Notes on The Great Mental Models - Volume 1: General Thinking Concepts

My notes from the book The Great Mental Models - Volume 1: General Thinking Concepts by Shane Parrish and Rhiannon Beaubien.

• Preface
• Introduction: Acquiring Wisdom
  
• Mental Model #1: The Map is Not the Territory
  
• Mental Model #2: Circle of Confidence
  
• Mental Model #3: First Principles Thinking
  
• Mental Model #4: Thought Experiment
  
• Mental Model #5: Second-Order Thinking
  
• Mental Model #6: Probabilistic Thinking
  
• Mental Model #7: Inversion
  
• Mental Model #8: Occam’s Razor
  
• Mental Model #9: Hanlon’s Razor

Book LInks:

• Series Homepage

“You only think you know as a matter of fact, and most of your actions are based on incomplete knowledge, and you really don’t know what it is all about, or what the purpose of the world is, or know a great deal of other things. It is possible to live and not know.” - Richard Feynman

Preface

Introduction: Education’s Shortcomings and Real-World Challenges

• Education’s Failure to Prepare for Reality: Parrish argues that traditional education did not adequately prepare him for the complexities of the real world.
• September 11th and a Shift in Priorities: Parrish’s job at an intelligence agency was significantly impacted by the events of September 11, 2001. The required skills and tasks changed drastically.
• Computer Science Background vs. Human Dynamics: Parrish’s background in computer science, focused on logic and code, did not equip him to deal with the human element of decision-making, including the impact on individuals, families, and countries.
• Unexpected Responsibilities and Lack of Guidance: He was thrust into a series of promotions without proper guidance, facing responsibilities he felt unprepared for.
• The Weight of Decisions: Parrish realized that his decisions carried significant weight, affecting not just his employees but also their families, and impacting both his own country and others.
• Recognizing the Obligation to Make Good Decisions: Despite lacking a clear framework for decision-making, Parrish felt a strong obligation to make the best possible choices.

Seeking Mentorship and Knowledge

• Finding Mentors: To improve his decision-making abilities, Parrish sought out mentors, observing their actions and learning from their experience.
• Extensive Reading: He engaged in extensive reading on the topic of decision-making.
• Pursuing an MBA: Parrish even returned to school to pursue an MBA, hoping it would provide the necessary skills and knowledge for effective decision-making. He viewed it as achieving a specific “end state” rather than an ongoing process.
• Disillusionment with the MBA Program: His expectations for the MBA program were quickly challenged when he encountered an open-book exam. This experience led him to question the rigor and value of the program.

A Turning Point: Embracing Open-Book Exams and Discovering Charlie Munger

• Shifting Perspective on Open-Book Exams: Parrish realized that open-book exams offered a unique advantage: he couldn’t fail as long as he knew where to find the answers within the allowed materials.
• Liberation from Rote Learning: This realization freed him from the pressure of memorization and allowed him to focus on deeper understanding and exploration.
• Encountering Charlie Munger: He shifted his attention to learning about Charlie Munger, a figure mentioned in his classes.
• From Theory to Practical Wisdom: This marked a transition from theoretical, detached examples to the real-world wisdom of a highly successful businessman.
• Charlie Munger’s Background: Munger is the billionaire business partner of Warren Buffett at Berkshire Hathaway.
• Munger’s Appeal: Parrish describes Munger as likable, intelligent, witty, and irreverent.
• Finding Value in Practical Knowledge: Discovering Munger provided access to knowledge derived from real-world experience and a commitment to understanding how the world works.
• The Compelling Nature of Munger’s Success: Munger’s professional success further enhanced the appeal of his approach to decision-making.

The Latticework of Mental Models

• Munger’s Approach to Problem-Solving: Munger advocates for using a “broad latticework of mental models” to approach problems.
• Definition of Mental Models: Mental models are simplified representations of knowledge from various disciplines that can be applied to understand the world better.
• The Power of Mental Models: Munger suggests that mental models help identify relevant information in a given situation and establish reasonable parameters for decision-making.
• Practical Effectiveness: Parrish emphasizes that Munger’s track record demonstrates the effectiveness of this approach in practice.

Introduction: Acquiring Wisdom

• Analogy of the Fish:

  • Two young fish encounter an older fish who asks, “How’s the water?”
  • One young fish later asks, “What the hell is water?”
  • Meaning: This analogy by David Foster Wallace highlights how we often fail to recognize the most fundamental aspects of our environment and experiences because they are so pervasive.

• Blind Spots:

  • In life and business, the person with the fewest blind spots wins.
  • Removing blind spots leads to a better understanding of reality, improved thinking, and better choices.

• Thinking Better:

  • Finding simple processes to approach problems from multiple perspectives.
  • Choosing solutions aligned with what matters to us.

• Wisdom: The skill of finding the right solutions for the right problems.

• Pursuit of Wisdom:

  • Uncovering how things work.
  • Continuously learning and improving our understanding.
  • Getting out of our own way to see the world as it truly is.

• Benefits of Understanding:

  • Better decisions based on knowledge rather than ignorance.
  • Preparation for unpredictable problems.
  • Avoiding problems by understanding their root causes and potential consequences.

• Peter Bevelin’s Approach:

  “I don’t want to be a great problem solver. I want to avoid problems, prevent them from happening, and doing it right from the beginning.”

• How to Do Things Right:

  • Understand how the world works.
  • Adjust our behavior accordingly.

• Thinking Better:

  • Not necessarily about being a genius.
  • Focused on the processes used to uncover reality and the choices made based on that understanding.

Mental Models: A Framework for Understanding

• Purpose of the Book:
  • Introduces the concept of mental models.
  • Explores the most useful mental models across various aspects of life.
• What are Mental Models?
  • Mental models describe how the world works.
  • They shape our thinking, understanding, and beliefs.
  • They are largely subconscious and influence how we perceive and interpret information.
  • They help us infer causality, identify patterns, and draw analogies.
• Definition:
  • A mental model is a representation of how something works.
  • We use models to simplify complexity and organize our understanding.
• Importance of Mental Models:
  • We use mental models daily to think, decide, and understand the world.
  • Some models are true, while others are false.
  • The book focuses on the great mental models, those with the broadest utility.
• Volume 1 Focus:
  • Presents nine general thinking concepts.
  • These models are often overlooked but are essential for rational decision-making.
  • They offer different lenses to examine situations from various perspectives.
• Fundamentals of Knowledge:
  • Core ideas from all fields of study reveal principles about how the universe works.
  • These principles are crucial for navigating life.
  • The models in the series come from fundamental disciplines, but no prior knowledge is required.
• Benefits of Mental Models:
  • Help us minimize risk by understanding the forces at play.
  • Allow us to anticipate potential consequences.
  • Multidisciplinary thinking reduces vulnerability and fosters adaptability.
  • Access to diverse knowledge provides more solutions.

Understanding Reality

• Importance of Understanding Reality:
  • Essential for accurately perceiving and addressing problems.
  • Requires breaking down problems into their fundamental parts to reveal interconnections.
• Bottom-Up Perspective:
  • Exposes causal relationships and their impact on the present and future.
  • Enables accurate descriptions of situations.
• Mental Models as Lenses:
  • Illuminate interconnections within problems.
  • The more lenses used, the more of reality is revealed.
  • Deeper understanding leads to better-informed actions.
• Complexity and Lenses:
  • Simple problems require fewer lenses.
  • Complex problems benefit from a wider range of lenses.
  • Most real-world problems are multidimensional, making multiple lenses valuable.

Keeping Your Feet on the Ground

• Antaeus Myth:
  • Antaeus, a giant, gained strength from contact with the earth.
  • Hercules defeated him by lifting him off the ground, severing his connection to his source of power.
• Relevance to Understanding:
  • Understanding must be grounded in reality to be effective.
  • When understanding is separated from reality, it loses its power.
• Continuous Process:
  • Testing understanding against reality and updating it accordingly is an ongoing process.
• Testing Ideas:
  • Putting ideas into action is crucial for determining their validity.
  • Without real-world testing, understanding remains theoretical and potentially flawed.

Getting in Our Own Way

• Barriers to Learning:

  • Ourselves are often the biggest obstacle to learning from reality.
  • We have blind spots that prevent us from seeing what we don’t notice or aren’t looking for.

• Three Sources of Failure:

  1. Lack of Perspective:
     • Galileo’s Ship Analogy: Demonstrates the limitations of our default perspective.
     • We need to be open to other viewpoints to gain a fuller understanding.
  2. Ego-Induced Denial:
     • We often have too much invested in our opinions to accept feedback and update our beliefs.
     • Fear of Criticism: We avoid putting our ideas out there to protect ourselves from being wrong.
     • Defensive Mindset: When our ideas are criticized, our ego defends them instead of seeking improvement.
  3. Distance from Consequences:
     • The further we are from the consequences of our decisions, the easier it is to maintain flawed beliefs.
     • Hot Stove Analogy: We quickly learn from immediate consequences (pain) and update our understanding.
     • Organizational Distance: In large organizations, we may be removed from the direct impact of our decisions, hindering feedback and learning.
     • Ego and Narrative: Distance allows our ego to create narratives that justify our actions, even when they are flawed.

• Confucius’s Wisdom:

  “A man who has committed a mistake and doesn’t correct it is committing another mistake.”

• Cognitive Biases: We often fail to perceive information that contradicts our existing beliefs.

• Charles Darwin’s Approach:

  • Notice things that easily escape attention.
  • Ask “why” things happen.

• Value of Simple Ideas:

  • We tend to undervalue simple ideas and overvalue complex ones.
  • Simple ideas are crucial for preventing complex problems.

• Elementary Principles:

  • The great mental models are often based on fundamental, time-tested principles from various disciplines.
  • Understanding these principles allows us to adapt to changing circumstances.

Understanding is Not Enough

• Actionable Insights:
  • Mental models are not just theoretical; they provide actionable insights for positive change.
• Example of Interruptions:
  • Knowing that you interrupt people is useless without changing your behavior.
  • Failure to adapt reinforces negative perceptions and hinders improvement.
• Consequences of Inaction:
  • Others may perceive your inaction as a lack of caring.
  • You may be surprised by repeated negative outcomes despite understanding the issue.
• Understanding and Adaptation:
  • In the real world, success requires both understanding and adapting to reality.
• Suboptimal Decisions and Mistakes:
  • Fear of learning and admitting ignorance leads to poor decisions.
  • Poor decisions cause stress, anxiety, and wasted time.
• Delayed Consequences:
  • The burden of poor decisions often becomes apparent later when fixing mistakes consumes time and resources.
• Passivity vs. Agency:
  • We are not passive victims of our circumstances.
  • The world reveals itself to us, and we respond.
• Ego’s Dual Role:
  • Ego can hinder learning but also motivates us to achieve ambitious goals.
  • We need to discern when ego is helpful and when it’s harmful.
• Short-Term vs. Long-Term: We often prioritize short-term ego protection over long-term happiness.
• Black and White Thinking: We tend to view things as either good or bad based on whether they align with our beliefs.
• Openness to Feedback:
  • The world provides valuable feedback if we are open to it.
  • Keeping our feet on the ground allows us to learn and adapt.

Mental Models and How We Use Them

• Gravity as a Mental Model:
  • We all have a mental model of gravity, even if we don’t consciously think about it.
  • This model helps us understand and predict how gravity works in everyday life.
• Applications of the Gravity Model:
  • Explaining the movement of celestial bodies.
  • Informing the design of structures (bridges, airplanes).
  • Assessing safety in various situations.
• Metaphorical Use:
  • We use gravity as a metaphor to describe the influence of strong personalities (e.g., “pulled into her orbit”).
  • Sales Techniques: Salespeople leverage the principle of diminishing influence with distance to encourage immediate purchases.
• Reliability and Limitations:
  • Gravity is a time-tested and reliable model.
  • Not all models are as reliable as gravity.
  • Some models are only applicable in specific situations.
  • Some models are flawed or untested.
• The Cost of Flawed Models:
  • Bloodletting Example: The flawed model of bloodletting as a cure caused unnecessary deaths.
  • Flawed models lead to misunderstandings of situations, variables, and cause-and-effect relationships, resulting in suboptimal actions.
• Better Models, Better Thinking:
  • The accuracy of our mental models directly impacts the quality of our thinking.
  • Understanding reality allows us to make better decisions and avoid harmful actions.
  • Good decision-making often involves avoiding bad decisions.
• Sources of Error:
  1. Wrong Model: The model doesn’t reflect reality.
  2. Misapplication: Applying a correct model to an inappropriate situation.
• Bloodletting Revisited:
  • The bloodletting model persisted because it was part of a larger system of flawed medical models.
• Updating Models:
  • We must be willing to update our models when evidence contradicts them.
  • Continuous testing and openness to feedback are essential for refining our understanding.
• Sample Size and Refinement:
  • Examining the results of applying a model over a large sample size helps refine its accuracy.

The Power of Acquiring New Models

• Quality of Thinking:

  • The quality of our thinking is influenced by the mental models we possess.
  • We need both accurate and diverse models to understand complex situations.

• Importance of Variety:

  • Specialization often limits our exposure to diverse models.
  • We tend to overuse familiar models even when they are not appropriate.

• Disciplinary Blind Spots:

  • People from different disciplines tend to default to models specific to their field, creating blind spots.
  • “Hammer and Nail” Analogy: We tend to approach problems with the tools we are most familiar with, even if they are not the best fit.

• Elephant Parable:

  • Blind men touching different parts of an elephant each perceive it differently, illustrating the limitations of a single perspective.
  • Image of the Elephant Parable

• Disciplinary Limitations:

  • Each discipline offers a partial truth, but none hold the complete truth.

• Forest Analogy:

  • Different professionals (botanist, environmentalist, engineer, business person) will focus on different aspects of a forest, demonstrating the need for multidisciplinary perspectives.

• Latticework of Mental Models:

  • Latticework: A series of interconnected points that reinforce each other.
  • Mental models can be conceptualized as a latticework, where models connect and interact to create a comprehensive framework for understanding.

• Charlie Munger on Latticework:

  “Well, the first rule is that you can’t really know anything if you just remember isolated facts and try to bang them back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form.” “You’ve got to have the models in your head, and you’ve got to array your experience both vicarious and direct on this latticework of models.”

• Importance of Connecting Knowledge:

  • Simply memorizing facts is not enough for effective learning and application.
  • Real-world success requires integrating knowledge and experience within a framework of models.

Expanding Your Latticework of Mental Models

• Interconnected Knowledge:

  • A latticework effectively represents the interconnected nature of knowledge.
  • The world is not divided into discrete disciplines; we impose those divisions for convenience.
  • We need to integrate knowledge from various fields to understand the whole.

• Benefits of a Latticework:

  • Reduces blind spots.
  • Provides a more complete understanding of problems and their potential solutions.
  • Improves decision-making, making it faster, more creative, and more effective.

• Complementing Specialization:

  • We can overcome the limitations of specialization by developing curiosity about other disciplines.
  • Nobel Prize Winners: Many Nobel laureates, despite their specialization, have demonstrated multidisciplinary interests.

• Charlie Munger on Essential Models:

  “80 or 90 important models will carry about 90% of the freight in making you a worldly wise person. And of those, only a mere handful really carry very heavy freight.”

• Historical Examples and Stories:

  • The book uses examples and stories to illustrate the application of mental models.

• Further Resources:

  • The website (fs.blog) and newsletter offer more practical examples and insights.

You Won’t Always Get it Right

• Trial and Error:
  • You won’t always choose the most appropriate model for every situation.
  • This is a natural part of the learning process.
• Learning from Mistakes:
  • Acknowledge, reflect on, and learn from your mistakes when applying mental models.
  • This helps build your repertoire and improve your judgment.
• Deliberate Practice:
  • Be intentional about choosing and applying models.
  • Record and reflect on your experiences to improve your selection and application process.
• Journaling:
  • Keep a journal to track your use of mental models, including:
    • How you applied them.
    • The process you followed.
    • The results you achieved.
• Developing Expertise:
  • Over time, you will develop a better understanding of which models are best suited for different situations.
• Persistence and Analysis:
  • Don’t give up if a model doesn’t work immediately.
  • Analyze why it didn’t work:
    • Was your understanding of the model flawed?
    • Did you overlook relevant aspects of the situation?
    • Were you focused on the wrong variable?
• Observing Models in Action:
  • When you identify a mental model at work in the real world, note it down.
  • Explore the applications you’ve observed to gain a deeper understanding.
• Benefits of Practice:
  • With practice, applying mental models will become more natural and intuitive.
  • You’ll be able to recognize and counteract cognitive biases like confirmation bias.
• Understanding Success and Failure:
  • When a model works, understand why it worked to inform future applications.
  • Initially, focus on the process of applying models rather than just the outcome.
• Feedback Loops:
  • Remain open to feedback as you use mental models.
  • Reflect on your experiences and adjust your approach accordingly.
• Improved Decision-Making:
  • While not the primary focus, this book will indirectly improve your decision-making.
  • Mental models help you shift from subjective perceptions to objective assessments of reality.
• Elephant Analogy Revisited:
  • Using multiple mental models expands your understanding beyond a single limited perspective, leading to better-informed decisions.
• Benefits of Understanding Reality:
  • Increased confidence in navigating the world.
  • Greater success in achieving goals.
  • More time, less stress, and a more meaningful life.

Sidebar: What Can the Three Buckets of Knowledge Teach Us About History?

• Three Buckets of Knowledge (Peter Kaufman):
  1. Inorganic Systems (13.7 billion years): Laws of math and physics, the physical universe.
  2. Organic Systems (3.5 billion years): Biology on Earth.
  3. Human History (e.g., 20,000 years): Recorded human behavior.
• Sample Size and Reliability:
  • Larger and more relevant sample sizes lead to more reliable models.
• Importance of Historical Perspective:
  • We tend to assume the present state of the world is representative of the past.
  • Looking at the past provides valuable context for understanding the present.
• Examples of Change Over Time:
  • Continental drift.
  • The existence of dinosaurs.
  • The evolution of multiple hominid species.
• Conclusion:
  • Studying the past through the lens of these “buckets” helps us develop a more accurate and comprehensive understanding of the world and its underlying principles.

Mental Model #1: The Map is Not the Territory

The map appears to us more real than the land. - D.H. Lawrence

Introduction

• The Map is Not the Territory: A concept popularized by Alfred Korzybski stating that the description of a thing is not the thing itself, a model is not reality, and an abstraction is not the abstracted.
• Maps as Reductions: Maps are reductions of the territories they represent, making them imperfect but useful for navigation and simplification.
  • Examples: Financial statements, policy documents, parenting manuals, performance reviews.
• Purpose of Maps: Maps are valuable when they are explanatory and predictive.

Key Elements of a Map (Korzybski, 1931)

Alfred Korzybski’s 1931 paper introduced the “map is not the territory” concept and outlined key elements:

1. Structural Similarity (or Dissimilarity)
   • A map’s structure can be similar or dissimilar to the territory’s structure.
   • Example: The London Underground map is useful for travelers but not for train drivers.
   • Maps are designed with specific purposes and cannot be everything to everyone.
2. Similar Structures, Similar Logic
   • If a map accurately shows a relationship (e.g., Dresden between Paris and Warsaw), a similar relationship exists in the actual territory.
   • A correct map can be used for practical navigation.
3. Map ≠ Territory
   • A map cannot fully capture the experience of being in the territory.
   • Example: The London Underground map doesn’t convey the experience of being in Covent Garden station or navigating outside of it.
4. Ideal Map: Self-Reflexiveness
   • An ideal map would contain a map of the map, a map of the map of the map, and so on, endlessly.
   • This would be overwhelming in practice.
   • Example: A hypothetical “Guide to the Guide to Paris” becoming increasingly complex.

Abstractions and Reality

• Abstractions in Everyday Life: We consume abstractions constantly (e.g., news articles), which are simplified representations of complex information.
• Loss of Detail: Abstractions can lose specific and relevant details during the simplification process.
• Mistaking the Map for Reality: When we accept abstractions without critical thinking, we may forget that the map is not reality.
• Example: Believing a GPS map without considering potential real-world discrepancies (e.g., a cliff not shown on the map).

Limitations of Maps and Models

• Abstractions and Limits: We often forget that maps and models are abstractions with limitations.
• Ignoring the Territory: We can lose sight of the actual territory, which contains details not captured by the map.
• Knowledge of the Map vs. Territory: Problems arise when our knowledge becomes about the map instead of the underlying territory.
• Static Rules in a Dynamic World: Mistaking the map for reality leads to rigid rules and policies that fail to adapt to a changing environment.
• Ignoring Feedback Loops: Closing off feedback loops prevents us from recognizing changes in the territory, hindering adaptation.
• Simplification vs. Understanding: While simplification is necessary, prioritizing it over understanding leads to poor decisions.

Adapting Maps to a Changing Reality

• Dynamic World, Dynamic Maps: Maps and models should not be static; they need to adapt to changes in the territory.
• Value of Predictive and Explanatory Power: Maps need to accurately represent reality to be predictive and explanatory.
• Example: Newtonian Physics vs. Einstein’s Relativity:
  • Newtonian physics was a useful model for centuries.
  • Einstein’s theory of relativity provided a new and more accurate map of the universe.
  • Newtonian physics is still useful within its limitations.
  • Einstein’s physics is not yet complete (e.g., challenges in integrating with quantum physics).
• Understanding the Limits of Maps: Physicists understand the boundaries of their models (e.g., where Newtonian physics applies and where it doesn’t).
• Exploring Uncharted Territory: They carefully explore new areas (e.g., quantum mechanics) instead of assuming existing maps explain everything.

Risks and Limitations

• Hidden Risks: Maps may not show all the risks present in the territory, leading to unexpected problems.
• Understanding Limitations: It’s crucial to understand what a map or model does and doesn’t tell us to avoid misinterpretations and potential danger.
• Elinor Ostrom’s Caution: Economist Elinor Ostrom warned against using models (e.g., “tragedy of the commons”) as rigid doctrines in public policy.
  • Models as Metaphors: Ostrom cautioned that models can become metaphors, where assumed constraints are treated as fixed realities.
  • Double Problem:
    • Assuming a territory matches a map in all aspects based on limited similarities.
    • Prioritizing adherence to the map over new information about the territory.
  • Models as Tools for Exploration: Ostrom emphasized the value of models for generating thinking and exploration, not for enforcing conformity.
• George Box’s Quote: “Remember that all models are wrong. The practical question is, how wrong do they have to be not to be useful?”

Using Maps and Models Effectively

To use maps and models accurately, consider:

1. Reality as the Ultimate Update
   • Updating Maps: Territories change, and maps should be updated based on real-world experiences and feedback.
   • Example: Stereotypes as Maps:
     • Stereotypes can be useful for simplifying information processing.
     • They become dangerous when we forget the complexity of individuals and territories.
   • Karimeh Abbud’s Photography:
     • Abbud challenged European ethnographic perspectives of Palestine through her photography.
     • She captured the territory as she saw it, offering a different map of Palestinian culture.
   • Maps as Snapshots in Time: Maps represent a specific moment and may not reflect current or future realities.
   • Rate of Change: Faster changes in the territory make it harder for maps to stay up-to-date.
   • Norman Thrower’s Quote: “Viewed in its development through time, the map details the changing thought of the human race…”
2. Consider the Cartographer
   • Subjectivity in Mapmaking: Maps reflect the values, standards, and limitations of their creators.
   • Example: National Boundaries:
     • National boundaries are not objective representations of shared identities.
     • Nationalism is a modern construct that has developed alongside maps.
   • Historical Context:
     • The borders of Syria, Jordan, and Iraq reflect Western interests rather than local customs.
     • Maps are most useful when understood in their historical context.
   • Cartography as a Blend of Science and Art:
     • Norman Thrower: “Cartography, like architecture, has attributes of both a scientific and artistic pursuit…”
3. Maps Can Influence Territories
   • Jane Jacobs’ Critique of City Planning:
     • City planners used abstract models to design cities without considering how cities function in reality.
     • Attempts to fit cities into models had negative consequences.
   • Statistical City and Reality:
     • Jacobs warned against believing that maps and reality can be made to align by altering reality.
   • Caution Against Faith in Models:
     • Jacobs’ work highlights the dangers of letting faith in models override real-world complexities.
   • David Hand’s Emphasis on the Real World:
     • Statistical models should aim to understand and predict the real world, not an abstract mathematical world.

Conclusion

• Value and Limitations of Maps: Maps are valuable tools for knowledge transfer, but they have inherent limitations as reductions of complex realities.
• Subjectivity and Time Sensitivity: Maps are influenced by their creators and the time of their creation.
• Necessity of Models: We need models to simplify the world and interact with it.
• Exploration and Updating: Maps can guide us, but we shouldn’t let them prevent us from discovering new territory or updating existing maps.
• Navigating by Terrain and Maps: While navigating by terrain is ideal, it’s not always feasible; maps help us understand and relate to the world.
• Flawed but Useful: Maps are flawed but useful; we need to think beyond the map to anticipate future possibilities.

Sidebar: The Tragedy of the Commons

• Aristotle’s Observation: “What is common to many is taken least care of…”
• Parable of Shared Resource Depletion: The tragedy of the commons illustrates how shared resources can be overused due to individual incentives.
• Garrett Hardin’s Explanation:
  • Herdsmen sharing a pasture will individually try to maximize their cattle, leading to overgrazing and ultimately ruin.
  • Each herdsman’s rational decision to add more animals has negative consequences for the shared resource.
  • Freedom in a commons can lead to ruin for all.

Sidebar: Model of Management

• Frederick Taylor’s Scientific Management:
  • Breaking down tasks into small, specialized pieces with financial incentives for efficiency.
  • Effective for a time, especially in manufacturing settings.
• Limitations of Taylor’s Model:
  • Workers becoming aware of the model and resisting manipulation.
  • Competitors adopting the same model, neutralizing its advantage.
  • Applicability limited to specific settings (e.g., factories).
  • Failure to account for the complexities of human motivation.
• Evolution of Management Models:
  • Better models emerged as the economy shifted and understanding of human behavior developed.
• Effectiveness with Limitations: Taylor’s model was effective within its limitations, but it was eventually superseded by more comprehensive models.

Maps: Necessarily Flawed vs. Necessary, Not Necessarily Flawed

• Lewis Carroll’s Satire: Carroll’s story “Sylvie and Bruno” satirizes the idea of a perfectly accurate map with a 1:1 scale.
• Need for Reduction: Maps need to condense the territory to be useful for navigation and understanding.

Mental Model #2: Circle of Confidence

“I’m no genius, I’m smart in spots, but I stay around those spots.” - Thomas Watson

Introduction

• Central Idea: Understanding and operating within your circle of competence is crucial for effective decision-making and achieving positive outcomes.
• Key Concepts:
  • Ego vs. Competence
  • Identifying Strengths and Weaknesses
  • Building and Maintaining Expertise
  • Operating Outside Your Area of Expertise

What is a Circle of Competence?

• Analogy: The Lifer vs. The Stranger
  • The Lifer:
    • Represents deep, nuanced understanding within a specific area (e.g., a small town).
    • Possesses detailed knowledge accumulated over years of observation and experience.
    • Understands the history, relationships, and intricacies of the domain.
    • Can anticipate challenges and has multiple solutions to problems.
  • The Stranger:
    • Represents superficial knowledge based on limited exposure.
    • Makes assumptions based on incomplete information.
    • Overestimates their understanding and takes unnecessary risks.
    • Lacks the depth of knowledge for effective decision-making.
• Circle of Competence:
  • Represents the areas where an individual has deep, well-developed knowledge and expertise.
  • Based on years of experience, learning from mistakes, and actively seeking improvement.
  • Enables accurate and efficient decision-making within the defined area.
• Example: Climbing Mount Everest
  • Outside the Circle: For most people, climbing Mount Everest is outside their circle of competence.
    • Lack of knowledge about training, gear, process, and risks.
    • Unaware of the extent of their own ignorance (“unknown unknowns”).
  • Inside the Circle: Sherpas
    • Indigenous people with generations of experience navigating the mountain.
    • Possess a deep understanding of the terrain, climate, and challenges.
    • Sherpa Tenzing Norgay co-led the first successful ascent in 1953.
    • Sherpas have a significantly higher success rate and make multiple ascents.
  • Importance of Expertise:
    • Climbing Everest requires specialized knowledge and skills beyond basic understanding.
    • The extreme conditions and risks necessitate a high level of competence.

How Do You Know When You Have a Circle of Competence?

• Characteristics of Being Inside Your Circle:

  • Awareness of Limitations: You know what you don’t know within your area of expertise.
  • Efficient Decision-Making: You can make quick and accurate decisions based on your knowledge.
  • Information Mastery: You understand what information is needed, where to find it, and what is unknowable.
  • Anticipating Objections: You can foresee and address challenges based on past experience.
  • Multiple Solutions: You have a range of options for solving problems due to your deep understanding.
  • Understanding Invariants: You can differentiate between aspects that can be changed and those that are fixed.

• Time and Experience:

  • A circle of competence takes years to develop through dedicated practice and learning from failures.
  • It’s not achieved through short courses or superficial exposure.

• Alexander Pope’s Analogy (An Essay on Criticism):

  “A little learning is a dangerous thing. Drink deep or taste not the Pierian spring. There, shallow draughts intoxicate the brain, and drinking largely sobers us again.”

• Key Takeaway: Deep understanding and expertise are essential for true competence.

How Do You Build and Maintain a Circle of Competence?

• Dynamic Nature: A circle of competence is not static; it requires ongoing effort to maintain and expand.
• Key Practices:
  1. Curiosity and a Desire to Learn:
     • Learning from Experience: Reflecting on your own successes and failures.
     • Learning from Others: Utilizing books, articles, conversations, and the experiences of experts.
     • Efficiency of Learning from Others: It’s faster and more effective than relying solely on personal experience.
     • Quote: “Learn from the mistakes of others. You can’t live long enough to make them all yourself.” - Anonymous
  2. Monitoring and Feedback:
     • Honest Self-Assessment: Keeping track of your performance and decisions within your area of expertise.
     • Overcoming Ego: Recognizing that ego can hinder accurate self-evaluation.
     • Methods for Monitoring:
       • Keeping a detailed journal of your actions and outcomes.
       • Observing and analyzing the results of your decisions in leadership roles.
       • Honestly reflecting on failures to identify areas for improvement.
     • Benefits of Journaling:
       • Promotes self-reflection and identification of patterns.
       • Helps understand what went wrong and how to improve.
  3. External Feedback:
     • Addressing Ego and Bias: Seeking feedback from trusted sources to gain an objective perspective.
     • Sources of Feedback:
       • Trusted colleagues or mentors who can provide honest evaluations.
       • Hiring a coach or expert for specialized guidance.
     • Example: Atul Gawande
       • A top surgeon who hired a coach to improve his skills.
       • Initially felt embarrassed but recognized the value of external perspective.
       • Gained insights into areas for improvement and learned how to provide better feedback to others.
     • Importance of Outside Perspective: Overcoming personal biases and limitations through objective feedback.

How Do You Operate Outside of a Circle of Competence?

• Acknowledging Limitations: Recognizing when you lack the expertise to make informed decisions.
• Strategies for Operating Outside Your Circle:
  1. Learn the Basics:
     • Acquire a foundational understanding of the unfamiliar domain.
     • Caution: Basic knowledge can lead to overconfidence; avoid making major decisions based solely on it.
  2. Consult Experts:
     • Identify individuals with strong circles of competence in the relevant area.
     • Ask Thoughtful Questions: Focus on understanding the underlying principles and “learning how to fish” rather than just getting answers.
     • Probing Expert Limitations: Consider potential biases and the influence of the situation on their advice.
  3. Apply General Mental Models:
     • Utilize broader frameworks and principles to analyze the unfamiliar situation.
     • Identify foundational concepts that can guide your decision-making process.
• Example: Queen Elizabeth I
  • Inherited a challenging political and religious situation upon ascending to the throne.
  • Recognized her limitations in certain areas of governance.
  • Formed a Privy Council:
    • Assembled a diverse group of advisors with expertise in various domains.
    • Fostered open debate and discussion to leverage their collective circles of competence.
  • Positive Outcomes:
    • Achieved stability and fostered loyalty within the kingdom.
    • Laid the groundwork for England’s future as a global power.

Conclusion

• Importance of Boundaries: Recognizing the limits of your expertise is crucial for effective decision-making.
• Acknowledging Expertise: Respecting the knowledge and experience of others who have dedicated time to mastering specific areas.
• Limitations of Individual Competence: No one can be an expert in everything.
• Value of Circle of Competence: Identifying your areas of expertise and knowing how to operate outside of them is essential for success.
• Quote: “Ignorance more often begets confidence than knowledge.” - Charles Darwin

Sidebar: The Problem of Incentives

• Impact on Competence: Incentives can influence the advice and recommendations you receive from others.
• Financial Realm:
  • Commissions and Bias: Traditional financial products often involved commissions that incentivized advisors to promote specific options regardless of their suitability.
  • Index Funds: The rise of index funds has reduced this issue by aligning incentives with client performance.
• Assessing Incentives:
  • Understand the compensation structure of financial advisors and other professionals before accepting their advice.
  • Consider potential biases in situations where the advisor benefits from your decision (e.g., purchasing a product).
• Example: The Mechanic:
  • Asymmetry of Knowledge: Most people lack expertise in car mechanics, making them vulnerable to being overcharged or misled.
  • Mechanic’s Incentive: To maximize profits while retaining customers.
  • Solution:
    • Educate yourself about basic car maintenance using online resources.
    • Research potential issues and costs before agreeing to major repairs.

Sidebar: Staying in Your Circle

• Warren Buffett’s Perspective:

  • Emphasizes the importance of sticking to your area of expertise in investments.
  • Avoid venturing into areas where you lack knowledge and understanding (“unknown unknowns”).

• Example: Rose Blumkin (Nebraska Furniture Mart):

  • A successful CEO who focused on her strengths in numbers and home furnishings.
  • Avoided areas outside her circle of competence (e.g., stock market investments).

• Buffett’s Quote:

  “I couldn’t have given her $200 million worth of Berkshire Hathaway stock when I bought the business because she doesn’t understand stock. She understands cash. She understands furniture. She understands real estate. She doesn’t understand stock, so she doesn’t have anything to do with them…if you deal with Ms. B and what I would call her circle of competence. She’s going to buy 5,000 end tables this afternoon if the price is right. She’s going to buy 20 different carpets in odd lots and everything else like that because she understands carpets. She wouldn’t buy 100 shares of General Motors if it was 50 cents a share.”

• Key Takeaway: Focusing on your strengths and staying within your circle of competence can lead to significant success.

Supporting Idea: Falsifiability

• Karl Popper’s Concept:

  “A theory is part of empirical science if and only if it conflicts with possible experiences and is therefore, in principle, falsifiable by experience.”

• Testability: A good theory must be able to be proven wrong through observation or experimentation.

• Falsification vs. Verification:

  • Focus on trying to disprove a theory rather than simply seeking evidence to support it.
  • Failing to falsify a theory strengthens its validity.

• Example: Evolution: Natural selection eliminates unfavorable mutations, strengthening the overall fitness of the species.

• Freud’s Psychoanalytic Theory:

  • Popper argued that it lacked falsifiability because it didn’t make specific, testable predictions.
  • While it may contain some truths, it’s difficult to scientifically evaluate.

• Historicism:

  • The belief that history follows fixed laws or trends leading to predictable outcomes.
  • Popper considered it pseudoscientific because it’s not falsifiable.

• Trend vs. Destiny:

  • Trends observed in the past do not guarantee future outcomes.
  • Conditions can change, invalidating previous patterns.

• Bertrand Russell’s Chicken Analogy:

  • A chicken fed daily assumes this trend will continue indefinitely.
  • This “law” is broken when the chicken is slaughtered, highlighting the difference between trends and guaranteed outcomes.

• Preparing for Extremes: We should consider the full range of possibilities allowed by physics, not just the worst events observed in the past.

• Value of Falsifiability: It helps determine the robustness and scientific validity of theories.

Mental Model #3: First Principles Thinking

“I don’t know what’s the matter with people. They don’t learn by understanding, they learn by some other way, by road or something. Their knowledge is so fragile.” - Richard Feynman

Introduction

• First Principles Thinking is introduced as a powerful method for:
  • Reverse-engineering complex situations.
  • Unleashing creative potential.

What is First Principles Thinking?

• Definition:

  First Principles Thinking, also known as reasoning from first principles, is a tool that helps clarify complicated problems by separating the underlying ideas or facts from any assumptions based on them.

• Core Idea:

  • Identify the fundamental, essential elements (first principles) of a situation.
  • Build knowledge and understanding around these foundational elements.
  • This process allows for the creation of something new or innovative.

Philosophical Roots

• Historical Context:
  • The concept of building knowledge from first principles has a long history in Western philosophy, tracing back to Plato and Socrates.
  • Significant contributions were made by Aristotle and Descartes.
• Goal of Early Philosophers:
  • To discover foundational knowledge that remained constant and unchanging.
  • They aimed to build ethical systems and social structures upon these fundamental truths.

First Principles in Practice

• Modern Application:
  • First principles thinking doesn’t necessarily seek absolute, universal truths.
• Epistemological Considerations:
  • Finding absolute truths is challenging (millennia of philosophical inquiry have shown this).
  • The scientific method emphasizes the importance of actively trying to disprove knowledge to strengthen its validity.
• Practical Definition:
  • First principles thinking focuses on identifying elements that are non-reducible within a specific context or situation.
• Dynamic Nature of First Principles:
  • First principles are not a fixed checklist of universally true statements.
  • Our understanding of first principles evolves as our knowledge expands.
  • They serve as the foundation for building knowledge, and this foundation will differ depending on the specific situation.
• Importance of Challenging Existing Knowledge:
  • The more we know, the more effectively we can challenge existing assumptions and refine our understanding of first principles.
• Example: Refrigerator Energy Efficiency
  • Laws of thermodynamics can be considered first principles when exploring ways to improve a refrigerator’s energy efficiency.
  • However, a theoretical chemist or physicist might delve deeper into concepts like entropy and break down the second law of thermodynamics further, exploring its underlying principles and assumptions.
• Context-Dependent Nature:
  • First principles serve as boundaries within which we operate in a given situation.
  • Different individuals or disciplines may have different first principles depending on their context and goals. (e.g., an appliance maker vs. a physicist).

Techniques for Establishing First Principles

The Importance of Deconstruction and Critical Analysis

• Consequences of Accepting Information Passively:
  • If we don’t learn to deconstruct, question, and rebuild our understanding of things, we become limited by what others tell us and trapped in traditional ways of thinking.
  • When circumstances change, we risk making costly mistakes by continuing to operate under outdated assumptions.
• Natural Skepticism:
  • Some individuals are naturally inclined to question information they receive, especially if it contradicts their experiences, is outdated, or doesn’t align with their thinking.
• Shared Beliefs vs. Laws of Nature:
  • Many concepts we take for granted are not laws of nature but rather shared beliefs.
  • Examples:
    • Money
    • Borders
    • Bitcoins
    • Love
• Techniques for Identifying First Principles:
  • Two primary techniques can help us cut through dogma and shared beliefs to identify the fundamental principles within a situation:
    • Socratic Questioning
    • The Five Whys

Socratic Questioning

• Definition:

  Socratic questioning can be used to establish the first principles through stringent analysis. This is a disciplined questioning process used to establish truths, reveal underlying assumptions, and separate knowledge from ignorance.

• Key Distinction:

  • Unlike ordinary discussions, Socratic questioning aims to systematically uncover first principles.

• Process:

  1. Clarifying Thinking and Origins of Ideas:
     • Why do I think this?
     • What exactly do I think?
  2. Challenging Assumptions:
     • How do I know this is true?
     • What if I thought the opposite?
  3. Seeking Evidence:
     • How can I back this up?
     • What are the sources?
  4. Considering Alternative Perspectives:
     • What might others think?
     • How do I know I am correct?
  5. Examining Consequences and Implications:
     • What if I’m wrong?
     • What are the consequences if I am?
  6. Questioning the Original Questions:
     • Why did I think that?
     • Was I correct?
     • What conclusions can I draw from the reasoning process?

• Benefits:

  • Socratic questioning helps us avoid relying on intuition or emotional responses.
  • It promotes the development of knowledge that is robust and well-founded.

The Five Whys

• Origin:
  • The Five Whys method is inspired by the natural curiosity of children.
• Children’s Instinctive First Principles Thinking:
  • Children inherently seek to understand the world around them.
  • They intuitively break through superficial explanations by repeatedly asking “why.”
• Goal:
  • The aim of the Five Whys is to arrive at a fundamental understanding of “what” or “how” something works.
• Focus:
  • It’s not about introspection (e.g., “Why do I feel this way?”).
  • It’s about systematically digging deeper into a statement or concept to distinguish reliable knowledge from assumptions.
• Identifying First Principles:
  • If your chain of “whys” leads to a statement of a falsifiable fact, you’ve likely reached a first principle.
  • If you end up with answers like “because I said so” or “it just is,” you’ve likely hit an assumption based on opinion, cultural myths, or dogma, not a first principle.

Challenges and Rewards of These Techniques

• Short-Term Costs:
  • Both Socratic questioning and the Five Whys require pausing, reflecting, and researching, which can feel like a slowdown.
• Confronting Our Ignorance:
  • We often realize that we lack the answers to many of the questions these techniques raise.
• Importance of Persistence:
  • It’s crucial to avoid giving up or resorting to defensiveness when faced with our own knowledge gaps.
  • If we do, we won’t be able to identify the first principles necessary for effective problem-solving and decision-making, leading to long-term setbacks.

First Principles Thinking as a Way to Overcome Inaccurate Assumptions

The Case of Stomach Ulcers

• Quote: “Science is much more than a body of knowledge. It is a way of thinking.” - Carl Sagan
• Example: The Discovery of H. pylori
  • The discovery that the bacterium Helicobacter pylori (H. pylori), not stress, was the primary cause of most stomach ulcers is a prime example of the power of challenging assumptions to reveal first principles.
• The “Sterile Stomach” Dogma:
  • Scientists previously believed that bacteria couldn’t survive in the acidic environment of the stomach.
  • This assumption was widely accepted by doctors and researchers in the mid-20th century.
  • As a result, bacterial infection was not considered as a potential cause of stomach pain.
• Challenging the Assumption:
  • The “sterile stomach” was not a first principle but an unverified assumption.
  • Kevin Ashton, in his book on creativity, explains that this dogma prevented researchers from investigating its potential falsity.
• Robin Warren’s Observations:
  • Pathologist Robin Warren noticed bacteria in stomach samples from patients.
  • This observation challenged the prevailing assumption of a sterile stomach.
• Collaboration with Barry Marshall:
  • Warren collaborated with gastroenterologist Barry Marshall, and they found evidence of bacteria in numerous stomachs.
• Shifting Focus:
  • Since the sterile stomach was no longer considered a first principle, they could question other assumptions related to stomach diseases and apply Socratic-style questioning to uncover the true first principles at play. (125-126, 134)
• Evidence and Persistence:
  • Warren and Marshall spent years challenging assumptions, clarifying their thinking, and gathering evidence.
• Nobel Prize Recognition:
  • Their work eventually led to the understanding of H. pylori’s role in stomach ulcers and earned them the Nobel Prize in 2005.
  • Their discovery revolutionized ulcer treatment with antibiotics, improving and saving millions of lives.
• Resistance to Change:
  • Despite the evidence, many practitioners and scientists initially resisted their findings due to the deeply ingrained belief in the sterile stomach dogma.
  • It was difficult to acknowledge that this “first principle” was based on flawed assumptions, often dismissed with explanations like “that’s just the way it is.”
• Historical Evidence:
  • Ashton notes that evidence of H. pylori existed in medical literature as far back as 1875, but it was Warren and Marshall who demonstrated that “because I said so” was not sufficient justification for considering the sterile stomach a first principle.

Incremental Innovation and Paradigm Shifts

Understanding the Rationale Behind Success

• The Importance of Understanding:
  • To effectively improve something, we need to understand the reasons behind its success or failure.
  • Otherwise, we risk blindly copying tactics without grasping their underlying rationale.
• Avoiding Blind Imitation:
  • First principles thinking helps us avoid relying on others’ tactics without understanding their purpose.
• Incremental Improvement and First Principles:
  • Even small improvements are more challenging to achieve if we cannot identify the relevant first principles.

The Case of Curved Cattle Chutes

• Temple Grandin’s Contributions:
  • Temple Grandin, an autistic scientist, is known for her insights into the autistic mind and her contributions to improving animal welfare in the livestock industry.
• Curved Cattle Chutes:
  • Grandin pioneered the use of curved cattle chutes.
  • Traditionally, straight chutes were used.
• Rationale for Curved Chutes:
  • Curved chutes are more efficient because they leverage the natural behavior of cattle, who tend to move more easily through curves due to their instinct to return to where they came from.
• Ongoing Research:
  • Animal science continues to evolve, with research constantly exploring better ways to handle livestock.
  • Image: Curved Cattle Chute
• Challenging the Curved Chute:
  • Research published in Stockmanship Journal questioned the universal superiority of curved chutes, finding that in some cases, straight chutes could be equally effective.
• Grandin’s Response:
  • Grandin’s response to this research highlights the importance of first principles thinking.
  • She explained that curved chutes are not a first principle in themselves but a tactic designed to address the first principle she identified: reducing stress in animals.
  • This principle affects various aspects of animal welfare, from conception rates to weight gain and immune function.
• Flexibility with Tactics:
  • As long as a livestock environment minimizes stress, a straight chute can be effective.
  • Understanding the underlying principles allows for flexibility in choosing tactics.

Questioning Existing Constructs

• Beyond Incremental Improvement:
  • Sometimes, we aim for more than just fine-tuning existing systems.
  • We might be skeptical or curious and want to explore possibilities beyond the status quo.
• Identifying First Principles for Paradigm Shifts:
  • When we question whether things have to be the way they are, we adopt a mindset conducive to identifying first principles that can lead to radical change.
• Strategic Choices:
  • First principles thinking enables us to move away from random changes and make informed choices with a higher likelihood of success.

The Case of Artificial Meat

• Rethinking Meat:
  • Starting in the 1970s, scientists began to question the fundamental nature of meat.
• First Principles of Meat:
  • Common answers include:
    • Taste
    • Texture
    • Smell
    • Culinary use
  • Notably, being part of an animal is not a first principle of meat.
• Consumer Priorities:
  • Taste is a crucial factor for consumers, while the origin of the meat (whether it came from an animal) is less important.
• The Science of Meat Flavor:
  • Research explored the reasons behind the characteristic taste of meat.
  • The Maillard reaction, a chemical reaction between sugars and amino acids during cooking, contributes significantly to meat’s flavor and aroma.
• Replicating First Principles:
  • Scientists aim to replicate the Maillard reaction to recreate the core taste and smell of meat.
• Potential Impact:
  • This could significantly reduce the need to raise animals for food.
• Moving Beyond Existing Systems:
  • Instead of focusing on incremental improvements within the livestock industry (e.g., reducing environmental impact), around 30 labs worldwide are developing artificial meat.
• Progress in Artificial Meat:
  • Lab-grown meat is approaching the composition and properties of traditional meat.
• Sensory Descriptions:
  • Researchers describe the product as having:
    • “a bite to it”
    • flavor from browning
    • “intense taste”
    • a consistency “close to meat”
    • a meat-like appearance
• Addressing Ethical and Environmental Concerns:
  • Artificial meat offers a potential solution to address ethical concerns about animal welfare and the environmental impact of the meat industry.

Conclusion

• Quote:

  “As to methods, there may be a million and then some, but principles are few. The man who grasps principles can successfully select his own methods. The man who tries methods ignoring principles is sure to have trouble.” - Harrington Emerson

• The Power of First Principles Thinking:

  • Reasoning from first principles allows us to move beyond historical constraints and conventional wisdom to explore new possibilities.
  • Understanding the underlying principles enables us to evaluate the effectiveness of existing methods, which often prove to be suboptimal.

• Creativity and First Principles:

  • The misconception that creativity is an innate talent is challenged.
  • Evidence suggests that we are all born creative, but societal pressures and reliance on convention can stifle this inherent ability.

• Breaking Free from Limitations:

  • First principles thinking helps us shed these limitations and see the world with fresh eyes.
  • It opens up a wider range of possibilities and empowers us to think for ourselves.

Mental Model #4: Thought Experiment

Creativity is intelligence having fun. - Anonymous

Introduction

• Thought experiments are defined as “devices of the imagination used to investigate the nature of things.”
• Many disciplines, including philosophy and physics, utilize thought experiments to explore the limits of knowledge.
• Benefits of Thought Experiments:
  • Learning from mistakes and avoiding future ones.
  • Exploring impossible scenarios.
  • Evaluating potential consequences of actions.
  • Re-examining history for better decision-making.
  • Clarifying desires and optimal paths to achieve them.

The LeBron James Basketball Analogy

Comparing LeBron James vs. Woody Allen

• The thought experiment poses the question: Who would win in a basketball game between LeBron James and Woody Allen?
• Intuition and Simulation:
  • We instinctively know LeBron James would win based on our knowledge of their physical attributes and basketball skills.
  • We mentally simulate the game without needing to actually observe it.
• Confidence in the Outcome:
  • The disparity in abilities leads to high confidence in the outcome, justifying a high-stakes bet.

Comparing LeBron James vs. Kevin Durant

• The thought experiment then asks: Who would win between LeBron James and Kevin Durant?
• Increased Difficulty:
  • Both players are highly skilled professionals with similar abilities.
  • Predicting the outcome is much more challenging.
• Reduced Confidence:
  • The uncertainty makes it unwise to place a high-stakes bet.
• Need for Observation:
  • Determining the winner with certainty would require observing them play multiple games.

The Power of Thought Experiments

• Simulating Reality: Thought experiments allow us to mentally simulate scenarios that are difficult or impossible to replicate in real life.
• Exploring Multiple Perspectives: They enable us to examine situations from various angles, exceeding the limitations of physical experimentation.
• Rigorous Approach: Effective thought experiments require the same rigor as traditional scientific experiments.

The Scientific Method in Thought Experiments

1. Ask a Question: Define the problem or scenario to be explored.
2. Conduct Background Research: Gather relevant information about the subject matter.
3. Construct Hypotheses: Formulate potential explanations or outcomes.
4. Test with Thought Experiments: Mentally simulate the scenario under different conditions.
5. Analyze Outcomes and Draw Conclusions: Evaluate the results of the thought experiment and derive insights.
6. Compare Hypotheses and Adjust Accordingly: Refine the hypotheses based on the analysis and repeat the process if necessary.

Applying the Scientific Method to the Basketball Analogy

• Question: Who would win in a basketball game between LeBron James and Woody Allen?
• Background Research: Understanding the players’ abilities, physical characteristics, and the rules of basketball.
• Hypothesis: LeBron James would win.
• Thought Experiment: Mentally simulate the game, considering various scenarios.
• Analysis: The overwhelming likelihood of LeBron James winning supports the hypothesis.
• Conclusion: LeBron James is highly likely to win in a basketball game against Woody Allen.

Estimating Probabilities

• Thought experiments can help us estimate the probability of different outcomes.
• Example: In a hypothetical 100,000 basketball games between LeBron James and Woody Allen, Woody Allen might only win in extremely rare circumstances, such as LeBron James experiencing a sudden medical emergency.
• Understanding Influence and Expectations: Exploring the spectrum of possibilities enhances our understanding of factors we can influence and realistic expectations for outcomes.

Applications of Thought Experiments

1. Imagining Physical Impossibilities

• Albert Einstein’s Elevator Thought Experiment:
  • Scenario: Imagine being in a closed elevator with your feet glued to the floor. Could you determine whether the elevator was accelerating upwards in space or stationary on Earth under the influence of gravity?
  • Conclusion: Einstein concluded that it would be impossible to distinguish between the two scenarios based on the felt force alone.
  • Implications: This led to the development of the general theory of relativity, which equates the forces of acceleration and gravity.
• “If Money Were No Object”:
  • This common expression prompts a thought experiment by removing the constraint of financial limitations.
  • Exploring Values: Imagining choices in a financially unrestricted scenario reveals our priorities and values.

2. Reimagining History

• Counterfactual and Semi-factual Reasoning:
  • What if? questions explore alternative historical scenarios by changing past events.
  • Examples:
    • What if Gavrilo Princip hadn’t assassinated Archduke Franz Ferdinand?
    • What if Cleopatra hadn’t met Julius Caesar?
• Caution with Chaotic Systems:
  • History is a chaotic system, where small changes can have significant and unpredictable consequences.
  • The Weather Analogy: Like weather forecasting, predicting historical outcomes based on altered events is inherently difficult due to the complexity of interconnected factors.
• The Importance of Rigor: Applying the scientific method to historical thought experiments helps mitigate the risk of misleading conclusions.

Exploring the Assassination of Archduke Franz Ferdinand

• Question: What if Gavrilo Princip hadn’t assassinated Archduke Franz Ferdinand?
• Background Research: Investigating the political climate, treaties, alliances, and personalities of the time.
• Refined Question: How did Princip’s assassination influence Austrian policy towards Serbia?
• Hypotheses:
  1. The assassination had no effect on policy.
  2. The assassination had a partial effect on policy.
  3. The assassination had a total effect on policy.
• Thought Experiment: Mentally simulate scenarios where the assassination doesn’t occur, considering alternative events and Austria’s potential reactions.
• Analysis: Evaluating the plausibility of different scenarios and their potential impact on Austrian policy.
• Understanding Causal Relationships: The goal is not to definitively determine whether the assassination caused World War I, but to assess its potential contribution and the interconnectedness of events.

3. Intuiting the Non-intuitive

• The Veil of Ignorance (John Rawls):
  • Scenario: Imagine designing a society without knowing your future position within it (economic status, ethnicity, talents, gender, etc.).
  • Promoting Fairness: This thought experiment encourages the creation of a just and equitable society by removing personal biases.
• Challenging Intuition: The veil of ignorance forces us to reconsider our initial notions of fairness and consider the implications of societal structures for all members.
• Applications Beyond Legislation: This type of thinking can be applied to various contexts, such as designing company policies that are fair and unbiased regardless of individual roles or characteristics.

The Value and Limitations of Thought Experiments

• Exploring Limits: Thought experiments reveal the boundaries of our knowledge and what is realistically achievable.
• Decision-Making: They enhance decision-making by allowing us to consider a wider range of possibilities and potential consequences.
• Understanding Cause and Effect: Thought experiments improve our understanding of causal relationships and the factors that influence outcomes.
• Rigor and Work: Effective thought experiments require careful planning, research, and analysis.
• Not a Substitute for Reality: While valuable, thought experiments cannot fully replace real-world experimentation and observation.

Sidebar: The Trolley Experiment

• Exploring Ethical Dilemmas: Thought experiments are often used to examine ethical and moral issues, particularly in situations where real-world experimentation would be unethical.
• The Trolley Problem:
  • Scenario: A runaway trolley is about to kill five people on the tracks. You can divert the trolley to a different track where only one person will be killed. What do you do?
  • Ethical Considerations: This thought experiment explores the complexities of utilitarianism (maximizing overall well-being) versus deontological ethics (adhering to moral rules).
• Relevance to Technological Advances: The trolley problem has gained renewed relevance in the context of self-driving cars and other technologies that may require ethical decision-making in life-or-death situations.
• Variations and Extensions: The trolley problem has been adapted and expanded upon to explore various ethical nuances, such as the permissibility of sacrificing one person to save many in different contexts (e.g., organ donation).

Sidebar: Reduce the Role of Chance

The Google Stock Example

• Scenario: You buy $100,000 of Google stock on margin (50% cash, 50% loan). The stock price doubles, tripling your initial investment.
• Exploring Alternative Outcomes:
  • What if Google had dropped 50% before rising 100%?
  • What if Google had experienced a significant crash, like the stock market in 1929-1932?
• Assessing Risk: Running the scenario multiple times in your mind reveals the potential for significant losses and the role of chance in the outcome.
• Decision-Making Implications: This thought experiment highlights the importance of considering a wider range of possibilities and understanding the limits of our knowledge when making investment decisions.
• Reducing Reliance on Luck: By analyzing potential scenarios, we can develop more robust decision-making processes that minimize the influence of chance and improve our chances of success.

Supporting Idea: Necessity and Sufficiency

• Distinguishing Between Necessary and Sufficient Conditions:
  • Necessary Conditions: Conditions that must be present for an event to occur.
  • Sufficient Conditions: Conditions that guarantee the occurrence of an event.
• The Gap Between Necessity and Sufficiency:
  • Achieving a desired outcome often requires more than just fulfilling the necessary conditions.
  • Example: Being a skilled writer is necessary but not sufficient to become a best-selling author like J.K. Rowling.
• The Role of Luck and Chance: The gap between necessity and sufficiency is often filled by factors outside our direct control, such as luck, timing, or unforeseen circumstances.
• Examples:
  • Fortune 500 Success: Capital, hard work, and intelligence are necessary but not sufficient. Luck and other factors also play a significant role.
  • Military Battles: Careful planning and logistics are necessary but not sufficient to win a battle. Unforeseen events and the actions of the enemy also influence the outcome.
  • Professional Sports: Physical ability and training are necessary but not sufficient to guarantee success. Competition and other factors can prevent talented athletes from reaching the professional level.
• Sets in Mathematics: The set of necessary conditions is a subset of the set of sufficient conditions. The sufficient set encompasses a wider range of factors.
• Avoiding Misleading Narratives: Understanding the distinction between necessity and sufficiency helps us avoid drawing incorrect conclusions about success or failure based solely on the presence of necessary conditions. We must acknowledge the role of other factors, including chance.

Mental Model #5: Second-Order Thinking

“Technology is fine, but the scientists and engineers only partially think through their problems. They solve certain aspects, but not the total, and as a consequence, it is slapping us back in the face very hard.” - Barbara McClintock

Introduction

• Second-order thinking is a mental model that involves thinking further ahead and holistically, considering not only the immediate consequences of actions but also the subsequent effects of those actions.

• First-order thinking is anticipating the immediate results of actions, which is easy and safe but can lead to similar results as everyone else.

• Quote:

  “Almost everyone can anticipate the immediate results of their actions. This type of first-order thinking is easy and safe, but it’s also a way to ensure that you get the same results that everyone else gets.”

• Failing to consider second- and third-order effects can lead to disaster.

• The law of unintended consequences refers to the unforeseen and often negative outcomes that arise from actions not fully considered.

• Quote: “Very often, the second level of effects is not considered until it’s too late.”

Examples of Second-Order Thinking Deficiency

The Cobra Effect in Colonial India

• The British government in India offered a reward for every dead cobra to reduce their population in Delhi.
• First-order effect: Citizens killed cobras and received rewards.
• Second-order effect: People began breeding cobras to increase their rewards, leading to a larger cobra population than before.
• Result: The British officials did not think through the second-order effects, and the snake problem worsened.

Traction on Tires

• Increasing traction on tires seems like a good idea for safety.
• First-order effect: Improved grip and reduced sliding, potentially leading to faster stopping.
• Second-order effects:
  • Increased engine workload to propel the car.
  • Reduced gas mileage.
  • Increased carbon dioxide emissions.
  • More rubber particles left on the road.
• Conclusion: Even seemingly simple changes can have unintended negative consequences.

Antibiotic Use in Livestock

• Antibiotics have been used in livestock for decades to make meat safer and cheaper.
• First-order effect: Animals gain more weight per pound of food, increasing profits for farmers.
• Second-order effects:
  • Development of antibiotic-resistant bacteria.
  • Entry of drug-resistant bacteria into the food chain.
  • Potential health risks to humans who consume meat with antibiotic-resistant bacteria.
• Garrett Hardin’s First Law of Ecology: “You can never merely do one thing.”
• High degrees of interconnectedness make second-order thinking crucial because actions can have far-reaching and unpredictable consequences.
• Quote: “Things are not produced and consumed in a vacuum. When we try to pick out anything by itself, we find it hitched to everything else in the universe.” - John Muir

Anticipating Consequences

• Second-order thinking is not about predicting the future with certainty, but about considering likely consequences based on available information.
• Example: The consequences of using antibiotics in animal feed could have been anticipated with a basic understanding of biology and evolution.
• Organisms adapt to environmental pressures, and bacteria exposed to antibiotics can develop resistance over time.
• Second-order thinking emphasizes the importance of understanding the web of connections and considering whether short-term gains are worth potential long-term pain.

Applications of Second-Order Thinking

Prioritizing Long-Term Interests

• Second-order thinking helps identify long-term effects and make choices that support them, even if it means foregoing immediate gains.
• Example: Choosing not to eat candy for better long-term health.
• Historical examples can be difficult to analyze because positive outcomes don’t always guarantee second-order thinking.

Cleopatra’s Alliance with Caesar (48 BC)

• Cleopatra’s situation:
  • Co-regent of Egypt with her brother, in a family known for sibling rivalry and murder.
  • Ousted from the palace and lacking support.
  • Facing assassination attempts from her brother.
• Caesar’s arrival:
  • Roman general pursuing his enemy Pompey in Egypt.
  • Asserting Roman control over the strategically important and wealthy Egypt.
  • Roman intervention unpopular with the Egyptians.
• Cleopatra’s options:
  • Reconcile with her brother.
  • Seek support from another country.
  • Align with Caesar.
• Cleopatra’s education:
  • Well-versed in political history and witnessed the consequences of various actions by her father and family members.
  • Understood the complexity of Egyptian politics and the precarious balance between Rome and the Egyptian people.
• Cleopatra’s decision:
  • Chose to align with Caesar, likely anticipating short-term pain but aiming for long-term gain.
• First-order effects:
  • Angered her brother, leading to increased assassination attempts.
  • Angered the Egyptian people who resented Roman involvement.
  • Started a civil war with a siege on the palace, trapping her and Caesar for months.
• Second-order effects (likely considered by Cleopatra):
  • Caesar’s support would eliminate opposition and solidify her reign.
  • Roman backing would provide stability and security in the long run.
• Outcome:
  • Cleopatra successfully ruled Egypt for many years after the civil war.
  • Caesar’s victory removed her opposition and secured her position.

Constructing Effective Arguments

• Considering second-order effects strengthens arguments by demonstrating foresight and addressing potential challenges in advance.

• Example: Convincing a boss or spouse by outlining the benefits that result from the proposed action.

• Mary Wollstonecraft’s argument for women’s rights (late 18th century England):

  • Women had limited rights and independence.

  • Instead of directly demanding rights, Wollstonecraft focused on the second-order effects of granting those rights.

  • She argued that educating women would make them better wives, mothers, and contributing members of society.

  • Quote:

    “Asserting the rights which women in common with men ought to contend for, I have not attempted to extenuate their faults, but to prove them to be the natural consequence of their education and station in society. If so, it is reasonable to suppose that they will change their character and correct their vices and follies when they are allowed to be free in a physical, moral, and civil sense.”

  • By highlighting the positive consequences for society, she initiated a conversation that contributed to the feminist movement.

  • First-order effect: Empowering women through recognized rights.

  • Second-order effect: Creating better women and a better society.

  • Quote: “Not only would women get freedoms they deserved, they would become better women and better members of society.”

Avoiding the Slippery Slope Fallacy

• Slippery slope effect: The flawed reasoning that a specific action will inevitably lead to a series of negative consequences.

• Garrett Hardin’s caution against the slippery slope:

  • People possess practical judgment and can avoid extreme outcomes.

  • Quote:

    “Those who take the wedge, slippery slope argument with the utmost seriousness act as though they think human beings are completely devoid of practical judgment. Countless examples from everyday life show that pessimists are wrong. If we took the wedge argument seriously, we would pass a law forbidding all vehicles to travel at any speed greater than zero. That would be an easy way out of the moral problem, but we pass no such law. In practical life, everything has limits.”

• Example: The argument that having one alcoholic drink will inevitably lead to a life of alcoholism is a slippery slope fallacy. While it’s a possibility, it’s not the most likely outcome for most people.

• Second-order thinking should focus on the most likely effects and their consequences, not on all possible outcomes.

• Balancing the need for higher-order thinking with practical judgment depends on the specific situation.

Conclusion

• Decisions are made within a system, and actions have consequences, both intended and unintended.
• Considering second-order effects helps avoid future problems and make better choices.
• Key question: “And then what?”
• Consequences can be tangible and intangible, affecting various aspects of life.
• Thinking systemically reveals the interconnectedness of consequences.
• Second-order thinking involves considering time, scale, thresholds, and weighing different paths.

Sidebar: Second-Order Problem (Warren Buffett’s Metaphor)

• Parade analogy: When a few people stand on their tiptoes to see better at a parade, everyone eventually has to do the same. No one sees better, and everyone is worse off.
• Illustration: fs.blog/tgmm

Sidebar: Developing Trust for Future Success

• Trust and trustworthiness are built through repeated interactions.
• Second-order thinking in relationships:
  • Seeking immediate payoffs in interactions, unless they are win-win, often leads to one-off interactions.
  • Building trust requires considering the long-term effects of actions on others and on one’s reputation.
  • Maximizing benefits in relationships occurs over time through cooperation and trust.

Mental Model #6: Probabilistic Thinking

“The theory of probability is the only mathematical tool available to help map the unknown and the uncontrollable. It is fortunate that this tool, while tricky, is extraordinarily powerful and convenient.” - Benoit Mandelbrot

Introduction

• Probabilistic thinking is the process of estimating the likelihood of specific outcomes using mathematical and logical tools.
• It enhances decision-making accuracy in a world influenced by countless factors.
• Probabilistic thinking helps identify the most likely outcomes in complex situations.
• The future’s inherent unpredictability stems from incomplete information and the potential for even minor errors to disrupt predictions.
• Generating realistic probabilities offers the best approach to estimating the future.

The Need for Probabilities

• While events either occur or don’t, uncertainty about the future necessitates probabilities.
• Probabilities help us navigate the future by understanding the likelihood of impactful events.
• Imperfect information about the world underscores the value of probability theory.
• The inherent unpredictability of the future arises from unknown variables and the potential for small errors to significantly alter predictions.

The Evolution of Probabilistic Thinking

• Our brains evolved heuristics (mental shortcuts) for probabilistic thinking in a time focused on survival.
• These heuristics, studied by psychologists Daniel Kahneman and Amos Tversky, remain effective for basic survival.
• However, thriving in today’s complex social systems requires a conscious understanding and application of probability principles.

Key Aspects of Probabilistic Thinking

To improve decision-making under uncertainty, three crucial aspects of probability are essential:

1. Bayesian Thinking
2. Fat-Tailed Curves
3. Asymmetries

1. Bayesian Thinking

Thomas Bayes and Bayesian Updating

• Thomas Bayes, an 18th-century English minister, explored how probabilities should be adjusted with new data.
• His work, published posthumously, laid the foundation for Bayes’ theorem, developed by mathematician Pierre-Simon Laplace.
• Bayesian thinking (or Bayesian updating) emphasizes incorporating prior knowledge when encountering new information.
• It involves using relevant prior information (or base rates) about similar past situations to inform current decisions.

Examples of Bayesian Thinking

• Violent Crime:
  • Headlines about rising crime rates might induce fear without context.
  • A Bayesian approach considers prior knowledge of declining crime trends over decades.
  • Even if crime doubles from a low base rate (e.g., 0.01% to 0.02%), the overall risk remains low.
• Diabetes Statistics:
  • A steady increase in diabetes diagnoses over time (e.g., from 0.93% in 1958 to 7.4% in 2015) signals a concerning trend.
  • Bayesian analysis, incorporating prior data, indicates a genuine cause for concern.

The Role of Priors

• Priors are probability estimates of the truthfulness of prior knowledge.
• They should not hinder the processing of new knowledge (likelihood ratio or Bayes factor).
• New information can challenge and potentially reduce the probability of a prior being true.
• This iterative cycle involves continuously challenging and validating existing beliefs.

Applying Bayesian Thinking

• When facing uncertain decisions, ask:
  • “What are the relevant priors?”
  • “What existing knowledge can enhance my understanding of the situation?”

2. Fat-Tailed Curves

Bell Curve vs. Fat-Tailed Curves

• The bell curve (normal distribution) depicts the relative frequency of many phenomena (e.g., height, exam scores).
• It’s characterized by symmetry and predictable extremes.
• Fat-tailed curves differ in their tails.
  • Extreme events are not capped, leading to longer tails.
  • While individual extreme events remain unlikely, the sheer number of possibilities increases their overall likelihood.
  • The central tendency (average) becomes less representative due to the influence of extreme events.

Examples of Fat-Tailed Distributions

• Height/Weight: Outliers exist, but within a defined range (e.g., no human is 10 times the average height).
• Wealth: Extreme outliers are common (e.g., individuals can be 10, 100, or 10,000 times wealthier than the average).

Terror Risk vs. Stair-Slipping Risk

• Comparing deaths from terrorism (e.g., 500) to deaths from slipping on stairs (e.g., 1,000) might suggest stairs are more dangerous.
• However, terror risk exhibits a fat tail, with a wider range of potential extreme events.
• The number of possible terror events in the future is far greater than the number of stair-slipping scenarios.

Dealing with Fat-Tailed Domains

• It’s impossible to predict every possible extreme event in a fat-tailed domain.
• The key is to position oneself to survive or benefit from unpredictable events by planning for a world with inherent uncertainty.

3. Asymmetries

Meta-Probability and Estimation Errors

• Meta-probability refers to the probability that your probability estimates are accurate.
• Asymmetries arise when estimation errors consistently skew in a particular direction.

Examples of Asymmetries

• Investment Returns:
  • Investors often overestimate their ability to achieve high returns (e.g., 20-40% annually).
  • Actual returns tend to be lower, closer to the market average (e.g., 7-8%).
• Travel Time Estimation:
  • People are more likely to underestimate travel time (arrive late) than overestimate it (arrive early).

Over-Optimism in Probabilistic Estimates

• Probability estimates are often wrong on the over-optimistic side.
• Investors aiming for high returns frequently fall short.
• It’s crucial to be aware of this tendency and adjust estimates accordingly.

The Spy World and Probabilistic Thinking

Vera Atkins and WWII Espionage

• Vera Atkins, a British intelligence officer during World War II, made countless decisions based on probabilistic thinking.
• Her role involved recruiting and deploying agents into occupied France, where failure meant death.

Agent Selection

• Atkins had to assess the probability of an agent’s success based on limited information.
• Factors considered included language skills, confidence, family ties, and problem-solving abilities.
• Agent development involved a series of continuously updated probability estimates.

Target Selection

• Choosing intelligence targets required evaluating the reliability of often unreliable information.
• Atkins had to weigh the relevancy, quality, and timeliness of intelligence gathered through various means (e.g., photographs, notes, wireless messages).
• Decisions considered both past events and potential future scenarios.

Agent Roles and Risks

• Agents worked in roles such as organizers, couriers, and wireless operators, each with inherent dangers.
• The full scope of threats was never fully known, requiring preparation for unpredictable events.
• The average life expectancy of a wireless operator in France was just six weeks.

Asymmetry in Success Estimation

• The high casualty rate among Atkins’ agents (100 out of 400) suggests potential overestimation of success probabilities.
• This highlights the limitations of probabilistic thinking, which doesn’t guarantee success but aims to improve the odds.

Vera Atkins’ Legacy

• Despite the significant loss of life, Atkins’ network achieved valuable sabotage, supporting the Allied cause.
• Her story demonstrates the critical role of probabilistic thinking in high-stakes situations.

Conclusion

• Successful probabilistic thinking involves:
  • Identifying key factors.
  • Estimating odds.
  • Checking assumptions.
  • Making informed decisions.
• It allows for greater certainty in complex and unpredictable scenarios.
• While perfect prediction is impossible, probabilistic thinking helps us anticipate the most likely future and strategize effectively.

Sidebar: Conditional Probability

• Conditional probability considers how past events influence future probabilities.
• Events can be independent (e.g., coin toss) or dependent.
• Dependent events mean outcomes are influenced by preceding events.

Example: Ice Cream Choice

• If someone chooses vanilla ice cream three times in a row, it doesn’t necessarily mean vanilla is their favorite.
• The probability of choosing vanilla might be conditional on the availability of other flavors.
• If choices are made sequentially, the probability of choosing vanilla depends on what flavors others have already selected.
• Using conditional probability requires careful observation of the conditions preceding an event.

Sidebar: Anti-fragile

Nassim Taleb’s Concept of Anti-fragility

• Nassim Taleb introduced the concept of anti-fragility in his book of the same name.
• Objects can be classified as:
  • Fragile: Harmed by volatility and unpredictability.
  • Robust: Neutral to volatility and unpredictability.
  • Anti-fragile: Benefit from volatility and unpredictability (up to a certain point).

Why Aim for Anti-fragility?

• The world is inherently unpredictable and volatile, with significant events (e.g., panics, crashes, wars) having disproportionate impacts.
• Instead of trying to predict these events (which is often impossible), it’s more effective to prepare for them by becoming anti-fragile.

Strategies for Achieving Anti-fragility

1. Upside Optionality:
   • Seeking situations with a high probability of offering opportunities, even if outcomes are uncertain.
   • Example: Attending a cocktail party to potentially meet new people, even if no guarantees exist.
2. Failing Properly:
   • Never take risks that could lead to complete ruin.
   • Develop resilience to learn from failures and try again.
   • This allows for temporary failures that lead to valuable learning and reduce vulnerability to future volatility.
   • Example: Starting a business with limited experience to gain rapid feedback and learn from both successes and failures.

The Anti-fragile Mindset

• Embrace scenarios where randomness and uncertainty can be beneficial.
• Learn from failures and adapt to become more resilient.

Sidebar: Insurance Companies

Probability and Insurance

• Insurance companies are highly reliant on probabilistic thinking.
• They assess the probability of various events to determine insurance premiums.

Examples of Insured Events

• Life insurance: Probability of death at a specific age.
• Auto insurance: Probability of car accidents.
• Home insurance: Probability of damage (e.g., tree falling on a house).
• Unique events: Insurance policies have been issued for specific body parts, contests, and even celebrity assets.

Pricing Insurance Policies

• Insurers carefully evaluate relevant factors, even if not fully predictable, to determine appropriate premiums.
• They use probabilistic thinking to estimate the likelihood of events and ensure profitability on average.

Supporting Idea: Causation vs. Correlation

Correlation

• Correlation measures the relationship between two variables.
• Correlation coefficient (ranging from -1 to 1) indicates the strength and direction of the relationship.

Examples of Correlation

• No correlation: Bottled water consumption vs. suicide rate (correlation coefficient close to 0).
• Perfect correlation: Temperature in Celsius vs. Fahrenheit (correlation coefficient of 1).
• Weak to moderate correlation: Height vs. weight (correlation coefficient between 0 and 1).

Reverse Correlation

• Correlation doesn’t always imply a direct causal link.
• Example: High alcohol consumption in parents correlating with low academic success in children could have multiple explanations (e.g., parental drinking causing poor academic performance, or the reverse).

Causation

• Causation implies that one variable directly causes a change in another.
• Regression to the mean can make it difficult to determine true causation.

Regression to the Mean

• Extreme values tend to move closer to the average over time, regardless of interventions.
• Example: Depressed children treated with an energy drink might show improvement simply due to regression to the mean, not necessarily because of the drink.

Identifying True Causation

• Control groups are used in research to distinguish between real effects and regression to the mean.
• In real-life situations without control groups, it can be challenging to isolate the true causes of change.

Conclusion

• Understanding the difference between correlation and causation is crucial for accurate analysis and decision-making.
• Regression to the mean must be considered when evaluating the effectiveness of interventions or policies.

Mental Model #7: Inversion

“The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function. One should, for example, be able to see that things are hopeless, yet be determined to make them otherwise.” - F. Scott Fitzgerald

Introduction

• Inversion is a thinking tool that helps improve decision-making and problem-solving by approaching situations from the opposite perspective.
• Definition: Inversion means to upend or turn upside down. In thinking, it involves starting from the end goal or the undesirable outcome and working backwards.
• Benefits:
  • Helps identify and remove obstacles to success.
  • Provides multiple perspectives on reality.
  • Allows for more strategic decision-making.

Two Approaches to Inversion

1. Assume True/False and Determine Consequences:
   • Assume your desired outcome is either true or false.
   • Explore what else would have to be true based on that assumption.
2. Focus on What to Avoid:
   • Instead of directly pursuing a goal, identify what you want to avoid.
   • Analyze the remaining options to guide your actions.

Historical Examples of Inversion

Carl Jacobi (Mathematics)

• 19th-century German mathematician known for solving complex problems using inversion.
• “Invert, always invert.” - Jacobi’s famous advice.
• Method: Assumed a property of an axiom was correct and worked backwards to determine consequences, leading to counterintuitive insights.

Hippasus (Mathematics)

• Greek mathematician (circa 2,300 years ago), follower of Pythagoras.
• Problem: Finding the square root of 2 using direct division proved fruitless.
• Solution: Inverted the approach by trying to prove what the square root of 2 could not be.
• Outcome: Led to the discovery of irrational numbers, a significant mathematical breakthrough.

Edward Bernays (Marketing)

• Challenge: American Tobacco Company wanted to increase Lucky Strike cigarette sales to women in the 1920s, facing societal taboos against female smokers.
• Inversion: Instead of asking how to sell more cigarettes to women directly, Bernays asked:
  • “If women bought and smoked cigarettes, what else would have to be true?”
  • “What would need to change to make smoking desirable and socially acceptable for women?”
  • “How could those changes be achieved?”
• Campaign Strategies:
  • Anti-sweets campaign: Promoted cigarettes as a slimming alternative to desserts.
  • Reshaping societal perceptions:
    • Used journalists and photographers to promote the slim ideal.
    • Obtained doctor testimonials on the supposed health benefits of smoking after meals.
    • Made cigarettes ubiquitous:
      • Persuaded hotels and restaurants to add cigarettes to menus.
      • Provided magazines with articles featuring cigarette-inclusive menus.
      • Encouraged designers and manufacturers to incorporate cigarette compartments into furniture and kitchenware.
  • Linking smoking with women’s emancipation:
    • Marketed cigarettes as “torches of freedom.”
    • Organized public events featuring women smoking, like the 1929 Easter Sunday parade.
• Outcome: Successfully normalized and glamorized smoking for women, significantly increasing sales and reshaping American culture.
• “Appeals of Indirection”: Bernays’ term for his inversion-based approach, focusing on changing behaviors rather than directly selling products.

Inversion in Personal Finance

• Traditional Approach: Focusing on getting rich, chasing high returns, and attempting to outsmart the stock market.
• Inversion Approach: Focusing on avoiding poverty and eliminating financially destructive behaviors.
• Examples of Destructive Behaviors:
  • Spending more than you earn.
  • Paying high interest rates on debt.
  • Delaying saving and investing.
• Index Funds (John Bogle):
  • Problem: Difficulty in consistently beating the stock market.
  • Inversion: Focused on minimizing investor losses due to fees and poor manager selection.
  • Solution: Created index funds, a low-cost and passive investment strategy.

Force Field Analysis (Kurt Lewin)

• Psychological theory (1930s) that recognizes the importance of addressing both driving and restraining forces for successful change.
• Process:
  1. Identify the problem.
  2. Define your objective.
  3. Identify forces supporting the desired change.
  4. Identify forces impeding the desired change (Inversion).
  5. Strategize solutions by strengthening supporting forces and weakening or removing impeding forces.

Florence Nightingale and Statistics

• Crimean War (1854-1855): British Army faced a high mortality rate (23%) in the first winter.
• Nightingale’s Contribution: Used statistics to reveal that poor sanitation was the leading cause of death.
• Inversion: Focused on preventing problems (improving sanitation) rather than just treating symptoms.
• Outcome: Mortality rate dropped to 2.5% in the second winter.
• Legacy: Nightingale’s advocacy for statistics led to their use in improving public health and preventing disease.

Sun Tzu’s Wisdom

• Quote: “He wins his battles by making no mistakes.”
• Quote: “Hence, to fight and conquer in all of your battles is not supreme excellence. Supreme excellence consists in breaking the enemy’s resistance without fighting.”
• Inversion in Warfare: Emphasizes avoiding unnecessary battles and achieving victory through strategic maneuvering and weakening the opponent’s resolve.

Sherlock Holmes and Inversion

• A Scandal in Bohemia: Holmes is hired to retrieve a compromising photograph from Irene Adler.
• Inversion: Instead of directly searching for the photo, Holmes considers what would be true if Adler planned to use it for blackmail:
  • The photo would be valuable to her.
  • It would be hidden in an easily accessible location.
• Method: Stages a fake fire to prompt Adler to reveal the photo’s hiding place.
• Outcome: Successfully confirms the existence and location of the photo through logical deduction based on inversion.

Conclusion

• Inversion is a powerful tool applicable to various fields, not just mathematics or science.
• It can help overcome obstacles and achieve goals by providing a different perspective.
• “Invert, always invert” when facing challenges or seeking innovative solutions.
• Taking the results of inversion seriously can lead to significant progress and breakthroughs.

Mental Model #8: Occam’s Razor

Anybody can make the simple complicated. Creativity is making the complicated simple. - Charles Mingus, Occam’s Razor.

Introduction

• Occam’s Razor is a principle of logic and problem-solving that favors simpler explanations over more complex ones.
• Charles Mingus and William of Ockham are associated with the concept of simplicity in explanation.
• Simpler explanations are more likely to be true than complicated ones.
• Occam’s Razor encourages making decisions based on the explanation with the fewest assumptions or “moving parts.”

Why Simpler Explanations Are Preferred

• Reduced Complexity: Simpler explanations are easier to understand, falsify, and manage.
• Tendency Towards Truth: They are generally more likely to be correct.
• Efficiency: Focusing on simplicity avoids wasting time and resources on disproving complex scenarios.

Examples of Overly Complex Explanations

• Everyday Situations:
  • Assuming a car accident if a husband is late.
  • Suspecting a medical issue if a child’s growth is slightly less than expected.
  • Fearing bone cancer due to a toe ache.
• These scenarios highlight the tendency to jump to worst-case conclusions without sufficient evidence.
• More likely explanations often involve simpler factors: being caught up at work, mismeasurement, or tight shoes.

The Human Tendency Towards Complex Narratives

• We often create complex stories to explain what we observe, from human behavior to physical phenomena.
• This tendency can be useful in some contexts, such as art, but can lead to unnecessary complexity in problem-solving.

Origins and Development of Occam’s Razor

• William of Ockham: A medieval logician who formulated the principle: “A plurality is not to be posited without necessity.”
• The principle had been in use since antiquity.
• David Hume: An 18th-century Scottish philosopher who applied Occam’s Razor to the evaluation of miracles.
• Hume argued for skepticism about miracles because:
  • They are, by definition, outside our normal understanding of nature.
  • A simpler explanation is likely: misinterpretation of events or a lack of understanding of natural phenomena.

Carl Sagan and the Explanation of Miracles

• Carl Sagan, in “The Demon-Haunted World,” explained that phenomena once considered miraculous are now understood through science.
• Examples:
  • Phototropism and plant hormones explain sunflowers following the sun.
  • Future scientific advancements may explain altered states of consciousness without resorting to supernatural explanations.
• Simpler Explanation: Miracles may represent natural principles we currently don’t understand.

Dark Matter: An Example of Occam’s Razor in Science

• Vera Rubin’s Observations: In the 1970s, astronomer Vera Rubin observed galaxies rotating in a way that contradicted Newton’s laws.
• Dark Matter Hypothesis:
  • Fritz Zwicky (1933) theorized the existence of dark matter, an unseen mass influencing galaxy behavior.
  • Dark matter became the simplest explanation for Rubin’s observations.
• Despite no direct detection of dark matter, it remains the most plausible explanation for the observed phenomena.

Mathematical Justification for Occam’s Razor

• Comparing Explanations: Consider two explanations for the same phenomenon, one with 3 variables and the other with 30.
• Probability of Error:
  • If each variable has a 99% chance of being correct, the simpler explanation (3 variables) has a 3% chance of being wrong.
  • The more complex explanation (30 variables) has a 26% chance of being wrong.
• Conclusion: Simpler explanations are more robust in the face of uncertainty.

Dark Matter and Ongoing Scientific Inquiry

• Lisa Randall argues that the existence of dark matter fits with our current understanding of the universe.
• Scientific Method: Science continually seeks to validate its assumptions and refine its understanding.
• Extraordinary Claims Require Extraordinary Proof: Carl Sagan advocated for rational investigation of extraordinary claims.
• Dark matter is currently the simplest explanation, but scientists continue to search for direct evidence.
• If dark matter becomes too complex, our understanding of gravity might need revision.
• Vera Rubin herself considered the possibility that a modified understanding of gravity might be the solution.
• Such a claim would require extraordinary proof.

Simplicity and Efficiency

• Limited Resources: Occam’s Razor helps prioritize investigations by focusing on the simplest explanations.
• Avoiding Dead Ends: Without this filter, we risk wasting time and resources on improbable scenarios.
• Power of Simplicity: Simple solutions can be surprisingly powerful and effective.
• Avoiding Unnecessary Complexity: Sometimes, complex solutions mask underlying flaws that need to be addressed.
• Making Decisions Based on Reality: Simplicity helps us see things as they are and make more informed decisions.

Examples of the Power of Simple Solutions

• Ivanhoe Reservoir:
  • Problem: Preventing the formation of bromate, a carcinogen, in the Los Angeles water supply.
  • Complex Solutions: Building a tarp or a retractable dome over the reservoir (infeasible).
  • Simple Solution: Using bird balls (floating spheres used at airports) to shade the water.
  • Result: Effective, inexpensive, and low-maintenance solution.
• Bengal Tiger Attacks:
  • Problem: Preventing tiger attacks on villagers in India’s Ganges Delta.
  • Complex Solutions: Various weapons and deterrents proved ineffective.
  • Simple Solution: Wearing masks with human faces on the back of the head to deter tigers who preferred to attack unseen prey.
  • Result: Significant reduction in tiger attacks for those wearing the masks.

Caveats to Occam’s Razor

• Irreducible Complexity: Some things are inherently complex and cannot be simplified further.
• Examples:
  • Fraudulent Schemes: Pyramid schemes and Ponzi schemes are complex phenomena with no single, simple explanation.
  • Human Flight: Achieving flight required understanding complex concepts like airflow, lift, drag, and combustion.
• Dealing with Complexity: When something cannot be simplified further, we must address its inherent complexity.

Determining the Limits of Simplicity

• Computer Code Analogy: Simplifying code must ensure it still performs its intended functions.
• Maintaining Accuracy: An explanation can be simplified only as long as it remains accurate and comprehensive.

Conclusion

• Focusing on simplicity can be a hallmark of genius, but it requires careful consideration.
• Remembering that simpler explanations are more likely to be true helps conserve time and energy.
• Occam’s Razor is a valuable tool for problem-solving and decision-making in various fields.

Sidebar: Occam’s Razor in the Medical Field

• Diagnosis: Occam’s Razor helps doctors prioritize diagnoses based on the likelihood of different conditions.
• Example: Flu-like symptoms are more likely to indicate the flu than Ebola.
• Bayesian Updating: Doctors use background knowledge and new information to update their assessment of probabilities.
• “When you hear hoofbeats, think horses, not zebras.”: This medical adage emphasizes the importance of considering the most common explanations first.
• Countering Hypochondria: For patients, Occam’s Razor can help reduce anxiety by focusing on the most likely explanations for symptoms.

Sidebar: The Razor in Leadership

• Louis Gerstner and IBM: When Gerstner took over IBM during a period of struggle, he rejected calls for a grand vision.
• Focusing on the Essentials: Instead, he prioritized serving customers, competing effectively, and focusing on profitable businesses.
• Simple Execution: Gerstner’s approach emphasized tough-minded business execution over complex strategies.
• Success Through Simplicity: By the end of the 1990s, IBM had recovered without relying on grand visions or technological overhauls.
• This example demonstrates the power of simplicity in leadership and problem-solving.

Mental Model #9: Hanlon’s Razor

I need to listen so well that I hear what is not said - Thuli Madonsela

Introduction

• Hanlon’s Razor is a principle suggesting that one should not attribute to malice what can be more easily explained by stupidity.
• Origin: Though difficult to trace definitively, it is often attributed to Robert J. Hanlon.
• Benefit: Helps avoid paranoia and ideology by encouraging a search for alternative explanations rather than assuming malicious intent.
• Core Idea: People make mistakes; it’s important to consider less intentional explanations for events.
• Principle of Least Intent: The most likely explanation is often the one that assumes the least amount of deliberate intent.

Examples of Misattributing Intent

Road Rage

• Assuming malice when someone cuts you off in traffic implies a complex, intentional act:
  • Noticing you specifically.
  • Gauging your speed and trajectory.
  • Deliberately swerving to impede your progress without causing an accident.
• A simpler explanation: The driver didn’t see you; it was a mistake, not malice.

The Linda Problem (Kahneman and Tversky, 1982)

• Experiment: Participants were presented with a description of Linda:
  • 31 years old, single, outspoken, very bright.
  • Majored in philosophy.
  • Concerned with discrimination, social justice, and participated in anti-nuclear demonstrations.
• Question: Which is more probable?
  1. Linda is a bank teller.
  2. Linda is a bank teller and is active in the feminist movement.
• Results: The majority chose option 2, exhibiting the fallacy of conjunction.
• Explanation: The vivid description led participants to assume Linda was a feminist, influencing their probability judgment despite the logical error.
• Implications:
  • Vivid evidence can override logical reasoning.
  • We tend to over-conclude based on limited information.
  • We readily incorporate unrelated factors if they align with our existing beliefs.
• Criticism: The wording of the problem might have influenced the results. However, the study highlights how framing can affect our judgment.

The Connection to Hanlon’s Razor

• When faced with unfavorable or seemingly wrong situations, we often default to assuming intentional wrongdoing.
• However, unintentional explanations (stupidity, ignorance, error) are statistically more likely.
• Assuming malice, like in the Linda problem, can lead to faulty conclusions.
• Vivid negative events can trigger emotional responses, hindering rational assessment.
• Hanlon’s Razor serves as a corrective, reminding us to consider less malicious explanations.

Consequences of Assuming Malice

Paranoia

• Constantly assuming malice can lead to paranoia, believing you are the target of others’ ill intentions.

Self-Centeredness

• Assuming malice places you at the center of everyone’s actions, an egocentric worldview.

Overlooking Other Explanations

• The focus on malice obscures more common explanations like ignorance, stupidity, and laziness.

Oscar Wilde Quote

“One is tempted to define man as a rational animal who always loses his temper when he is called upon to act in accordance with the dictates of reason.”

• This quote highlights the human tendency to abandon reason in favor of emotional responses, like assuming malice.

Historical Example: The Fall of the Western Roman Empire

Emperor Honorius and General Stilicho (408 AD)

• Honorius, Emperor of the Western Roman Empire, suspected his general, Stilicho, of treason.
• Stilicho was a skilled and loyal general but had made a controversial decision:
  • He advised the Roman Senate to negotiate with Alaric, the leader of the Visigoths, who had attacked Rome.
  • This damaged Stilicho’s reputation and fueled Honorius’s suspicions.
• Honorius, assuming malicious intent (Stilicho wanting the throne), ordered Stilicho’s arrest and execution.
• Consequences:
  • Without Stilicho, Rome’s military strength declined.
  • Alaric sacked Rome in 410 AD, a major contributing factor to the empire’s eventual collapse.

Benefits of Applying Hanlon’s Razor

Countering Confirmation Bias

• Hanlon’s Razor helps overcome confirmation bias, the tendency to favor information confirming existing beliefs.

Finding Effective Solutions

• Recognizing stupidity or error allows for more realistic and effective problem-solving.

Avoiding Defensive Mode

• Assuming malice triggers a defensive mindset, limiting opportunities and hindering a broader perspective.

The Man Who Saved the World: Vasily Arkhipov (1962)

Cuban Missile Crisis

• During the height of the Cuban Missile Crisis, US destroyers and Soviet submarines were in a standoff.
• The US planned to drop depth charges to force the Soviet subs to surface (without informing the Soviets).
• Soviet submarine B-59:
  • Carried a nuclear weapon.
  • Was unaware of the US plan.
• Vasily Arkhipov: An officer on B-59.

Assuming the Worst vs. Staying Calm

• When the depth charges began, the Soviet captain believed war had started and wanted to launch the nuclear torpedo.
• Arkhipov’s Intervention: He refused to authorize the launch, arguing for caution and contact with Moscow.
• Reasoning: Instead of assuming malice, he considered the possibility of a mistake or miscommunication.
• Outcome: B-59 surfaced, averting a potential nuclear war.
• Recognition: Arkhipov’s role in preventing disaster was only revealed decades later.

Limitations of Hanlon’s Razor

• Not a Universal Rule: While helpful, it shouldn’t be applied indiscriminately.
• Focus on Unintentional Actions: It primarily addresses situations where actions are driven by ignorance, error, or laziness, not deliberate malice.
• Principle of Least Effort: It suggests that actions requiring less effort (e.g., stupidity) are more likely than those requiring complex planning (malice).

Conclusion

• Hanlon’s Razor reminds us that true villains are rarer than we might think.
• People are fallible, prone to mistakes, laziness, and flawed thinking.
• Acknowledging this human reality leads to a more effective and compassionate approach to life.

Sidebar: The Devil Fallacy (Robert Heinlein, 1941)

• Doc Graves, a character in Robert Heinlein’s “Logic of Empire,” describes the devil fallacy:

  “You have attributed conditions to villainy that simply result from stupidity.”

• Explanation: The devil fallacy involves attributing negative outcomes to intentional evil rather than incompetence or unintended consequences.

• Hanlon’s Razor as a Counter: Hanlon’s Razor helps us avoid the devil fallacy by reminding us to consider less malicious explanations.

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About Me:

• I’m Christian Mills, a deep learning consultant specializing in computer vision and practical AI implementations.
• I help clients leverage cutting-edge AI technologies to solve real-world problems.
• Learn more about me or reach out via email at [email protected] to discuss your project.