Mental Models: A Thinking Toolkit
A comprehensive collection of mental models from physics, biology, psychology, economics, and systems thinking. These models help you understand complex problems, make better decisions, and see patterns across disciplines.
Epistemology (How We Know Things)
- Map is Not the Territory — Cautions against confusing mental representations with reality; requires continually updating models based on experience.
- Occam’s Razor — Select the simplest explanation with fewest assumptions when faced with competing alternatives.
- Thought Experiment — A device of imagination to investigate nature of things, clarify thinking, and reveal hidden assumptions.
- Scientific Method — A structured approach (hypothesis, computation, comparison, repetition) to test beliefs against empirical reality.
- Relativity (Perspective) — Perceptions are shaped by unique vantage points; gain complete view by considering multiple valid perspectives.
- Sampling — Ensure the group studied is large and representative enough to draw statistically sound conclusions.
Problem Solving
- First Principles Thinking — Break down complex problems into fundamental, non-reducible truths to unlock creative solutions.
- Inversion — Approach a problem backward (e.g., “What would guarantee failure?”) to identify and remove obstacles to success.
Decision Making
- Second-Order Thinking — Go beyond immediate consequences to anticipate ripple effects (“And then what?”) for long-term beneficial outcomes.
- Probabilistic Thinking — Navigate uncertainty by estimating likelihood of outcomes, often using base rates/priors.
- Circle of Competence — Stay within your personal sphere of deep expertise where judgments are reliable.
- Trade-offs / Opportunity Cost — Make choices intentionally by calculating what must be given up when one option is chosen over another.
Learning
- Feynman Technique — Explain complex concepts simply (as if to a child) to identify knowledge gaps and deepen comprehension.
- Ordinary Language Test — True understanding requires explaining concepts in simple language without jargon.
Creativity
- Feynman’s Advice: Play More! — Encourage ‘serious play’ to maintain childlike curiosity, leading to rejuvenation and innovation.
Psychology & Behavioral Biases
- Hanlon’s Razor — Assume stupidity or error rather than malice; encourages practical solutions over paranoia.
- Intellectual Humility — Recognize limits of your knowledge and challenge assumptions to foster genuine learning.
- Self-Preservation — Behavior is fundamentally driven by instinct to protect existence, identity, and psychological well-being.
- Tendency to Minimize Energy Output — Recognize universal inclination toward least resistance; consciously correct where it hinders value.
- Framing — Shape perception by deciding what information to emphasize, minimize, or omit.
- Survivorship Bias — Seek out failures and “silent evidence” to avoid false conclusions based only on existing successes.
- Availability Heuristic — Salient, recent, or loud information is often overestimated in likelihood.
- Bias from Incentives — Humans distort thinking when it serves their interests; predict behavior by analyzing motivations.
- Confirmation Bias — The deeply ingrained habit of seeking information that confirms pre-existing beliefs.
- Representativeness Heuristic — Account for statistical base rates and avoid broad generalizations or stereotypes.
- Hindsight Bias — The false belief that you “knew it all along” after an outcome occurs; counter with decision journals.
- First-Conclusion Bias — Mind’s tendency to settle on first idea to save energy, often leading to erroneous results.
- Tendency to Overgeneralize from Small Samples — Create rules from insufficient instances; ignore reliability of large sample sizes.
- Narrative Instinct — Recognize the human drive to seek meaning in stories; this constructs organizations and shared beliefs.
- Social Proof — Understand instinct to conform to group behavior; can lead to both cooperation and foolish actions.
Physics & Physical Systems
- Thermodynamics / Entropy — Order requires energy; isolated systems trend toward disorder. Focus energy on prevention and maintenance.
- Inertia / Momentum — Inertia is resistance to change; momentum is force of mass in motion. Continuous movement easier than starting/stopping.
- Velocity — Both speed and direction matter; ensures progress toward correct outcome, not just movement.
- Friction / Viscosity — Forces that oppose movement; minimize these to improve efficiency and progress.
- Critical Mass — Determine necessary resources to reach tipping point and achieve self-sustaining growth.
- Surface Area — Determines how much an object interacts with environment; useful for evaluating exposure and cognitive diversity value.
Chemistry & Reaction Systems
- Activation Energy — Evaluate total energy required to kick-start a reaction/project through to sustained conclusion.
- Catalysts / Autocatalysis — Identify factors that accelerate reactions without being consumed; autocatalysis creates self-sustaining growth.
- Alloying — Mix different elements (like knowledge) to create something stronger than the sum of isolated parts.
Biology & Evolution
- Evolution (Adapt or Die) — Plan for constant change; environmental pressures force systems/species to adapt or become extinct.
- Red Queen Effect — Fight complacency by recognizing continuous adaptation required merely to maintain relative performance.
- Exaptation — Flexibility in repurposing existing tools, skills, or knowledge for new functions; key to innovation.
- Replication — The process of copying information; necessary for survival but imperfect, with errors fueling adaptation.
- Cooperation / Symbiosis — Working together expands possibilities by creating emergent properties greater than individual components.
- Niches — The specific role where a species or idea can thrive; focus on being the best within your unique context.
- Ecosystems — Individual components are interdependent; helps analyze systemic health and avoid unintended consequences.
- Law of the Minimum — Critical limiting factor dictates maximum output, even if all other factors are abundant.
- Convergence — Different, unrelated species find same solutions to same environmental problems.
Systems Engineering & Design
- Leverage — Amplify effort to achieve outsized results; identify key inputs that cascade into massive results.
- Margin of Safety — Build buffers, redundancy, or extra capacity into a system to survive unexpected stress.
- Feedback Loops — System output influences input; vital for observing changes, making adjustments, and ensuring dynamic systems.
- Bottlenecks — Identify single limiting constraint; focus all effort on alleviating it for maximum overall improvement.
- Algorithms — A list of crisp, unambiguous steps that reliably produces results; functions as ‘if-then machine’.
- Hierarchy — The invisible scaffolding that organizes systems, enabling specialization and complex behaviors.
Systems Optimization & Complexity
- Global and Local Maxima — Achieving goals is not steady upward climb; requires continually seeking best possible solution.
- Emergence / Irreducibility — Complex systems create unpredictable results greater than sum of parts; some cannot be broken down.
- Equivalence — Different inputs can produce identical results; helps simplify systems by focusing on essential components.
- Law of Diminishing Returns — Optimization yields easier wins initially; further improvements become progressively harder.
Economics
- Gresham’s Law — Low-quality options may drive out high-quality ones if incentives are misaligned.
Social Dynamics
- Reciprocity — Positive initiative often yields proportional returns; useful for starting and maintaining “win-win” relationships.
Core Applications
Decision Making: Probabilistic thinking, second-order thinking, circle of competence, trade-offs.
Problem Solving: First principles thinking, inversion, thought experiments, feedback loops.
Systems Understanding: Thermodynamics, emergence, ecosystems, bottlenecks, hierarchy.
Avoiding Bias: Intellectual humility, survivorship bias, confirmation bias, hindsight bias, frame your thinking explicitly.
Innovation & Adaptation: Exaptation, convergence, evolution, red queen effect, alloying.
Integration Strategy
Combine models across disciplines. For example:
- Use feedback loops (systems) + second-order thinking (decision) to design robust solutions.
- Apply first principles (problem solving) + intellectual humility (psychology) for deep learning.
- Leverage catalysts (chemistry) + critical mass (physics) for sustainable growth. “category”: “Epistemology”, “explanation”: “Cautions against confusing mental representations (models) with the complex reality they describe; requires models to be continually updated based on experience.”