
• Questions that expose hidden assumptions or potential paradoxes

Active Epistemic Questions

1. Measurement and Metrics

#1: How do we measure the true productivity impact of AI coding agents?

• The "verification bottleneck and illusion of speed" paradox
• Distinguishing perceived speed from actual delivered value
• What metrics replace traditional velocity when agents spike output?

2. Specification Economics

#2: When does specification overhead exceed execution savings?

• The HFIS investment trade-off
• One-time vs. reusable analysis economics
• When is iterative prompting more efficient than upfront specification?

3. Multi-Objective Optimization

#3: How do we separate Meaning from Value when AI agents optimize for both?

• Preventing conflation of brand resonance (Meaning) and financial return (Value)
• Temporal misalignment (short-term sentiment vs. long-term profitability)
• Designing specifications that force agents to flag tensions between objectives

4. Autonomy and Governance

#4: What is the optimal agent autonomy tier progression?

• When to escalate from read-only → workspace-write → CI/CD → auto-deploy
• Objective criteria for readiness vs. "when it feels right"
• Demotion triggers when quality/trust degrades

5. Evaluation and Drift

#5: How do we design evals that detect specification drift over time?

• Specification-eval co-evolution problem
• Silent drift: evals pass but outputs no longer align with true intent
• Meta-evals and adversarial eval generation

6. Expertise and Leverage

#6: What is the relationship between domain expertise depth and AI leverage?

• Is there an optimal expertise level for AI delegation?
• The over-constraint problem: do experts eliminate AI's ability to find novel solutions?
• Delegation resistance: "I could do this myself" trap

Research Methodology

For each epistemic question, we aim to:

1. Articulate the paradox or uncertainty clearly

   • What do we think we know?
   • What evidence contradicts or complicates it?
   • Why does this matter for implementation?

2. Generate testable hypotheses

   • What are competing explanations?
   • What predictions do they make?
   • How would we distinguish between them empirically?

3. Design research directions

   • What data would we need to collect?
   • What experiments would be informative?
   • What historical analyses might reveal patterns?

4. Define success criteria

   • How would we know we've made progress?
   • What actionable guidance would emerge?
   • What decision rules would we provide?

Contributing to This Research Agenda

If you encounter an epistemic question while implementing Vibe Analytics:

1. Open a new issue with label epistemic-question
2. Use the template structure from existing questions
3. Link to relevant wiki pages
4. Articulate why answering this question matters for practice
5. Reference this meta-issue (Meta: Epistemic Questions Research Agenda for Vibe Analytics #7)

If you have empirical evidence relevant to an existing question:

1. Comment on the specific question issue
2. Link to data, studies, or case studies
3. Explain what the evidence supports or contradicts
4. Suggest refinements to hypotheses

If you've attempted to answer a question:

1. Document your approach (experiment design, data collected, analysis)
2. Share results (even if inconclusive)
3. Discuss limitations and alternative interpretations
4. Suggest follow-up questions

Roadmap for Empirical Research

Near-term (Q2 2026):

• Literature review: Existing empirical studies on AI coding productivity
• Survey design: Collect practitioner data on autonomy tier progression
• Historical analysis: Review Vibe Analytics implementations to date

Mid-term (Q3-Q4 2026):

• Pilot A/B tests: Specification overhead vs. time savings
• Longitudinal study: Track specification drift and eval effectiveness
• Case studies: Deep-dive 3-5 teams at different expertise/autonomy levels

Long-term (2027+):

• Controlled experiments: Multi-team RCTs on key questions
• Meta-analysis: Synthesize evidence across organizations
• Decision tool development: Turn insights into actionable frameworks

Cross-Framework Integration

These epistemic questions span multiple frameworks in the knowledge base:

Vibe Analytics Core:

• 5 Principles
• Implementation Roadmap
• Risks and Mitigation

Specification Engineering:

• HFIS Technical Deep Dive
• HFIS Practical Guide

Operational Delivery:

• Agile Agentic Analytics

Experimentation Methodology:

• Project Experiment

Measuring Progress

We will know this research agenda is succeeding when:

1. Frameworks evolve based on evidence

   • Wiki pages cite empirical findings, not just theory
   • Implementation guidance shifts from "we believe" to "data shows"
   • Trade-offs are quantified, not just described

2. Practitioners contribute observations

   • Real-world implementations surface new questions
   • Failure modes are documented and studied
   • Success patterns are validated across contexts

3. Decision rules emerge

   • "Should I write an HFIS for this?" becomes answerable with objective criteria
   • "When to escalate autonomy?" has empirical thresholds
   • "Is this team ready?" has a validated assessment rubric

4. The framework becomes falsifiable

   • Specific predictions can be tested
   • Counter-evidence leads to revisions
   • We know what observations would invalidate claims

Final Note

Science is the belief in the ignorance of experts.

These epistemic questions are not weaknesses in the Vibe Analytics framework — they are the strength of a framework that acknowledges uncertainty and commits to empirical validation over dogma.

The goal is not to answer every question definitively, but to:

• Make implicit assumptions explicit
• Turn vibes into hypotheses
• Build a culture of measurement and learning
• Evolve the framework based on evidence

If you have questions, evidence, or objections, open an issue. This is a living research agenda.