Externalities

Definition:

An externality is a benefit or cost which “spills over” to a third party.

Externalities can be positive or negative.

Example: 

Positive Externality — taking care of my front lawn.

This is a positive externality because the benefits of a tidy front lawn accrue to my entire neighbourhood by making it more attractive and pleasant to my neighbours. That is, my neighbours don’t have to incur the cost of taking care of my lawn, but the incur spillover benefits from my activity.

Negative Externality — pollution.

Pollution is the most common example of a negative externality. If I throw my trash in the streets around my neighbourhood the negative consequences affect my neighbours as much as they do me.

Why is it important? 

Externalities take place any time the market fails to fully price the full effects of an activity.

In such a case where externalities are not fully priced in, too much of a good will be produced because someone else will pay for these costs. If there is a positive externality, too little of the good is produced because the producer doesn’t receive all of the benefits associated with the action.

In a more general sense, externalities distort incentives causing us to engage in too much or too little of an activity.

Thinking about externalities resulting from certain actions will help us to consider alternative actions that otherwise we might not if taking into account only the direct costs and benefits.

Marginality (Marginal Benefit, Marginal Cost, Diminishing Marginal Utility )

Definition:

Marginal concepts in economics look at changes in output which result from a specified change in the input.

Marginal Benefit — the additional utility (satisfaction) deriving from consuming one additional unit of a particular good or service

Marginal Cost — the additional cost incurred by production of one additional unit

Diminishing Marginal Utility — every additional unit of consumption will provide less utility than the previous

Examples:

Marginal benefit — My total enjoyment increases when I get one additional scoop of ice cream

Marginal cost — Every additional scoop of ice cream increases the total cost of my ice cream cone

Diminishing Marginal Utility —  I derive more enjoyment from the first scoop of ice cream than I do from the 100th.

Why is it important:

The importance of marginal thinking cannot be overstated. It’s a fantastic mental model / lens through which to look at problems and situations.

Marginal thinking is already interesting taken in the economic sense. For most processes in life it is worth thinking about  what a small change in input would do to the output. That is, will that extra effort really produce enough effect to justify it?

Thinking at the margin involves thinking about changes and what small variations could do to the overall result. Thinking at the margin is not something that comes natural to most people as we tend to focus on the overall picture rather than thinking about changes and their effects.

On a metaphorical level, thinking at the margin means thinking near the boundary of a problem.  In that sense, thinking at the margin requires us to think about what that boundary is, which is useful in itself. Then, it requires that we think about changes.

The concept of diminishing marginal utility is also interesting to analyze in a bit more depth. There are many things in life and in different domains that ascribe to this ‘law’ of economics. For example, the enjoyment of most activities only add additional enjoyment up to a point. Beyond a certain threshold, more and more gains will actually lead to less enjoyment and could even lead to overall negative enjoyment.

Application:

It is possible to apply marginal thinking to any problem involving an input and an output. While it’s most obvious applications are to decision making in the economic or utilitarian sense, it’s also interesting to reflect on marginal thinking as a lens to aid in general decision making.

An insightful use of marginal thinking is asking yourself these two questions:

  1. If you found yourself with an extra hour of free time, what would you do more of?
  2. If you found yourself with an hour less of free time, what would you cut back?

This is thinking marginally at its prime. If you think about this long and hard it turns out that if your answers to both these questions differ at all, you are not doing the best you could be doing in managing your life prioritizing lower value activities over higher value ones. That is, you should simply replace things from your second answer with things from the first answer. This same logic applies to money, energy, and most of your resources in life.

Comparing marginal benefit with marginal cost is an interesting decision-making tool. For example, I’m not going to spend 10 more minutes working on this text as I don’t think the extra time spent on it will add much value, but 10 minutes of sleep will do me wonders tomorrow. 😉

Falsifiability

Definition:

Falsifiability — A statement/claim/hypothesis is falsifiable if it is possible to disprove it. If a theory cannot be falsified, then there is no point even looking examining evidence.

The natural tendency is to put forward a hypothesis and look for evidence to confirm it or induce conclusions from observational data.

Falsifiability is looking at it the other way around, looking for examples that contradict the theory. If the theory resists multiple attempts at contradiction, it’s a good working hypothesis of truth.

Example:

Ask the question “What would be an example of something that, if observed, would contradict the hypothesis?”

Hypothesis — All swans are white

Traditional Approach — Look for white swans to confirm that swans are white

Falsifiability Approach — Look for non-white swans to disprove the hypothesis. (i.e., finding one black nullifies the hypothesis that all swans are white)

Why is it important: 

Using the falsifiability approach provides a more comprehensive understanding of how to structure and run experiments. Although falsifiability is not universally accepted and it has its critics, the concept is still a foundation of most modern scientific experiments.

Karl Popper is credited with disseminating falsificationism as a philosophy of science in the mid 20th century. Popper’s view on science was guided by his use of formal logic. Since there was no way to arrive at the undeniability of a conclusion through induction (going from particulars to a more general principles), Popper theorized that the answer lie in using deductive reasoning (arriving at particular through general principles) and falsifiability. That is, we can always use negative evidence to contradict a statement, but positive evidence does not lead to the conclusion that the general case is always true.

“A million successful experiments cannot prove a theory correct, but one failed experiment can prove a theory wrong.” – Karl Popper

Applications:

Falsifiability is applicable in any area seeking knowledge or truth through empirical evidence.

Science

Most scientific tests today are based on the falsifiability principle.

Personal Life: 

Instead of looking for signs that someone loves you, look for signs that they don’t.

Sources & Suggested Reading:

Power Law

Definition:

A power law is simply a function where one variable varies as a power of another.

Example:

Examples of power laws include:

  • Frequency of word usage — a few words dominate most of our vocabulary
  • Size of cities — the largest cities account for an unequal proportion of urban population
  • Distribution of internet traffic — a few websites account for the majority of internet traffic
  • Pareto or 80/20 distribution — 20% of the Italian population owned 80% of the land in Italy. Or in the business world, 80% of your sales come from 20% of your customers

Why is it important:

Mathematically, a power law can be represented simply as a function such as Y = X^Z.

If x is a decimal and its exponent is a positive integer we get a decreasing curve that looks like:

Downward Sloping Power Law

If x is an integer and it’s exponent is a positive integer we get an increasing curve that looks like:

Increasing Sloping Power Law

The power law is in direct contrast to the normal, or gaussian, distribution which looks like:

Gaussian

The Gaussian (also known as normal) distribution assumes the entire population is distributed across with a huge majority of values close to the average.

Gaussian distributions are observable in situations where there are finite limits. For example, if you were to plot the height of every adult in the world, it tends to resemble a normal distribution.

Power Law distributions are observables in situations suitable to wild randomness. Wild randomness occurs in environments where a single observation can severely impact the total. A common example is a swing in the stock market such as Black Monday, when the Dow Jones lost almost 22% in a single day, or the wealth distribution where 1% of people account for 50% of the world’s wealth.

While the Gaussian world treats extreme events as highly unlikely outliers the Power Law world recognizes that a few events have a disproportionate impact and account for most of the end result. In a Gaussian world, extreme events can be safely ignored as the tails of distributions are inexorably small.

In the Power Law world, extreme events completely change the game as they can occur in an unprecedented scale. Most of our models have to be thrown out of the window as the analytical tools we have developed are designed to understand a Gaussian world. Forecasts become less meaningful and are hardly predictive (in fact, they are harmful!).

A lot of our institutions (businesses, governments, organizations) operate in a power law world, but pretend they are in a Gaussian world because it is more simple to model and understand. This is clearly a mistake.

Applications:

The effects of shifting mindset if you are operating in a power-law world are enormous.

For one, we can throw all of our Gaussian (normal distribution) models in the trash including those we use when measuring risk, returns, and probabilities. Though it is only human to prefer the predictable over the unpredictable, process over instability, certainty over ambiguity, the Power Law world requires that we think differently and embrace the outcome of unlikely, yet extremely powerful, events. In a power-law world, a small change alters the slope of impact resulting in large changes in outcomes similar to a Butterfly Effect.

In a power-law world, a small change alters the slope of impact resulting in large changes in outcomes similar to a Butterfly Effect.

In venture capital, for example, a disproportionate amount of returns are concentrated in very few winners. That being the case, it is preferable to invest in startups that could either lose/win big than in startups that you know will only return a meager amount. This effect is not only true in venture capital, but in all industries susceptible to ‘superstar effects’ such as sports, music, actors, movies, politics, and many, many others. In fact, I would venture to say that the Power Law is much more prominent than we think or give it credit for.

In our personal lives we are constantly faced with Power Laws. A few common examples include our investments in time, money, effort, and relationships. Think about the small number of purchases that really impact your life — your favorite article of clothing, an awesome mattress, your smartphone, etc…; a few experiences have probably impacted your life and made more a difference than all the others combined; you probably derive more value from a few relationships (your family, closest friends) than from all others combined.

In most cases, it is incredibly hard to predict with any level of accuracy which activities will generate outsized returns so it is extremely important to focus on those that you can.

Suggested Reading:

Occam’s Razor or Principle of Parsimony

Definition:

Occam’s razor is a problem solving principle that states that among competing hypothesis the simpler explanation is more likely to be true. That is, a hypothesis with fewer assumptions is preferable to others if they have the same predictive capability.

The term ‘razor’ refers to shaving away unnecessary assumptions or cutting apart two similar conclusions.

Example:

If you lost your wallet do you assume that (1) you misplaced it or (2) goblins stole it?

Occam’s razor implies that it is much more likely that you misplaced your wallet. In fact that another entity other than you was involved in the action (regardless of whether it was goblins or your sister) is enough to invoke Occam’s Razor. So next time you lose something, jump to the most likely culprit — you. The simplest explanation wins.

Occam’s razor is also commonly illustrated in the adage, “when you hear hoofbeats behind you, think horses, not zebras.”

Why is it important?

Occam’s Razor underlies most scientific undertakings and theory building.

The principle is particularly important because of the undetermination problem which states that for a given set of data there is always an infinite number of possible models to explain it. In a classic example from mathematics, you can draw an infinite number of lines through two points on a plain. Occam’s Razor could be invoked to state that it is more likely that this line is straight than that it has 1000 inflection points. It is important to understand underdetermination because it is at the root of many common mental fallacies — that a theory fits all the data points doesn’t mean it is correct. There are several situations where more than one theory fits the data (as in our Goblin’s example above) and Occam’s razor helps us to isolate the theories which are more likely to be true, if not only because of statistical probability (a statement with one proposition has a higher likelihood than a statement with many propositions). This isn’t to say that complex theories never win the day, just that is more likely that the simplest explanation is correct.

If thought through in this sense, Occam’s Razor is particularly important when seeking to apply our lattice work of mental models. If there are two competing mental models that lead to the same result, we should apply the simpler one, minimizes cognitive load and increasing the probability that our outcome is correct.

Some people equate Occam’s razor to the KISS (keep it simple, stupid) principle, but that is, for lack of better words, stupid. Simplicity isn’t preferable over complexity just to avoid the problems with complex models. Occam’s razor simply states that if two models exist that have the same predictive capability, the simplest model is preferred. That is, you should always prefer the simplest model only if they have the same explanatory power.

Occam’s razor is most relevant to universal models such as those in systems theory, mathematics, or philosophy. If the foundations of universal models are unnecessarily complex the chances that we can arrive at manageable models and explanations are slim.

How can you apply it? 

Occam’s razor is often considered one of the fundamental tenants in modern science and can be applied in a myriad of fields including, but not limited to, physics, biology, medicine, statistics, religion, ethics, probability theory, and even to personal situations.

In medicine, for example, Occam’s razor is also known as diagnostic parsimony. Diagnostic parsimony advocates that doctors should look for the least possible causes that account for all symptoms. That being said, it is often more likely that a patient has several common diseases rather than one rare disease responsible for all symptoms. Caution is advised, especially when dealing with outcomes where the loss function is prohibitively high, such as medicine.

There are several papers investigating Occam’s razor in probability theory. In fact, Occam’s razor is intuitively justified through probability as by definition each new assumption introduces additional probably for error.

Occam’s razor is also found in programming and software development. The best programmers are those that utilize less code, and thus computational power, to arrive at similar results. Software solutions increase in complexity as the number of requirements and features increase, however, that does not mean that the number of lines of code has to increase in lockstep. The best developers are those that can reduce complexity as they increase functionality.

The Lean Startup methodology has garnered quite a bit of traction in the startup space and much of it is derived from application of Occam’s razor. A Minimum Viable Product (MVP) is the simplest version of a product that enables a team to collect the maximum amount of validated learning with the least effort. In this sense the Minimum Viable Product is arrived at by applying Occam’s Razor to a startup problem hypothesis. By trimming assumptions startup founders arrive at better MVPs.

Simplicity is the ultimate sophistication

Leonardo da Vinci

The business schools reward difficult complex behaviour more than simple behaviour, but simple behaviour is more effective.

Warren Buffet

Everything should be made as simple as possible, but not simpler.

Albert Einstein

Anchoring

Definition:

Anchoring is a cognitive bias that describes how humans tend to focus on the first piece of information presented and use it as an ‘anchor’ with which to make estimations and decisions. Once an anchor is set we adjust our judgements up or down from it.

Example:

Suppose you want to buy a lollipop from your friend Jimmy. Jimmy offers to sell you his lollipop for $10 and you think that is outrageous. Still, you are sugar-crazed and you manage to negotiate Jimmy down to $6. You leave thinking you got a great deal, nevermind that the lollipop was only worth $2 to begin with.

By setting a high anchor of $10, Jimmy influenced your perceived value and controlled the negotiation as you negotiated down using the initial price of $10. That is anchoring.

Why is it important? 

Anchoring is one of the strongest biases (a systematic deviation in judgement) uncovered by Amos Tversky and Daniel Kahneman, one of the only psychologists to have won the Nobel prize in economics.

In 1975, Tversky and Kahneman ran a famous experiment in which they asked test subjects the percentage of African countries in the United Nations. Except that before answering the question participants were told to spin a wheel of fortune (with numbers from 0 – 100) rigged to always land on either 10 or 65.  Only then were participants asked whether the percentage of African countries in the UN were lower or higher than the wheel of fortune number followed by an exact guess. What they found was mind blowing. Though the number from the wheel of fortune obviously had no bearing or was connected in any way to the percentage of African countries in the UN, that information seriously biassed participants. Those that landed on 10 guessed around 25% of African countries in the UN, those that landed on 65 averaged 45%. The anchoring effect is so strong that even unrelated pieces of information have an effect in our decision making. If we are going off of no information, we will grasp anything that we can even if we know it should have no bearing on the final outcome.

Anchoring is so strong that:

  • Experts and non-experts are affected similarly by the bias
  • If test subjects were paid for accuracy the bias remained strong
  • Telling test subjects explicitly that they should not take the wheel of fortune number into account when estimating the percentage of African countries in the UN only had a small effect on the result

In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of partial computation. In either case, adjustments are typically insufficient…that is, different starting points yield different estimates, which are biased toward the initial values.

Kahneman, Slovic and Tversky

How can you apply it?  

Anchoring is common in many buyer/seller transactions. Consider how you negotiate off the given price when buying most things rather than establishing your own value for the asset. Retail stores are all too familiar with anchoring. If a jacket was originally priced at $50 and now it is being sold at $45, it must be a bargain!

First impressions and their impact are also a form of anchoring. It is challenging to alter what you think of a person after a bad first impression. Making a good first impression works in a similar way, which is why it’s so important to nail the initial part of the interaction.

Researchers have also shown that when trying to decide how someone else feels, we start by anchoring onto how we would feel in the situation and adjust it for how we think the other person might feel.

Anchoring has such a strong effect, a lot of it subconscious, that avoiding it is pretty darn difficult. In negotiation, it is important to come with a predefined value for what you are negotiating about before being biased by any other estimate. In fact, as a negotiator you have a much stronger position if you provide the first value and anchor the conversation around that, something which might seem a bit counter-intuitive. This applies for other aspects of life as well — always set the anchor in your best interest!

Compounding

Definition:

Investopedia defines compounding as “the ability of an asset to generate earnings, which are then reinvested in order to generate their own earnings. In other words, compounding refers to generating earnings from previous earnings.”

Example:

In practical terms, investing $100 at 6% interest in beginning of year 1 will yield $6 at the end of the year for a total of $106. In the next year that $106 at 6% interest will generate $6.36 of interest for a total of $112.36. Notice how without adding any additional money the interest generated went from $6 to $6.36. This is compounding at work and its power is exponential.

Why is it important? 

Understanding the power of compounding is as important to your personal life as it is to businesses around the world.  Despite being a relatively simple concept, the true power of compounding is often underestimated.

Business Insider has compiled charts which show just how powerful compounding can be, but these shouldn’t surprise you if you understand the power of the concept.

Borrowing one chart from JP Morgan to illustrate, the best strategy hands down is to start early and save consistently. Starting early in particular has a drastic impact in the end result. Comparing Susan and Bill below you can see that Susan is in a better place saving $5,000 between the ages of 25 and 35 investing only $50,000 than Bill if he invests $5,000 between the ages of 35 and 65, triple the amount that Susan invested.

How can you apply it? 

Warren Buffet has written a number of essays on the power of compounding in personal finance and probably my favorite example is from his 1965 letter to shareholders where he writes:

“The saga of trading acumen etched into history by the Manhattan Indians when they unloaded their island to that notorious spendthrift, Peter Minuit in 1626. My understanding is that they received $24 net. For this, Minuit received 22.3 square miles which works out to about 621,688,320 square feet. While on the basis of comparable sales, it is difficult to arrive at a precise appraisal, a $20 per square foot estimate seems reasonable giving a current land value for the island of $12,433,766,400 ($12 1/2 billion). To the novice, perhaps this sounds like a decent deal. However, the Indians have only had to achieve a 6 1/2% return to obtain the last laugh on Minuit. At 6 1/2%, $24 becomes $42,105,772,800 ($42 billion) in 338 years, and if they just managed to squeeze out an extra half point to get to 7%, the present value becomes $205 billion.”

Compounding can be applied in several dimensions other than finance.

Stephen Cohen, co-founder of Palantir, applies it to the realm of intelligence:

We tend to massively underestimate the compounding returns of intelligence. As humans, we need to solve big problems. If you graduate Stanford at 22 and Google recruits you, you’ll work a 9-to-5. It’s probably more like an 11-to-3 in terms of hard work. They’ll pay well. It’s relaxing. But what they are actually doing is paying you to accept a much lower intellectual growth rate. When you recognize that intelligence is compounding, the cost of that missing long-term compounding is enormous. They’re not giving you the best opportunity of your life. Then a scary thing can happen: You might realize one day that you’ve lost your competitive edge. You won’t be the best anymore. You won’t be able to fall in love with new stuff. Things are cushy where you are. You get complacent and stall. So, run your prospective engineering hires through that narrative. Then show them the alternative: working at your startup.

Frank Lio applies it to simple disciplines or errors over time:

Principle #1 – Simple Disciplines Repeated Over Time:
It is the simple disciplines (choices) in life that don’t seem to make any differences at all in the moment; however, repeated over time, the compounded effect makes all the difference in the world.  Personal choices may be to take time daily to talk to a loved one, to show compassion to a colleague or report, to exercise, to meditate, to save money. In business, we employ continuous follow-up after every customer installation, product changes based on customer feedback and implementation, quality corrective actions.

Principle #2 – Simple Errors Repeated Over Time:
It is the simple errors in judgment that don’t seem to make any difference in the moment; however, repeated over time, the compounded effect makes all the difference in the world.  Personal examples may be smoking, eating that daily bacon breakfast sandwich, skipping lunch or exercise. These are innocuous, seemingly innocent little “miscues” or transgressions.  Businesses may decide to “go cheap” and skimp on customer service, allow more quality defects or cheaper parts in their product, a bank may continuously add fees, etc.

And Adam Miller applies it to startups and growth:

If there’s one theme that all of the greatest growth stories, be it Dropbox, Uber, or Airbnb, have in common it’s the rarely discussed topic of “compounding” growth. We all understand that users beget more users beget more users, but few stop to understand that the reasoning behind each customer’s decision to use a new product or service will differ.

And the list goes on and on…..