The Crazy Long Checklist of Mental Models #4

Borrowing from Farnam Street, then editing heavily because they don’t have much for a lot of these. Oh, and I added some of my own, and deleted a bunch of theirs that I felt had been covered in previous checklists, and then a lot of this has been copied and pasted from sources throughout the web without attribution because I’m using this primarily as my own notes:

  • Pricing Power: One of the single-most important things a business can have is pricing power. This means that, as they raise their prices, their demand does not fall as much as you would expect; a doubling of price doesn’t mean that you lose half of your customers. Pricing power indicates that your product is thought of as necessary by your customers, and also allows you to set your prices for optimal profit. I wrote a longer piece on Quora about how to look for pricing power in startups.
  • Economies of Scale: As you grow a business, you will be more profitable if your business can sell more of the same thing using the same underlying assets. One example here is software — traditionally, you build it once and then you can make money off of it for a long period of time — which has great economies of scale. An example of bad economies of scale is a yoga studio — there are only so many people a yoga teacher can teach at one time in a studio setting, and to teach more people, you need to add more teachers and more space.
  • Diminishing Returns: Despite economies of scale, you will reach a point as you add resources / investment in which you will produce incrementally less than you did before. For example, adding more programmers to a software project doesn’t make it get done faster, as each individual programmer has a learning curve and is reliant on the work of others.
  • Double-Entry Accounting: In accounting, if you add something to one column, you must take it out of another. This is the basis for all modern accounting.
  • Basic profitability: As a business, your job is to sell what you make for more money than it costs you to produce. You’re supposed to optimize this to be the highest possible price you can charge. However, you must be well-aware of all of your costs, and be sure to look at your profitability on any sale as it relates to all costs that went into that sale rather than carefully selected cost items.
  • Mr. Market: This is a concept from investor Ben Graham and retold by Warren Buffett about the stock market being a person they call ‘Mr. Market’: The market may ignore business success for a while, but eventually will confirm it. As Ben said: “In the short run, the market is a voting machine but in the long run it is a weighing machine.” The speed at which a business’s success is recognized, furthermore, is not that important as long as the company’s intrinsic value is increasing at a satisfactory rate. In fact, delayed recognition can be an advantage: It may give us the chance to buy more of a good thing at a bargain price. Mr. Market is there to serve you, not to guide you. It is his pocketbook, not his wisdom, that you will find useful. If he shows up some day in a particularly foolish mood, you are free to either ignore him or to take advantage of him, but it will be disastrous if you fall under his influence. Indeed, if you aren’t certain that you understand and can value your business far better than Mr. Market, you don’t belong in the game.
  • Circle of Competence: Again, from Warren Buffett: What an investor needs is the ability to correctly evaluate selected businesses. Note that word “selected”: You don’t have to be an expert on every company, or even many. You only have to be able to evaluate companies within your circle of competence. The size of that circle is not very important; knowing its boundaries, however, is vital. The point is that you need to know what you know, and you can only expand those areas of knowledge very slowly.
  • Gaia Hypothesis: The theory, put forward by James Lovelock, that living matter on the earth collectively defines and regulates the material conditions necessary for the continuance of life. The planet, or rather the biosphere, is thus likened to a vast self-regulating organism. As a mental model, it is useful to think of all complex systems as being self-regulating and determined to ensure their own survival.
  • Bateman’s Principle: In biology, Bateman’s principle suggests that in most species, variability in reproductive success, or “reproductive variance,” is greater in males than in females. Females, especially mammalian females, almost always invest more energy into producing offspring than males invest. Bateman’s principle anticipated and is consistent with Robert Trivers’s theory of Parental investment — in most species females are a limiting factor over which males will compete. As a mental model, it’s useful to remember that there are usually these kinds of natural hubs and spokes in a system — many males, but few females.
  • Coexistence Theory (Ecology): Coexistence theory is a framework to understand how competitor traits can maintain species diversity and stave-off competitive exclusion even among similar species living in similar environments. Coexistence theory explains the stable coexistence of species as an interaction between two opposing forces: fitness differences between species, which should drive the best-adapted species to exclude others within a particular ecological niche, and stabilizing mechanisms, which maintains diversity via niche differentiation. For many species to be stabilized in a community, population growth must be negative density-dependent, i.e. all participating species have a tendency to increase in density as their populations decline. In such communities, any species that becomes rare will experience positive growth, pushing its population to recover and making local extinction unlikely. As the population of one species declines, individuals of that species tend to compete predominantly with individuals of other species. Thus, the tendency of a population to recover as it declines in density reflects reduced interspecific (between-species) competition relative to intraspecific (within-species) competition, the signature of niche differentiation (see Lotka-Volterra competition).
  • Kielber’s Law: Named after Max Kleiber’s biological work in the early 1930s, is the observation that, for the vast majority of animals, an animal’s metabolic rate scales to the ¾ power of the animal’s mass. Symbolically: if q0 is the animal’s metabolic rate, and M the animal’s mass, then Kleiber’s law states that q0 ~ M¾. Thus a cat, having a mass 100 times that of a mouse, will have a metabolism roughly 32 times greater than that of a mouse. In plants, the exponent is close to 1. The metabolic theory of ecology (MTE) is an extension of Kleiber’s law and posits that the metabolic rate of organisms is the fundamental biological rate that governs most observed patterns in ecology.[
  • Liebig’s Law of the Minimum: In farming, growth is controlled not by the total amount of resources available, but by the scarcest resource (limiting factor).
  • Plant-soil feedback: This is a process where plants alter the biotic and abiotic qualities of soil they grow in, which then alters the ability of plants to grow in that soil in the future. Negative plant-soil feedback occurs when plants are less able to grow in soil that was previously occupied by a member of the same species, and positive plant-soil feedback occurs when plants are more able to grow in soil that was previously occupied by a member of the same species. Although it was originally assumed that negative plant-soil feedback was caused by plants depleting the soil of nutrients, recent work has suggested that a major cause of plant-soil feedback is a buildup of soil-borne pathogens. Mutualism and allelopathy are also thought to cause plant-soil feedback. Studies have shown that, on average, plant-soil feedback tends to be negative; however, there have been many notable exceptions, such as many invasive species.
  • Symbiosis: This is a close and often long-term interaction between two different biological species; some symbiotic relationships are obligate, meaning that both symbionts entirely depend on each other for survival. For example, many lichens consist of fungal and photosynthetic symbionts that cannot live on their own. Others are facultative (optional): they can, but do not have to live with the other organism.
  • Sunk Costs: In economics and business decision-making, a sunk cost is a cost that has already been incurred and cannot be recovered. Sunk costs (also known as retrospective costs) are sometimes contrasted with prospective costs, which are future costs that may be incurred or changed if an action is taken.
  • Opportunity Costs: This is the loss of potential gain from other alternatives when one alternative is chosen.
  • Moral Hazard: This occurs when one person takes more risks because someone else bears the cost of those risks. A moral hazard may occur where the actions of one party may change to the detriment of another after a financial transaction has taken place. The tragedy of the commons is an example of moral hazard in the extreme and on a community-wide basis.
  • Compound Interest: This is interest on interest. It is the result of reinvesting interest, rather than paying it out, so that interest in the next period is then earned on the principal sum plus previously-accumulated interest.
  • Marginal Cost: The cost added by producing one extra item of a product.
  • Feedback loops: Every action creates an equal and opposite reaction. When reactions loop back to affect themselves, a feedback loop is created. All real-world systems are composed of many such interacting feedback loops — animals, machines, businesses, and ecosystems, to name a few. There are two types of feedback loops: positive and negative. Positive feedback amplifies system output, resulting in growth or decline. Negative feedback dampers output, stabilizes the system around an equilibrium point. Feedback loops are typically used to accomplish regulation and control. A feedback loop is like an input, but its origin is from within the system itself, not from outside the system. In many systems, the output reenters the system as another input. This is exactly what happens with a microphone and speakers when the sound from the speakers feed back into the microphone, often causing a loud squeal.
  • Recursion: Recursion occurs when a thing is defined in terms of itself or of its type. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no loop or infinite chain of references can occur.
  • Redundancy: We learn from Engineering that critical systems often require back up systems to guarantee a certain level of performance and minimize downtime. These systems are resilient to adverse conditions and if one fails there is spare capacity or a backup system. However, adding too much redundancy can introduce complexity, make people feel like someone else is handling the problem, or lead people to think that risky behavior is ok because there are systems set up to catch the problem.
  • Margin of Safety: “You don’t try to buy something for $80 million that you think is worth $83,400,000.” You can think of it as a reservoir to absorb errors or poor luck. Size matters. At least in this case, bigger is better. And if you need a calculator to figure out how much room you have, you’re doing something wrong.
  • Tight coupling: This is when a group of classes are highly dependent on one another. This scenario arises when a class assumes too many responsibilities, or when one concern is spread over many classes rather than having its own class.
  • Breakpoint: This is a means of acquiring knowledge about a program during its execution. During a pause (the breakpoint), the programmer inspects the test environment (general purpose registers, memory, logs, files, etc.) to find out whether the program is functioning as expected. In practice, a breakpoint consists of one or more conditions that determine when a program’s execution should be interrupted.
  • Bayes Theorem: This theorem is about concerned how we should adjust probabilities when we encounter new data. We modify our opinions with objective information: Initial Beliefs + Recent Objective Data = A New and Improved Belief. … each time the system is recalculated, the posterior becomes the prior of the new iteration. It was an evolving system, with each bit of new information pushed closer and closer to certitude.
  • Power Law: In statistics, a power law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another.
  • Law of Large Numbers: The law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed. The LLN is important because it “guarantees” stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins.
  • Permutations: A permutation, also called an "arrangement number" or "order," is a rearrangement of the elements of an ordered list into a one-to-one correspondence with itself. The number of permutations on a set of elements is given by (n!) factorial; Uspensky 1937, p. 18). For example, there are 2! = 2 * 1 = 2 permutations of {1, 2}; namely {1, 2} and {2, 1}. A set with three elements would have six permutations: 3! = 3 * 2 * 1 = 6.
  • Combinations: This is the number of ways of picking k unordered outcomes from n possibilities. It is like a permutation, except that order does not matter in combinations.
  • Variance: The variance is a numerical value used to indicate how widely individuals in a group vary. If individual observations vary greatly from the group mean, the variance is big; and vice versa.
  • Standard Deviation: The standard deviation (SD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values. A normal distribution is also known as the bell curve.
  • Regression to the Mean: In statistics, regression toward (or to) the mean is 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 — and if it is extreme on its second measurement, it will tend to have been closer to the average on its first.
  • Inversion: It is in the nature of things that many hard problems are best solved when they are addressed backward; “many problems can’t be solved forward.” Rather than think about what makes a good life, you can think about what prescriptions would ensure misery. Avoiding stupidity is easier than seeking brilliance.
  • Multiplicative Systems: Something all engineers learn very early on is that a system is no stronger than its weakest component. And the weakest element, in many cases, is people (often acting irrationally).
  • Outlier: An outlier is an observation in statistics that lies outside the overall pattern of distribution. When evaluating an opportunity, it’s important to recognize whether any of the data points you have are outliers.
  • Correlation versus Causation: Just because two variables can be correlated to one another doesn’t mean that one causes another. That study of causation must be done separately and isn’t a statistical analysis; it must be studied via experimentation.
  • Mean, median and mode: The mean (average) is the sum of all numbers in a set divided by the number of numbers in that set; it is susceptible to being influenced unduly by outliers. The median is the middle value in a set (or the average of the two closest to the middle in a set with an even number of entries), which gives a good idea of a ‘typical’ value in a set. The mode is the value that appears most often in a set of data (the value most likely to be sampled).
  • Zeroth law of thermodynamics: If two systems are in thermal equilibrium with a third system, they are in thermal equilibrium with each other. This law helps define the notion of temperature.
  • First law of thermodynamics: When energy passes, as work, as heat, or with matter, into or out from a system, the system’s internal energy changes in accord with the law of conservation of energy. Equivalently, perpetual motion machines of the first kind are impossible.
  • Second law of thermodynamics: In a natural thermodynamic process, the sum of the entropies of the interacting thermodynamic systems increases. Equivalently, perpetual motion machines of the second kind are impossible.
  • Third law of thermodynamics: The entropy of a system approaches a constant value as the temperature approaches absolute zero.[2] With the exception of non-crystalline solids (glasses) the entropy of a system at absolute zero is typically close to zero, and is equal to the logarithm of the product of the quantum ground states.
  • Entropy: This is a thermodynamic quantity representing the unavailability of a system’s thermal energy for conversion into mechanical work, often interpreted as the degree of disorder or randomness in the system. Said differently, nature tends from order to disorder in isolated systems.
  • Conservation of Energy: The law of conservation of energy states that the total energy of an isolated system remains constant — it is said to be conserved over time. Energy can neither be created nor destroyed; rather, it transforms from one form to another. For instance, chemical energy can be converted to kinetic energy in the explosion of a stick of dynamite.
  • Kinetic & Potential Energy: The cars of a roller coaster reach their maximum kinetic energy when at the bottom of their path. When they start rising, the kinetic energy begins to be converted to gravitational potential energy. The sum of kinetic and potential energy in the system remains constant, ignoring losses to friction. Friction would be a conversion to thermal energy (a type of kinetic energy).
  • Autocatalysis: A single chemical reaction is said to have undergone autocatalysis, or be autocatalytic, if one of the reaction products is also a reactant and therefore a catalyst in the same or a coupled reaction. The reaction is called an autocatalytic reaction. The rate equations for autocatalytic reactions are fundamentally nonlinear. This nonlinearity can lead to the spontaneous generation of order. A dramatic example of this order is that which is found in living systems. The spontaneous order creation corresponds to a decrease in the entropy of the system, which must be compensated by a larger increase in the entropy of the surroundings in order to satisfy the Second Law of Thermodynamics. A set of chemical reactions can be said to be “collectively autocatalytic” if a number of those reactions produce, as reaction products, catalysts for enough of the other reactions that the entire set of chemical reactions is self-sustaining given an input of energy and food molecules (see autocatalytic set).
  • Newton’s First Law: When viewed in an inertial reference frame, an object either remains at rest or continues to move at a constant velocity, unless acted upon by a net force.
  • Newton’s Second Law: In an inertial reference frame, the vector sum of the forces F on an object is equal to the mass m of that object multiplied by the acceleration vector a of the object: F = ma.
  • Newton’s Third Law: When one body exerts a force on a second body, the second body simultaneously exerts a force equal in magnitude and opposite in direction on the first body.
  • Conservation of Angular Momentum: The law of conservation of angular momentum states that when no external torque acts on an object, no change of angular momentum will occur. Angular momentum is a vector quantity describing an object in circular motion; its magnitude is equal to the momentum of the particle, and the direction is perpendicular to the plane of its circular motion. Conservation laws also exist for linear momentum, electric charge, and mass-energy.
  • General Relativity: For an analogy to general relativity, consider that you stretched out a bedsheet or piece of elastic flat, attaching the corners firmly to some secured posts. Now you begin placing things of various weights on the sheet. Where you place something very light, the sheet will curve downward under the weight of it a little bit. If you put something heavy, however, the curvature would be even greater. Assume there’s a heavy object sitting on the sheet and you place a second, lighter, object on the sheet. The curvature created by the heavier object will cause the lighter object to “slip” along the curve toward it, trying to reach a point of equilibrium where it no longer moves. (In this case, of course, there are other considerations — a ball will roll further than a cube would slide, due to frictional effects and such.) This is similar to how general relativity explains gravity. The curvature of a light object doesn’t affect the heavy object much, but the curvature created by the heavy object is what keeps us from floating off into space. The curvature created by the Earth keeps the moon in orbit, but at the same time the curvature created by the moon is enough to affect the tides.
  • Wave–particle duality: This is the concept that every elementary particle or quantic entity may be partly described in terms not only of particles, but also of waves. The term wave is often intuitively understood as referring to a transport of spatial disturbances that are generally not accompanied by a motion of the medium occupying this space as a whole. In physics, a wave is a traveling disturbance that travels through space and matter transferring energy from one place to another. When studying waves it’s important to remember that they transfer energy, not matter.
  • Heisenberg’s Uncertainty Principle: Heisenberg reasoned that since matter acts as waves, some properties, such as an electron’s position and speed, are “complementary,” meaning there’s a limit (related to Planck’s constant) to how well the precision of each property can be known. Under what would come to be called “Heisenberg’s uncertainty principle,” it was reasoned that the more precisely an electron’s position is known, the less precisely its speed can be known, and vice versa. This uncertainty principle applies to everyday-size objects as well, but is not noticeable because the lack of precision is extraordinarily tiny.
  • Critical Mass: The minimum amount of fissile material needed to maintain a nuclear chain reaction. This varies based on the type and amount of fuel, the shape, the temperature, the density of the mass, the use of a neutron reflector, and the use of a tamper, which holds in the expanding fissioning material as it expands and thus lowers the critical mass needed for an explosion.
  • Equilibrium: A state in which opposing forces or actions are balanced so that one is not stronger or greater than the other. Moving systems tend towards static (unmoving) equilibrium over time.
  • Natural Selection: The process whereby organisms better adapted to their environment tend to survive and produce more offspring. This is often misconstrued as survival of the fittest, but it really says that adaptations that encourage a greater amount of survival (and, thus, reproduction) eventually become more common in a population. These adaptations can start out as random mutations in the gene, interbreeding due to migration, or genetic drift (variation in the relative frequency of different genotypes in a small population, owing to the chance disappearance of particular genes as individuals die or do not reproduce).
  • Asymmetric information: Sometimes referred to as information failure, is present whenever one party to an economic transaction possesses greater material knowledge than the other party. This normally manifests itself when the seller of a good or service has greater knowledge than the buyer, although the opposite is possible. Almost all economic transactions involve information asymmetries.
  • Occam’s Razor: Occam’s razor recommends that, when faced with two equally good hypotheses, choose the simpler. Said another way, Occam’s razor encourages the reduction of unnecessary elements in a design or system to achieve maximum simplicity without compromising functionality. A quote often attributed to Einstein said it a bit more precisely: Everything should be made as simple as possible, but not simpler.
  • Deduction and Induction: Inductive reasoning involves drawing a conclusion by moving from specific observations to general ones. Deductive reasoning, on the other hand, involves drawing conclusions by applying a generalization to a specific example. It’s important to be sure that you know what kind of reasoning you’re applying.
  • Decision Making Process: Rigorous decision analysis combines a systematic assessment of the probabilities of future events with a hard-headed evaluation of the costs and benefits of particular outcomes. As such, it can be an invaluable tool in helping organizations overcome the biases that hinder them in estimating the likelihood of unpleasant events. Decision analysis begins with a clear definition of the decision to be made, followed by an explicit statement of objectives and explicit criteria for assessing the “goodness” of alternative courses of action, by which we mean the net cost or benefit as perceived by the decision-maker. The next steps involve identifying potential courses of action and their consequences. Because these elements often are laid out visually in a decision tree, this technique is known as “decision tree analysis.” Finally, the technique instructs decision-makers to explicitly assess and make trade-offs based on the potential costs and benefits of different courses of action. To conduct a proper decision analysis, leaders must carefully quantify costs and benefits, their tolerance for accepting risk, and the extent of uncertainty associated with different potential outcomes. These assumptions are inherently subjective, but the process of quantification is nonetheless extremely valuable’ it forces participants to express their assumptions and beliefs, thereby making them transparent and subject to challenge and improvement.
  • Scientific Method: From Richard Feynman: Now I’m going to discuss how we would look for a new law. In general, we look for a new law by the following process. First, we guess it (audience laughter), no, don’t laugh, that’s the truth. Then we compute the consequences of the guess, to see what, if this is right, if this law we guess is right, to see what it would imply and then we compare the computation results to nature or we say compare to experiment or experience, compare it directly with observations to see if it works. If it disagrees with experiment, it’s wrong. In that simple statement is the key to science. It doesn’t make any difference how beautiful your guess is, it doesn’t matter how smart you are who made the guess, or what his name is … If it disagrees with experiment, it’s wrong. That’s all there is to it.
  • Gresham’s Law: Whenever coins containing precious metals have been used along with base metal coins of the same denomination, both legally accepted as tender, the bad coins have driven the good coins out of circulation. The better coins were either hoarded or melted down and sold as bullion, were used in the fine arts, or were absorbed in the foreign exchanges. In other words, what Gresham discovered was that cheaper money tends to drive out dearer; that when people begin to discriminate between two coinages, they will invariably pay out the inferior and hoard the better, thus removing the better from circulation. There are certain industries and human activities that lack the “policing” necessary to keep systems of behavior on the straight and narrow, and thus bad behavior has gained a hard-to-replace foothold. While it’s admirable to be the “cleanest shirt” in a pile of dirty laundry, certain areas of human life do not allow the clean shirts to win.
  • The Red Queen Effect: “For an evolutionary system,” Heylighen concludes, “continuing development is needed just in order to maintain its fitness relative to the systems it is co-evolving with.” Basically, in a competitive world progress (“running”) is needed just to maintain relative placement (“staying put”). Or, as Warren Buffett put it: “Over the years, we had the option of making large capital expenditures in the textile operation that would have allowed us to somewhat reduce variable costs. Each proposal to do so looked like an immediate winner. Measured by standard return-on-investment tests, in fact, these proposals usually promised greater economic benefits than would have resulted from comparable expenditures in our highly-profitable candy and newspaper businesses. But the promised benefits from these textile investments were illusory. Many of our competitors, both domestic and foreign, were stepping up to the same kind of expenditures and, once enough companies did so, their reduced costs became the baseline for reduced prices industrywide. Viewed individually, each company’s capital investment decision appeared cost-effective and rational; viewed collectively, the decisions neutralized each other and were irrational (just as happens when each person watching a parade decides he can see a little better if he stands on tiptoes). After each round of investment, all the players had more money in the game and returns remained anemic.”
  • Process versus Outcome: It seems like the best way to reach a desired result would be to focus on that result, try to move toward it, and judge each attempt by how closely you approximate it. But actually that approach is far from optimal. If you focus your attention and effort less on the results you’re hoping for and more on the processes and techniques you use, you will learn faster, become more successful, and be happier with the outcome.
  • The Agency Problem: A conflict of interest inherent in any relationship where one party is expected to act in another’s best interests. In corporate finance, the agency problem usually refers to a conflict of interest between a company’s management and the company’s stockholders. This can be solved by making management significant long-term shareholders.
  • The 7 Deadly Sins: Hubris, greed, lust, malicious envy, gluttony, inordinate anger, and sloth. Or, you know, don’t lie, don’t covet what other people had, and don’t obsess about everything already.
  • Dukkha: Life is characterized by impermanence and suffering, and the origin of this is attachment to desire. According to Buddhism, the way to stop this is by eliminating desire and attachment through the Noble Eight-fold path.
  • Network Effect: In economics and business, a network effect (also called network externality or demand-side economies of scale) is the effect that one user of a good or service has on the value of that product to other people. When a network effect is present, the value of a product or service is dependent on the number of others using it. The classic example is the telephone. The more people who own telephones, the more valuable the telephone is to each owner. This creates a positive externality because a user may purchase a telephone without intending to create value for other users, but does so in any case. Online social networkswork in the same way, with sites like Twitter and Facebook becoming more attractive as more users join.
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