First Principles in Scrum: Implementing Scrum and Agile Practices

Note to Reader

Preface

The Agile Technology Stack: From Physics to Values to AI

1. The Agile Technology Stack: An Analogy
2. Start with First Principles
3. 1. Physics Layer (Agile’s “Physical Layer”)
4. 2. Biology Layer (Agile’s “Data Link Layer”)
5. 3. Neuroscience Layer (Agile’s “Network Layer”)
6. 4. Complex Adaptive Systems (CAS) Layer (Agile’s “Transport Layer”)
7. 5. Scrum Layer (Agile’s “Session Layer”)
8. 6. Scrum@Scale Layer (Agile’s “Presentation Layer”)
9. 7. Agile Values and Principles Layer (Agile’s “Application Layer”)
10. 8. AI Layer (Agile’s “eXtreme Agile Layer”)
11. Case Study: The Dysfunctional Scrum Team and the Agile Technology Stack
12. Case Study: The Downfall of Waterfall at AT&T and Bell South
13. Conclusion: Why You Should Never Use Waterfall
14. References

The Power of Bootstraps: From Computer Science to the Universe, Human Health, Scrum and AI

1. Introduction
2. The Magic of the Digital PDP7
3. Wolfram’s Physics Project: The Universe as a Bootstrap
4. Bootstrapping Cancer: A Computational Journey into Carcinogenesis
5. Ratfor: Bootstrapping a New Programming Paradigm
6. The iRobot Experience: Bootstrapping Intelligence from Simple Rules
7. Implications for Scrum and Beyond
8. Further Reading and References

How Scrum Emerged From First Principles

1. Introduction
2. The FBI’s Triumph and the Path to Empiricism
3. Taiichi Ohno’s Sage Advice and the Pursuit of Continuous Improvement
4. Scrum’s Origin in Lean and the Dance of Complex Adaptive Systems
5. Gödel’s Theorem and Computational Irreducibility: Unraveling Certainty
6. The Cathedral Unveiled
7. The FBI’s Triumph and the Path to Empiricism
8. Taiichi Ohno and the Pursuit of Continuous Improvement
9. Gödel’s Theorem and the Power of Empiricism in Scrum
10. Computational Irreducibility and the Emergence of Scrum
11. From Biology to Scrum: The Evolutionary Paradigm
12. The Transformative Power of Scrum: Shaping the Future of Product Development
13. References

The Paradox of Inductive vs Deductive Reasoning

1. Personal Prologue
2. Abstract
3. Introduction
4. The Paradox Explored
5. Conclusion
6. References

Responding to Change - Embracing the Paradox of Prediction and Adaptation

1. Introduction to the Paradox
2. Bayesian Surprise in Agile Environments: Friston’s Free Energy Principle
3. Free Energy in AI and Robotics
4. Free Energy in Agile Environments
5. Free Energy and Scrum Patterns
6. The Paradox of Prediction: A Dialogue Between Titans
7. Resolving the Paradox: The Agile Manifesto and Minimizing the Cost of Change
8. The Underappreciated Genius of the Agile Manifesto
9. Synthesizing the Paradox in Scrum Practice Across Industries
10. Conclusion: Scrum as Evolutionary Success
11. References

Understanding the Agile Mindset through the Wisdom of Four Warriors

1. Sun Tzu’s Art of Agile Leadership
2. Miyamoto Musashi’s Dual Swords: The Agile Leader’s Balance
3. Musashi’s “The Five Approaches” in Agile Context
4. The Musashi Meditation: A Daily Practice for Agile Leaders
5. Carl von Clausewitz and Agile Leadership: Navigating the Fog of War
6. John Boyd’s OODA Loop: The Agile Mindset’s Ultimate Weapon
7. Synthesizing the Warriors’ Teachings in Agile Leadership
8. Integration of Principles into a Comprehensive Agile Mindset
9. Integrating Warrior Wisdom into Scrum and Scrum@Scale
10. Conclusion
11. References

The Physics of High-Performing Teams - Unpacking Complex Adaptive Systems, the Seven Deadly Sins, and Seven Neural Accelerators in Scrum

1. Introduction
2. The Quantum Mechanics of Scrum: Unraveling the Physics of High-Performing Teams
3. Survival in a Complex World: The Imperative of Adaptation and Innovation
4. First Principles of Complex Adaptive Systems
5. Deviating from CAS Leads to the Seven Deadly Sins
6. Incorporating Neuroscience Accelerators into Scrum Principles
7. Conclusion: Embracing First Principles for High-Performing Teams
8. References

Scrum’s Secret Sauce: The Next Best Step

1. The Complexity and Importance of Prioritization in Scrum
2. The Next Best Step: A Cognitive Perspective, Quantum Mechanics, and the Power of Observation
3. Game Theory and Decision Making in Scrum
4. Driving System Evolution via Punctuated Equilibrium and Complex Adaptive Systems
5. The Role of AI: A ChatGPT Analogy and the Emergence of Expertise
6. Leveraging AI for Decision Making
7. The Human Element in Scrum Decision Making
8. Challenges and Potential Solutions
9. Real-World Applications and Case Studies
10. Future Implications: Welcoming AI onto the Scrum Team
11. Conclusion: The Next Best Step as the Key to Survival and Success**
12. References

The Secret Sauce of Scrum: Punctuated Equilibrium

1. Abstract
2. Introduction
3. The Secret Sauce of Scrum: Empirical Process Control and Punctuated Equilibrium
4. The Next Best Step: A Legacy from the First Scrum Team
5. The Role of AI in Identifying the Next Best Step
6. Conclusion
7. References

The Fibonacci Series: Why Non-Linear Estimation Works in Scrum

1. Preamble: The Search for a “Natural” Algorithm
2. 1. The Lure of the Golden Ratio: Deconstructing Popular Myths
3. 2. The True First Principle: The Psychophysics of Human Perception
4. 3. The Historical Principle: From Cold War Forecasting to Planning Poker
5. 4. The Practical Principle: Fibonacci as a Cognitive and Social Tool
6. 5. Advanced Metaphors: Re-contextualizing Complexity Theory
7. 6. Conclusion: A Cognitive Tool, Not a Cosmic Law
8. Works cited

Early Finish, Accelerated Innovation: Harnessing the Buffer Pattern for Agile Story Completion in Product Development

1. Friston’s Free Energy Model of the Brain
2. Illigitimus Non Interruptus - Where It Comes From
3. Essence of the Buffer Pattern
4. References

Hierarchy and Autonomy in Scrum: The Influence of Holarchy and the Power of the People

1. Introduction
2. The Paradox
3. The Concept of Holarchy
4. Koestler’s Forethought: Complex Adaptive Systems in Scrum
5. Conclusion

Leveraging Scrum: Empowering Quality Excellence in Product Development

1. Introduction
2. The Evolutionary Approach of Scrum
3. Technical Debt and the Evolutionary Approach
4. The Impact of Computational Irreducibility on Scrum
5. Case Study: Systematic
6. The Scrum Framework and Quality
7. Emphasizing Early and Constant Testing
8. Shortening Delivery Time
9. Digital Management of Autonomous Teams
10. Impact of AI on Quality Assurance, Technical Debt, and Architecture
11. Continuous Customer Engagement
12. Humphrey’s Law and the Importance of Continuous Customer Engagement
13. Conclusion
14. References

Going From Average to Awesome: Why Teams That Finish Early Accelerate Faster!

1. Amazon’s Rigorous Approach: Teams on Probation and the Quest for Awesomeness
2. From Good to Great, Average to Awesome: The Quest for the Singular Data Point
3. Applying the Collins Method to Scrum Teams: A Surprising Discovery
4. Teams That Finish Early, Accelerate Faster: A Quantum Leap in Understanding Scrum
5. The Search for a Deeper Understanding
6. Friston’s Free Energy Model of Brain Function: The Key to Understanding Acceleration
7. Practical Implications for Scrum Masters
8. References

Enhancing Agile Practices: The Strategic Use of Spikes for Optimal Resource Management

1. Early History of Spikes
2. Emergence in Extreme Programming
3. Influence on Other Agile Frameworks
4. The Role of the Product Owner in Managing Spikes
5. Integrating Spike Management into Sprint Planning
6. Benefits of Strategic Spike Management
7. Example: Johns Hopkins Applied Physics Laboratory
8. Conclusion
9. References

Beyond Kaizen to Kaikaku: Two Patterns That Transform Good Scrum to Great

1. The Critical Role of One-Week Sprints
2. The Power of Integration: The Scrum Inc. Story
3. Large Organization Case Study: Microsoft’s Scrum Transformation
4. Key Lessons for Large Organizations
5. The Parallel with Scrum Inc
6. Beyond Kaizen: Toyota’s Discovery of Scrum as Kaikaku
7. Detailed Pattern Implementation
8. The Leverage Effect
9. Common Implementation Mistakes
10. References

How to Make Agile Transformations Successful: Measuring Business Agility

1. Introduction
2. If the Failure Rate is 53% Why Do Companies Do Agile Transformations
3. Business Agility via Only Agile if it Generates Business Outcomes
4. Example of How Decision Speed Affects Business Agility
5. OODA Loop Decision Metrics
6. References

Aligning Agile Product management with the Laws of Physics, Simulation of Waterfall, Kanban, and Scrum

1. Introduction
2. The First Principle: Computational Irreducibility
3. Complex Adaptive Systems and Evolution
4. Product management Methods: Waterfall, Scrum, and Kanban:
5. Product management Simulation: Waterfall, Scrum, and Kanban
6. Parameters
7. Wolfram Code and Results
8. Scaling Agile: Addressing the Seven Deadly Sins
9. Scaling Agile: Addressing the Seven Generative Neuroscience Effects
10. Collaborative Environment and Social Neuroscience
11. Iterative Progress and the Zeigarnik Effect
12. Feedback Loops and Dopamine Reward System
13. Adaptive Change and Neuroplasticity
14. Empirical Process Control and Predictive Coding
15. Mirror Neuron Effect
16. The Vagus Nerve and Emotional Regulation:
17. Conclusion
18. References

Fibonacci in the Age of Hybrid Intelligence

1. 1. The First Principle of Cognitive Orchestration
2. 2. The Biology of Hybrid Teams: Scrum@Scale as the OS
3. 3. Friston’s Law: Minimizing Surprise in High-Velocity Environments
4. 4. From Retrospective to Prospective: The Simulation Engine
5. 5. Solving the Priority Paradox: Value Density and the AI Slicer
6. 6. The Thermodynamic Paradox: The 100 Gigawatt Problem
7. 7. Future-Proofing the Framework
8. References & Further Reading

Why Scrum@Scale: Taking Scrum to the Next Level

1. Background
2. Improvements Identified After Examining Millions of Scrum Projects
3. Conclusion
4. References

Why Scrum@Scale: Using a Hybrid Approach to Get Money for Nothing and Change for Free

1. Introduction: The Need for a Dual Operating System in Agile Transformations
2. Use Case 1: Balancing Current Operations with Future Innovation
3. Use Case 2: Overcoming Suboptimization with Constraint Theory and Scrum@Scale
4. Use Case 3: Ensuring Value Delivery with Systematic Measurement and Incremental Scaling
5. Use Case 4: Achieving Enterprise Agility with Scrum@Scale - Money for Nothing, Change for Free
6. References

Why Scrum@Scale: Digital Management of Autonomous Teams

1. Introduction
2. The Impact of the Emergence of Sentient AI
3. Technological Revolutions and Financial Capital (Perez)
4. Diffusion of Innovations (Rogers)
5. Crossing the Chasm (Moore)
6. Disruptive Innovation (Christensen)
7. Converging S-Curves and the Age of AI (Seba)
8. The Role of Digital Management in Agile Organizations
9. Speed of Innovation Began in Software Decades Ago
10. Tesla’s Digital Management of Autonomous Teams
11. Scrum@Scale Supports and Enhances Digital Management
12. References

Agile Means All At Once: How Tesla Condensed 5-10 Years of Construction into 19 Months with Superior Quality

1. Executive Summary
2. Introduction: The “All-at-Once” Lineage
3. Background on Lithium in the EV Supply Chain
4. Project Overview
5. Construction Timeline: The “All at Once” Advantage
6. Innovative Technology: Higher Quality through Agility
7. Economic and Community Impact
8. Challenges and Achievements
9. Conclusion
10. References

The Protocol for the AI Daily Scrum

1. 1. Executive Summary
2. 2. Team Topology: Carbon vs. Silicon
3. 3. The Pre-Event Protocol: Asynchronous Synchronization
4. 4. The Event Protocol: The 15 Minutes
5. 5. The Role of the Scrum Master: The Interface Manager
6. 6. The “Definition of Done” for Agents
7. 7. Conclusion: The Strategic Imperative

The Sutherland Doctrine: An Analysis of Scrum Sage and the Integration of First Principles into AI-Driven Agile Management

1. Introducing Scrum Sage
2. Section 1: Executive Summary: The Emergence of the AI-Augmented Scrum Master
3. Section 2: Functional Architecture of Scrum Sage: Zen Edition V2
4. Section 3: The “Zen Edition” Philosophy: Integrating Mindfulness and Flow
5. Section 4: The First Principles: Deconstructing the Scientific Foundations of Scrum Sage
6. Section 5: Strategic Analysis and Future Outlook
7. References

Definition of Ready

1. Abstract
2. Introduction
3. Diverse Opinions on Definition of Ready
4. The Origin of Definition of Ready
5. Scrum and Complex Adaptive Systems theory (CAS)
6. The Systematic Case: Twice the Work in Half the Time
7. Balancing Readiness with Speed and Adaptability
8. The Role of Definition of Ready in Scrum
9. Benefits of Adopting Definition of Ready
10. Drawbacks of Adopting Definition of Ready
11. Finding the right balance
12. Conclusion
13. Future Directions
14. Final Thoughts
15. References

Epilogue: Our Journey Continues