HUMMBL-Unified-Tier-Framework

Researchers Guide

Home > Guides > Researchers

For Academic & Applied Researchers

This guide outlines research opportunities and methodologies for validating and extending the HUMMBL Framework.

Research Opportunities

Empirical Validation

BASE120 Model Validation:

Current status: 9.1/10 quality (October 2025)
Opportunities: Field testing, refinement
Methods: Case studies, comparative analysis

Tier Classification Reliability:

Inter-rater reliability studies
Test-retest stability
Construct validity assessment

Base-N Selection Effectiveness:

Cognitive load measurements
Learning outcome studies
Expert performance comparisons

Theoretical Extensions

New Problem Dimensions:

Beyond current 5 questions
Domain-specific assessments
Cultural variations

Learning Progression:

Developmental trajectories
Transfer effects
Expertise acceleration

Integration Studies:

Combination with existing frameworks
Meta-framework development
Cross-domain applications

Research Methods

Quantitative Approaches

Survey Research:

Wickedness scoring reliability
Base-N usage patterns
Practitioner outcomes

Experimental Studies:

Training effectiveness
Decision quality improvements
Performance outcomes

Longitudinal Studies:

Learning progression tracking
Skill development patterns
Long-term impact

Qualitative Approaches

Case Study Research:

Deep dives into specific applications
Process documentation
Outcome analysis

Ethnographic Studies:

Practitioner communities
Implementation contexts
Adaptation patterns

Interview Research:

Expert perspectives
Novice learning experiences
Implementation challenges

Current Validation Evidence

BASE120 Validation (October 31, 2025):

Models Validated: 120
Quality Score: 9.1/10 average
Transformations: 6 complete
Status: ✅ Release-ready

See: Full Validation Evidence

Research Collaboration

HUMMBL, LLC Partnership Opportunities

Areas of Interest:

Empirical validation studies
Domain-specific adaptations
Training effectiveness research
Tool development

Contact:

GitLab: HUMMBL Dev Project
GitHub: Framework Repository

Publication Opportunities

Academic Venues

Relevant Journals:

Decision Sciences
Organizational Behavior
Complexity Theory
Problem Solving Research
Educational Psychology

Conference Presentations:

Decision Sciences Institute
Academy of Management
Complexity Science conferences
Educational research forums

Practitioner Publications

Professional Journals:

Harvard Business Review
Sloan Management Review
McKinsey Quarterly
Domain-specific publications

Citation

Citing the Framework

APA Style:

Bowlby, R. (2025). HUMMBL Unified Tier Framework v1.0: Integrating Problem 
Complexity, Learning Progression, and Base-N Architecture. HUMMBL, LLC.

BibTeX:

@techreport{bowlby2025hummbl,
  title={HUMMBL Unified Tier Framework v1.0: Integrating Problem Complexity, 
         Learning Progression, and Base-N Architecture},
  author={Bowlby, Reuben},
  year={2025},
  institution={HUMMBL, LLC}
}

Attribution Requirements

See Attribution page for complete details on:

Framework components
Academic foundations
AI contributions
Proprietary elements

Research Ethics

When Conducting Studies:

Obtain necessary IRB approvals
Respect intellectual property
Provide appropriate attribution
Share findings with community

When Publishing:

Credit framework appropriately
Acknowledge prior work
Share methodology openly
Contribute back to framework

Funding Opportunities

Potential Sources:

NSF Decision, Risk & Management Sciences
Organizational research grants
Educational research funding
Foundation grants for complexity research