Mental Models - The Comprehensive Guide

Mental Models: how to avoid “smoov.bra.in2023” reasoning errors and suboptimal outcomes.

The human brain is a fascinating machine. It’s a complex system comprised of billions of interactions which shape our thoughts, feelings, dreams, memories, and ultimately make us who we are.

Powering the human earth-suit, the brain is (quite arguably) the most advanced piece of hardware/software ever made.

But the human brain also has its limitations (e.g., computational power, multi-tasking, reasoning, attentional, etc.) which can cloud judgment and inhibit decision making ability.

It’s a very powerful but limited machine.

And it doesn’t even come with an instruction manual.

We had to just figure it out on our own.

So, for the past 1,000 years or so we’ve been trying to reverse engineer our core programming.

Now, we must take into account all we have learned, and, using our own brain, ask our brain:

Is there a limit to the human brain?
Is human IQ/intellect capped at a certain level?
Is there an inherent limit to what our mind can learn and understand?

Let us attempt to work through the numbers.

Experts estimate that the human brain has about 100 billion neurons.

Most of these same experts contend that each neuron can fire with a frequency as high as ~200 times per second.

It may be higher, it may be lower, but for this shitty analysis let’s assume this is the mean.

Each of these 100 billion neurons is connected to about 7,000 other neurons.

Note: the firing rate of neurons can vary widely depending on the type of neuron and its role in the nervous system. Here are some general guidelines:

Frequency: Neurons can fire at frequencies as low as a few times per minute to as high as several hundred times per second.
Type-Specific Rates: Sensory neurons may have different firing rates compared to motor neurons or interneurons. For example, some sensory neurons fire very rapidly (up to 500-800 Hz) in response to stimuli.
Context-Dependent: The firing rate can also be context-dependent. For example, neurons involved in alertness or stress responses may fire more rapidly in certain situations.

Taking all of these variables into consideration, we can make the case that 200,000,000,000,000 bits of information are transmitted every second inside your brain.

That’s 200 million million.

You could make a very strong case that the human brain is a very powerful machine.

But, like every machine, it has its limitations.

Here are some of the key constraints:

Cognitive Limits

Working Memory Capacity: The brain can only hold a limited amount of information in working memory at any given time, often cited as “7±2” chunks of information.
Attentional Resources: Humans have a limited capacity for attention, which restricts the amount of information that can be processed simultaneously.
Cognitive Load: Complex tasks can overwhelm the brain’s processing capacity, leading to errors or reduced comprehension.
Decision Fatigue: Making repeated decisions can wear down the brain’s decision-making capabilities, leading to poorer choices over time.

Biological Constraints

Metabolic Costs: The brain consumes a significant amount of energy, and there are limits to how much energy it can use.
Neural Plasticity: While the brain can adapt and change, there are limits to its plasticity, especially as one ages.
Speed of Processing: Neurons can only fire so fast, and synaptic transmission takes time, setting a speed limit on how quickly the brain can process information.

Emotional and Psychological Limits

Stress and Anxiety: High levels of stress hormones like cortisol can impair cognitive function and memory.
Cognitive Biases: The brain is susceptible to a range of biases that can impair judgment and decision-making.
Emotional Reasoning: Emotional states can significantly impact logical reasoning and decision-making capabilities.

Limits in Perception

Sensory Thresholds: There are limits to what the human senses can perceive; anything below these thresholds is imperceptible to humans.
Change Blindness: The brain often fails to notice significant changes in the environment when attention is diverted.

This is just a general list, but there are many other constraints, and sub-constraints of the primary constraints.

Developer’s Note: If this does not make sense to you, it’s likely a runtime error… [smoov.bra.in2023] needs more CPU and RAM to process fully but the sim params have prevented this. This is an ongoing issue that the developers expected but have been unable to correct.

To make things worse, scientists have theorized that human intelligence may be close to its evolutionary limit. Limits that have been set by the laws of physics.

Note: a bigger brain could help, but brain size carries diminishing returns. A bigger brain would help us be “smarter”, but a larger brain would also consume more energy and be less efficient, taking up a disproportionate amount of space in your head.

So, how are we supposed to control and manage a superpowered machine that is apparently full of glitches and bugs? And only releases a software update maybe once every 50,000 years?

We’ve established that the brain has processing, multi-tasking, reasoning, attentional, and biological limitations.

But there’s some hope.

Technology (from an external source) allows us to expand the capacity of our powerful but limited brains.

For example, over the past 1,000 years, our brains have been able to solve difficult physics and calculus problems, and humans have progressed from living in dank caves to fancy skyscrapers. This means that the computational power our Neanderthal ancestors used to survive can be applied to a much wider range of problems than nature intended, natural selection notwithstanding.

And the humans began to use technology in this way much earlier than you would expect.

Our ancient ancestors built and used tools to help them solve problems and survive, and we are still doing the same thing today, only in slightly different ways.

One of the modern tools that can help humans solve the (often difficult) problems of the modern age are mental models, which are designed to provide a simplified, internalized representation of our complex world, and serve as cognitive frameworks that help us understand and predict phenomena in various domains.

Definition: What Are Mental Models?

Let’s begin with what a mental model is. A mental model is an explanation of how something works. It’s a cognitive structure that represents your understanding of a concept, system, or phenomenon. Think of them as a mental shortcut or cheat code that can help you simplify complex realities into simple solutions.

Mental models also help you understand life.

For example, Game Theory is a mental model that helps you understand how trust and relationships work. Probability Theory is a mental model that can help you estimate the likelihood of any specific outcome. First Principles is a mental model can help you understand how to solve hard problems by breaking them down into their most basic elements and then putting them back together.

There are many types of models humankind has generated throughout history.

Some of the popular ones include:

Descriptive Models: These models represent how things are. For example, the supply and demand model in economics provides a framework for understanding market dynamics.
Normative Models: These models outline how things should be, often serving as idealized standards for judgment. For example, utility theory in economics suggests how rational agents should make choices to maximize utility.
Prescriptive Models: These models offer specific guidelines or steps for achieving a particular outcome. The scientific method is a prescriptive model for conducting empirical research.

That said, the application of these models can be wide reaching, but some of the most popular applications include:

In problem solving: mental models serve as tools for solving complex problems by breaking them down into more manageable components.
In decision-making: mental models help in evaluating multiple variables and their potential impact, thereby aiding in the selection of the best course of action.
In prediction: mental models enable individuals to make predictions about future events based on their understanding of existing systems.

The concept of mental models can be traced back to the works of Kenneth Craik, who posited that the mind constructs “small-scale models” of reality to anticipate events. Over time, the idea has been refined and expanded upon by scholars in psychology, cognitive science, and philosophy.

There are tens of thousands of mental models out there (every discipline has its own set), but you don’t need to know all of them.

You just need to know the basics.

The meat.

I created this list to help yet you started.

This list will highlight some of the most important models in a variety of disciplines.

Many of them have been repeatedly useful to me over the years, both in the office (from spaceflight probability to hiring decisions to general strategy) and outside the office (e.g. investing, personal life, and, of course fantasy football).

I have included the most important and useful mental models on this page. I have separated them out by industry below.

The Core Mental Models

First Principles Thinking: A problem-solving approach that involves breaking down complex issues into their most basic, fundamental elements and then reassembling them from the ground up.
Second-Order Thinking: The practice of considering not just the immediate effects of an action or decision, but also its subsequent, indirect consequences.
Probabilistic Thinking: The approach of evaluating the likelihood of various outcomes in decision-making, often using statistical or mathematical models.
Inversion: A problem-solving technique that involves looking at problems from the opposite end, asking what you should avoid rather than what you should do.
The Map is Not the Territory: The idea that our mental or symbolic representations of reality are not the same as reality itself, serving as a reminder to question our assumptions and models.
Circle of Competence: The concept that focuses on operating within the areas where one has the most expertise and knowledge, while being cautious when venturing outside of it.
Occam’s Razor: The principle that the simplest explanation is usually the best one.
Hanlon’s Razor: The heuristic that suggests attributing actions to ignorance or mistake rather than to malice when there is no clear evidence to the contrary.
Thought Experiment: A hypothetical scenario used to explore ideas, theories, or test hypotheses in a way that is not necessarily bound by real-world limitations.

The Mental Models of Microeconomics

Incentives: Factors that motivate individuals or entities to perform certain behaviors, often used in economics to explain how choices are made based on the prospect of reward or penalty.

Trade-offs: The concept that describes the sacrifices made when choosing one option over another, often involving a compromise in benefits or resources.

Economies of Scale: The cost advantages that enterprises obtain due to their scale of operation, resulting in a reduction in average costs per unit of output as the scale of output increases.

Efficient Market Hypothesis: The theory that all available information is already reflected in asset prices, making it impossible to consistently achieve returns in excess of average market returns on a risk-adjusted basis.

Diversification: The strategy of allocating capital in a way that reduces the exposure to any single asset or risk, aiming to maximize return by investing in different areas that would each react differently to the same event.

Common Knowledge: Information that is known by all participants in a market or situation, even if they do not know that others share that knowledge.

Opportunity Cost: The cost of forgoing the next best alternative when making a decision.
Creative Destruction: The process by which new innovations replace outdated technologies, leading to economic growth but also temporary dislocation.
Comparative Advantage: The ability of an entity to produce a good or service at a lower opportunity cost than another entity.
Specialization (Pin Factory): The division of labor where each worker becomes an expert in one isolated area of production, thereby increasing efficiency.
Seizing the Middle: The strategy of targeting a broad market with products that have a balanced mix of price and features, as opposed to focusing on just a high-end or low-end market.
Trademarks, Patents, and Copyrights: Legal protections for intellectual property that grant exclusive rights to the creators and owners.
Double-Entry Bookkeeping: An accounting method that records each transaction twice, as both a debit and a credit, to ensure the accounting equation stays balanced.
Utility (Marginal, Diminishing, Increasing): The satisfaction derived from consumption, with marginal utility referring to the additional satisfaction from one more unit, diminishing utility indicating reduced additional satisfaction, and increasing utility indicating growing additional satisfaction.
Bribery: The illegal or unethical act of offering something of value to influence the actions of an individual in the decision-making position.
Arbitrage: The practice of buying and selling the same asset in different markets to profit from price differences.
Supply and Demand: The fundamental model for determining the price and quantity of goods in a market based on how much is available and how much consumers want.
Scarcity: The basic economic problem that arises because people have unlimited wants but resources are limited.
Mr. Market: A hypothetical investor described by Benjamin Graham, used to illustrate the emotional ups and downs of the market and the importance of making rational investment decisions.
Adverse Selection: The tendency of those in dangerous jobs or high-risk lifestyles to get life or disability insurance.
Asymmetric Information: When one party has more or better information than the other, leading to market inefficiencies.
Diminishing Returns: The reduction in the incremental benefit derived from an additional unit of input.
Game Theory: The study of strategic interaction among rational decision-makers.
Marginal Utility: The additional satisfaction gained from consuming one more unit of a good or service.
Moral Hazard: The tendency to take greater risks when the cost of those risks is borne, in whole or in part, by others.
Principal-Agent Problem: The conflict of interest that arises when an agent is supposed to act in the best interest of a principal.
Sunk Cost Fallacy: The mistake of incorporating past costs into current decisions.

The Mental Models of Military and War

Asymmetric Warfare: A form of conflict where one side uses unconventional tactics and strategies to offset the conventional strengths of a more powerful opponent.
Two-Front War: A military situation where a country or alliance has to fight against enemies on two geographically separate fronts, complicating logistics and strategy.
Counterinsurgency: A military or political strategy aimed at defeating a rebellion or insurgency by winning the support of the population rather than solely using military force.
Mutually Assured Destruction: A deterrence strategy that posits that two or more sides would refrain from initiating a full-scale conflict due to the guarantee of catastrophic retaliation from both parties.
Seeing the Front: The concept that emphasizes the importance of firsthand observation and situational awareness in understanding the realities of a conflict.

The Mental Models of Numeracy

Distributions: The arrangement of values showing their frequency or relationship in a set.
Compounding: The process where the value of an investment increases because the earnings on an investment, both capital gains and interest, earn interest as time passes.
Equivalence: The condition of being equal or equivalent in value, worth, function, etc.
Surface Area: The measure of the total area that the surface of an object occupies.
Global and Local Maxima: Points in a data set where a variable reaches its highest value either within a given range (local) or over the entire domain (global).
Sampling: The selection of a subset of individuals from a statistical population to estimate characteristics of the whole population.
Randomness: The lack of pattern or predictability in events, often understood as a measure of uncertainty.
Regression to the Mean: The phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement.
Multiplying by Zero: In arithmetic, any number multiplied by zero results in zero.

The Mental Models of Physics and Chemistry

Relativity: A framework in physics, most famously articulated by Einstein, that describes how the laws of physics remain constant for all observers, regardless of their motion or frame of reference.
Friction and Viscosity: Forces that oppose the relative motion of two surfaces in contact (friction) or the flow of a fluid (viscosity).
Velocity: A vector quantity that describes the speed and direction of an object’s motion.
Reciprocity: In the context of physics and chemistry, this refers to the mutual influence or interdependence between interacting elements, often seen in phenomena like Newton’s Third Law of Motion.
Thermodynamics: The study of energy, heat, work, and how they interact, often encapsulated in four fundamental laws that describe the conservation of energy and the direction of energy flow.
Inertia: The tendency of an object to resist changes in its state of motion, described by Newton’s First Law of Motion.
Catalysts: Substances that speed up chemical reactions by lowering the activation energy, but are not consumed in the process.
Alloying: The process of combining two or more metals to create a material with properties different from its constituent elements.
Activation Energy: The minimum amount of energy required to initiate a chemical reaction.

The Mental Models of Biology

Evolution Part 1: Natural Selection and Extinction: The process by which traits that enhance survival and reproduction become more common in successive generations, while less advantageous traits lead to extinction.
Evolution Part 2: Adaptation and The Red Queen Effect: The concept that organisms must continually adapt to their environment just to maintain their current fitness level, as articulated by the Red Queen hypothesis which posits that constant adaptation is necessary to keep up with evolving competitors or challenges.
Ecosystems: A complex network of interactions among organisms and their environment, where changes to one element can have cascading effects on the whole.
Tendency to Minimize Energy Output (Mental and physical): The biological inclination to conserve energy whenever possible, optimizing for the most efficient pathways in both cognitive and physical functions.
Equilibrium (Homeostasis): The self-regulating process by which biological systems maintain stability while adjusting to changing external conditions.
Heredity: The passing on of traits from parents to their offspring, either through sexual or asexual reproduction.
Niches: The specific environmental conditions and roles that an organism is best adapted to, shaping its behavior and interactions within an ecosystem.
Self-Preservation: The innate behavior in organisms to protect themselves from harm and increase their chances of survival and reproduction.
Replication: The process by which biological entities, such as cells or genes, make copies of themselves, serving as the basis for growth, repair, and inheritance.
Cooperation: The phenomenon where organisms work together, often in a mutually beneficial relationship, to achieve a common goal or enhance their survival.
Hierarchical Organization: The arrangement of biological systems into a series of increasingly complex levels, from molecules to cells to organisms to ecosystems.
Incentives: Factors that motivate organisms to perform certain behaviors, often linked to survival or reproductive advantages.
Signaling: The transfer of information between cells or organisms using a variety of biochemical agents, serving to regulate or coordinate actions.

The Mental Models of Math and Engineering

Power Laws: Mathematical relationships between two quantities, where one quantity varies as a power of another, often used to describe phenomena with heavy tails or a lot of variance.
Break Points: Locations within a system where a failure is most likely to occur, often identified to improve system robustness.
Leverage: In physics, the use of a lever and fulcrum to amplify an applied force, allowing for the movement of heavier objects with less effort.
Margin of Safety: The factor of safety built into engineering designs to account for uncertainties and potential errors, ensuring that systems can handle loads greater than they were designed for.
Redundancy: The duplication of critical components or functions in a system to increase its reliability and decrease the likelihood of failure.
Normal Distribution (Bell Curve): A type of continuous probability distribution for a real-valued random variable, commonly used in statistics as a simple model for complex random variables.
Permutations and Combinations: Concepts in combinatorial mathematics that deal with the arrangement of objects (permutations) and the selection of objects without regard to the order of selection (combinations).

The Mental Models of Psychology

Anchoring: The cognitive bias where individuals rely too heavily on an initial piece of information when making decisions.
Classical Conditioning (Pavlov): A learning process where a neutral stimulus becomes associated with a significant stimulus, leading to a similar response.
Commitment and Consistency Bias: The tendency to be internally and externally consistent with prior commitments and attitudes.
Hyperbolic Discounting: The tendency for people to increasingly choose a smaller-sooner reward over a larger-later reward as the delay occurs sooner rather than later.
Illusion of Control: The tendency for people to overestimate their ability to control events.
Loss Aversion: The psychological phenomenon where the pain of losing is psychologically about twice as powerful as the pleasure of gaining.
Maslow’s Hierarchy of Needs: A motivational theory comprising a five-tier model of human needs, often depicted as hierarchical levels within a pyramid.
Mere Exposure Effect: The psychological phenomenon where people tend to develop a preference for things merely because they are familiar with them.
Operant Conditioning (Skinner): A type of learning where behavior is strengthened or weakened by the consequences that follow it.
Reciprocity: The social norm where a positive action leads to another positive action, rewarding kind actions.
Status Quo Bias: The psychological preference for the current state of affairs, resisting change.
Survivorship Bias: The logical error of focusing on aspects or individuals that “survived” some process while overlooking those that did not.
Tribalism: The strong loyalty to one’s own social or cultural group, often in opposition to others.

The Mental Models of Reasoning

The Scientific Method: A systematic and logical approach to discovering how things in the universe work, typically involving observation, hypothesis formation, experimentation, and conclusion.
Inversion: A problem-solving technique that involves looking at problems from the opposite end, asking what you should avoid rather than what you should do.
Riding the Wave: A metaphorical concept that involves adapting to and taking advantage of the momentum of a situation rather than trying to alter its course.
Working Backward: A problem-solving strategy that starts with the end goal and works in reverse to determine the steps needed to reach that goal.

The Mental Model of Systems Thinking

Feedback Loops: Mechanisms where the output or result influences the input in a cyclical process.
Equilibrium: The state where all competing influences in a system are balanced.
Bottlenecks: Points in a system where the capacity is limited.
Scale: The concept that describes how characteristics of a system change as its size changes.
Margin of Safety: The buffer built into a system to account for uncertainties.
Churn: The rate at which elements or participants enter and leave a system.
Algorithms: Sets of rules designed to perform specific tasks within a system.
Critical Mass: The minimum amount of resources required for a system to sustain itself.
Emergence: The phenomenon where collective behavior results in new system properties.
Irreducibility: The idea that a system cannot be fully understood by analyzing its individual components.
Law of Diminishing Returns: The principle that incremental benefit decreases beyond a certain point.