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ABIRU v1.1
Systematic Decision-Making Framework
Transform ad-hoc, emotion-driven decisions into systematic, model-driven analysis that consistently improves outcomes through iterative learning.
A cognitive processing system combining 40+ mental models, 5-layer architecture, and AI-human hybrid governance.
AT A GLANCE
Framework Overview
40+
MENTAL MODELS
Across 9 domains: Psychology, Economics, Mathematics, Physics, Evolution, Finance, and more
5-LAYER
COGNITIVE SYSTEM
Input → Reframing (PEM-L) → Model Deployment → Synthesis → Action with Bayesian Feedback
5
CONTEXT MODES
Exploration, Crisis, Design, Conviction, Review — each optimized for specific decisions
THE CHALLENGE
Why Most Decisions Fail
Most decision-making is reactive, emotion-driven, and inconsistent. People rely on gut instinct, repeat the same cognitive biases, and lack systematic frameworks to improve.
Reactive Decision-Making
Decisions made without structured analysis, leading to inconsistent outcomes and repeated mistakes
Hidden Cognitive Biases
Confirmation bias, recency bias, and loss aversion systematically distort judgment without awareness
No Learning System
Decisions lack feedback loops, no way to track predictions vs. outcomes or improve accuracy over time
Information Overload
Too many factors, conflicting data, and analysis paralysis prevent clear, confident decisions
WHO IT’S FOR
Built for Strategic Thinkers
Strategic Leaders
CEOs • Founders • Executives
Making high-stakes organizational decisions requiring systematic analysis and long-term thinking
Product Managers
Product Leaders • Design Strategists
Prioritizing features, evaluating trade-offs, and making data-informed product decisions
Investment Analysts
Investors • Analysts • Due Diligence
Evaluating opportunities, calculating expected value, and managing portfolio risk systematically
Consultants & Advisors
Consultants • Executive Coaches
Helping clients make better decisions through structured frameworks and bias-resistant analysis
THE SOLUTION
5-Layer Cognitive Processing Architecture
ABIRU transform decision-making into a systematic, repeatable process that combines mental modles, AI-assisted analysis, and Bayesian learning to consistently improve outcomes.
CAPTURE
Capture raw inputs (questions, data, context)
Parse explicit statements and implicit assumptions
Categorize by type and urgency
REFRAME
Challenge assumptions using systems thinking
Apply PEM-L checklist (Role, Objective, Constraints, Process, Output, Tone)
Identify leverage points for maximum impact
→ Pause-for-Approval Gate: User must approve reframed problem
ANALYZE
Select 3-5 optimal mental models from 40+ stack
Execute parallel analysis and simulations (Monte Carlo, Bayesian)
Generate model-specific insights and predictions
SYNTHESIZE
Integrate outputs using OODA loop
Cross-reference with ABIRU Constitution
Check for cognitive and strategic biases
Generate recommendation with confidence intervals
EXECUTE & LEARN
Deliver specific, actionable next steps
Define success metrics and review timeline
Log predictions for Bayesian updating
Update beliefs based on actual outcomes
WHAT MAKES ABIRU DIFFERENT
Unique Framework Advantages
Constitutional Governance
Every decision must pass 5 core principles:
Truth over Comfort, Bias Resistance, Action Through Insight, Systems Thinking, Interdisciplinary Thinking
40+ Mental Model Stack
Organized across 9 domains (Psychology, Economics, Physics, etc.).
Automatically selects optimal 3-5 models per decision
Pause-for-Approval Gate
AI proposes reframed problem → Human approves before proceeding.
Shared accountability ensures human judgment remains final
Bayesian Feedback Loop
Track predictions vs. actual outcomes, Update beliefs and model weights automatically
System improves accuracy over time through learning
TECHNICAL ARCHITECTURE
AI-Human Hybrid System
FRAMEWORK COMPONENTS
CONSTITUTION LAYER
5 governing principles filtering all decisions
MENTAL MODEL STACK
40+ models across 9 domains (Psychology → Spiritual)
PEM-L (PROMPT ENGINEERING MICRO-LAYER)
Role, Objective, Constraints, Process, Output, Tone + Success Criteria
PROTOCOLS SYSTEM
5 context modes (Exploration, Crisis, Design, etc.)
AI INTEGRATION
Claude/GPT for model deployment and simulation
MEMORY SYSTEM
Logs decisions, outcomes, bias patterns
INTERACTION MODEL
AI Role: Parse inputs, select models, run simulations, detect bias
Human Role: Provide context, approve reframing, make final decisions
DECISION-TYPE DEPLOYMENT
Optimized for Every Decision Context
ABIRU adapts by selecting optimal mental models and protocols for each situation
Career Decisions
Models: Opportunity Cost,
Expected Value, Network Effects
Protocol: Conviction Mode
Output: Long-term value
maximization with risk analysis
Strategic Planning
Models: Systems Thinking,
Leverage, Feedback Loops
Protocol: Exploration Mode
Output: Strategic roadmap
with scenario planning
Product Development
Models: First Principles,
Selection Pressure, Emergence
Protocol: Design Mode
Output: Prioritized feature
strategy with ROI justification
Crisis Management
Models: First Principles,
Constraints Theory, Trade-offs
Protocol: Crisis Mode
Output: Simplified decision tree
with essential actions only
Investment Analysis
Models: Bayesian Inference,
Expected Value, Capital Allocation
Protocol: Conviction Mode
Output: Risk-adjusted return
projections with confidence
IMPACT & OUTCOMES
Proven Framework Results
STATUS: DEPLOYED & ACTIVE
Version 1.1 released August 2025 • Continuous improvement through Bayesian feedback system
Systematic Improvement
• Reduced decision-making time through structured process
• Increased confidence intervals through bias detection
• Better predictions via Bayesian updating
• Consistent framework application across decision types
Real-World Applications
• Career transitions and opportunity evaluation
• Product feature prioritization and roadmap planning
• Investment due diligence and portfolio management
• Strategic planning and organizational decisions
Get ABIRU Framework Access
ABIRU v1.1 is proprietary intellectual property. Request access to use the framework for strategic decision-making in your organization.
Complete Documentation
Full framework documentation
40+ mental models reference
PEM-L checklist and templates
Community best practices
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Transform Your Decision-Making
Stop making reactive, biased decisions. Start using systematic frameworks that improve with every
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