Tag: Decision Making

All Models Are Wrong

How is your journey towards understanding Farnam Street’s latticework of mental models going? Is it proving useful? Changing your view of the world? If the answer is that it’s going well that’s good. There’s just one tiny hitch.

All models are wrong.

Yep. It’s the truth. However, there is another part to that statement:

All models are wrong; some are useful.

Those words come from the British statistician, George Box. In a groundbreaking 1976 paper, Box revealed the fallacy of our desire to categorize and organize the world. We create models (a term with many applications), once to confuse them for reality.

Box also stated:

Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.

What Exactly Is A Model?

First, we should understand precisely what a model is.

The dictionary definition states a model is ‘a representation, generally in miniature, to show the construction or appearance of something’ or ‘a simplified description, especially a mathematical one, of a system or process, to assist calculations and predictions.’

For our purposes here, we are better served by the second definition. A model is a simplification which fosters understanding.

Think of an architectural model. These are typically a small scale model of a building, made before it’s built. Its purpose is to show what the building will look like and to help people working on the project to develop a clear picture of the overall feel. In the iconic scene from Zoolander, Derek (played by Ben Stiller) looks at the architectural model of his propsed ‘school for kids who can’t read good’ and shouts “What is this? A center for ants??”

That scene illustrates the wrong way to understand models: Too literally.

Why We Use Models- And Why They Work

At Farnam Street, we believe in using models for the purpose of building a massive, but finite amount of fundamental, invariant knowledge about how the world really works. Applying this knowledge is the key to making good decisions and avoiding stupidity.

“Scientists generally agree that no theory is 100 percent correct. Thus, the real test of knowledge is not truth, but utility. Science gives us power. The more useful that power, the better the science.”

— Yuval Noah Harari

Time-tested models allow us to understand how things work in the real world. And understanding how things work prepares us to make better decisions without expending too much mental energy in the process.

Instead of relying on fickle and specialized facts, we can learn versatile concepts. The mental models we cover are intended to be widely applicable.

It’s crucial for us to understand as many mental models as possible. As the adage goes, a little knowledge can be dangerous and creates more problems than total ignorance. No single model is universally applicable – we find exceptions for nearly everything. Even hardcore physics has not been totally solved.

“The basic trouble, you see, is that people think that “right” and “wrong” are absolute; that everything that isn’t perfectly and completely right is totally and equally wrong.”

— Isaac Asimov

Take a look at almost any comment section on the internet and you are guaranteed to find at least one pedant raging about a minor perceived inaccuracy, throwing out the good with the bad. While ignorance and misinformation are certainly not laudable, neither is an obsession with perfection.

Like heuristics, models work as a consequence of the fact they are usually helpful in most situations, not because they are always helpful in a small number of situations.

Models can assist us in making predictions and forecasting the future. Forecasts are never guaranteed, yet they provide us with a degree of preparedness and comprehension of the future. For example, a weather forecast which claims it will rain today may get that wrong. Still, it’s correct often enough to enable us to plan appropriately and bring an umbrella.

Mental Models and Minimum Viable Products

Think of mental models as minimum viable products.

Sure, all of them can be improved. But the only way that can happen is if we try them out, educate ourselves and collectively refine them.

We can apply one of our mental models, Occam’s razor, to this. Occam’s razor states that the simplest solution is usually correct. In the same way, our simplest mental models tend to be the most useful. This is because there is minimal room for errors and misapplication.

“The world doesn’t have the luxury of waiting for complete answers before it takes action.”

— Daniel Gilbert

Your kitchen knives are not as sharp as they could be. Does that matter as long as they still cut vegetables? Your bed is not as comfortable as it could be. Does that matter if you can still get a good night’s sleep in it? Your internet is not as fast as it could be. Does that matter as long as you can load this article? Arguably not. Our world runs on the functional, not the perfect. This is what a mental model is – a functional tool. A tool which maybe could be a bit sharper or easier to use, but still does the job.

The statistician David Hand made the following statement in 2014;

In general, when building statistical models, we must not forget that the aim is to understand something about the real world. Or predict, choose an action, make a decision, summarize evidence, and so on, but always about the real world, not an abstract mathematical world: our models are not the reality.

For example, in 1960, Georg Rasch said the following:

When you construct a model you leave out all the details which you, with the knowledge at your disposal, consider inessential…. Models should not be true, but it is important that they are applicable, and whether they are applicable for any given purpose must, of course, be investigated. This also means that a model is never accepted finally, only on trial.

Imagine a world where physics like precision is prized over usefulness.

We would lack medical care because a medicine or procedure can never be perfect. In a world like this, we would possess little scientific knowledge, because research can never be 100% accurate. We would have no art because a work can never be completed. We would have no technology because there are always little flaws which can be ironed out.

“A model is a simplification or approximation of reality and hence will not reflect all of reality … While a model can never be “truth,” a model might be ranked from very useful, to useful, to somewhat useful to, finally, essentially useless.”

— Ken Burnham and David Anderson

In short, we would have nothing. Everything around us is imperfect and uncertain. Some things are more imperfect than others, but issues are always there. Over time, incremental improvements happen through unending experimentation and research.

The Map is Not the Territory

As we know, the map is not the territory. A map can be seen as a symbol or index of a place, not an icon.

When we look at a map of Paris, we know it is a representation of the actual city. There are bound to be flaws; streets which have been renamed, demolished buildings, perhaps a new Metro line. Even so, the map will help us find our way. It is far more useful to have a map showing the way from Notre Dame to Gare du Nord (a tool) than to know how many meters they are apart (a piece of trivia.)

Someone who has spent a lot of time studying a map will be able to use it with greater ease, just like a mental model. Someone who lives in Paris will find the map easier to understand than a tourist, just as someone who uses a mental model in their day to day life will apply it better than a novice. As long as there are no major errors, we can consider the map useful, even if it is by no means a reflection of reality. Gregory Bateson writes in Steps to an Ecology of Mind that the purpose of a map is not to be true, but to have a structure which represents truth within the current context.

“A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.”

— Alfred Korzybski

Physical maps generally become more accurate as time passes. Not long ago, they often included countries which didn’t exist, omitted some which did, portrayed the world as flat or fudged distances. Nowadays, our maps have come a long way.

The same goes for mental models – they are always evolving, being revised – never really achieving perfection. Certainly, over time, the best models are revised only slightly, but we must never consider our knowledge “set”.

Another factor to consider in using models is to take into account what they’re used for.

Many mental models (e.g. entropy, critical mass and activation energy) are based upon scientific and mathematical concepts. A person who works in those areas will obviously need a deeper understanding of it than someone who want to learn to think better when making investment decisions. They will need a different map and a more detailed one showing elements which the rest of us have no need for.

“A model which took account of all the variation of reality would be of no more use than a map at the scale of one to one.”

— Joan Robinson

In Partial Enchantments of the Quixote, Jorge Luis Borges provides an even more interesting analysis of the confusion between models and reality:

Let us imagine that a portion of the soil of England has been leveled off perfectly and that on it a cartographer traces a map of England. The job is perfect; there is no detail of the soil of England, no matter how minute that is not registered on the map; everything has there its correspondence. This map, in such a case, should contain a map of the map, which should contain a map of the map of the map, and so on to infinity.Why does it disturb us that the map be included in the map and the thousand and one nights in the book of the Thousand and One Nights? Why does it disturb us that Don Quixote be a reader of the Quixote and Hamlet a spectator of Hamlet? I believe I have found the reason: these inversions suggest that if the characters of a fictional work can be readers or spectators, we, its readers or spectators, can be fictions.

How Do We Know If A Model Is Useful?

This is a tricky question to answer. When looking at any model, it is helpful to ask some of the following questions:

  • How long has this model been around? As a general rule, mental models which have been around for a long time (such as Occam’s razor) will have been subjected to a great deal of scrutiny. Time is an excellent curator, trimming away inefficient ideas. A mental model which is new may not be particularly refined or versatile. Many of our mental models originate from Ancient Greece and Rome, meaning they have to be functional to have survived this long.
  • Is it a representation of reality? In other words, does it reflect the real world? Or is it based on abstractions?
  • Does this model apply to multiple areas? The more elastic a model is, the more valuable it is to learn about. (Of course, be careful not to apply the model where it doesn’t belong. Mind Feynman: “You must not fool yourself, and you’re the easiest person to fool.”)
  • How did this model originate? Many mental models arise from scientific or mathematical concepts. The more fundamental the domain, the more likely the model is to be true and lasting.
  • Is it based on first principles? A first principle is a foundational concept which cannot be deduced from any other concept and must be known.
  • Does it require infinite regress? Infinite regress refers to something which is justified by principles, which themselves require justification by other principles. A model based on infinite regress is likely to required extensive knowledge of a particular topic, and have minimal real-world application.

When using any mental model, we must avoid becoming too rigid. There are exceptions to all of them, and situations in which they are not applicable.

Think of the latticework as a toolkit. That’s why it pays to do the work up front to put so many of them in your toolbox at a deep, deep level. If you only have one or two, you’re likely to attempt to use them in places that don’t make sense. If you’ve absorbed them only lightly, you will not be able to use them when the time is at hand.

If on the other hand, you have a toolbox full of them and they’re sunk in deep, you’re more likely to pull out the best ones for the job exactly when they are needed.

Too many people are caught up wasting time on physics-like precision in areas of practical life that do not have such precision available. A better approach is to ask “Is it useful?” and, if yes, “To what extent?”

Mental models are a way of thinking about the world that prepares us to make good decisions in the first place.

Get Smart: Three Ways of Thinking to Make Better Decisions and Achieve Results

“Give me six hours to chop down a tree and I will spend the first four sharpening the axe.”
— Abraham Lincoln

***

Your ability to think clearly determines the decisions you make and the actions you take.

In Get Smart!: How to Think and Act Like the Most Successful and Highest-Paid People in Every Field, author Brian Tracy presents ten different ways of thinking that enable better decisions. Better decisions free up your time and improve results. At Farnam Street, we believe that a multidisciplinary approach based on mental models allows you to gauge situations from different perspectives and profoundly affect the quality of decisions you make.

Most of us slip into a comfort zone of what Tracy calls “easy thinking and decision-making.” We use less than our cognitive capacity because we become lazy and jump to simple conclusions.

This isn’t about being faster. I disagree with the belief that decisions should be, first and foremost, fast and efficient. A better approach is to be effective. If it takes longer to come to a better decision, so be it. In the long run, this will pay for itself over and over with fewer messes, more free time, and less anxiety.

In Get Smart, Tracy does a good job of showing people a series of simple, practical, and powerful ways of examining a situation to improve the odds you’re making the best decision.

Let’s take a look at a few of them.

1. Long-Time Perspective Versus Short-Time Perspective

Dr. Edward Banfield of Harvard University studied upward economic mobility for almost 50 years. He wondered why some people and families moved from lower socioeconomic classes to higher ones and some didn’t. A lot of these people moved from labor jobs to riches in one lifetime. He wanted to know why. His findings are summarized in the controversial book, The Unheavenly City. Banfield offered one simple conclusion that has endured. He concluded that “time perspective” was overwhelmingly the most important factor.

Tracy picks us up here:

At the lowest socioeconomic level, lower-lower class, the time perspective was often only a few hours, or minutes, such as in the case of the hopeless alcoholic or drug addict, who thinks only about the next drink or dose.

At the highest level, those who were second- or third-generation wealthy, their time perspective was many years, decades, even generations into the future. It turns out that successful people are intensely future oriented. They think about the future most of the time.

[…]

The very act of thinking long term sharpens your perspective and dramatically improves the quality of your short-term decision making.

So what should we do about this? Tracy advises:

Resolve today to develop long-time perspective. Become intensely future oriented. Think about the future most of the time. Consider the consequences of your decisions and actions. What is likely to happen? And then what could happen? And then what? Practice self-discipline, self-mastery, and self-control. Be willing to pay the price today in order to enjoy the rewards of a better future tomorrow.

Sounds a lot like Garrett Hardin‘s three lessons from ecology. But really what we’re talking about here is second-level thinking.

2. Slow Thinking

“If it is not necessary to decide, it is necessary not to decide.” 
— Lord Acton

I don’t know many consistently successful people or organizations that are constantly reacting without thinking. And yet most of us are habitually in reactive mode. We react and respond to what’s happening around us with little deliberate thought.

“From the first ring of the alarm clock,” Tracy writes, we are “largely reacting and responding to stimuli from [our] environment.” This feeds our impulses and appetites. “The normal thinking process is almost instantaneous: stimulus, then immediate response, with no time in between.”

The superior thinking process is also triggered by stimulus, but between the stimulus and the response there is a moment or more where you think before you respond. Just like your mother told you, “Count to ten before you respond, especially when you are upset or angry.”

The very act of stopping to think before you say or do anything almost always improves the quality of your ultimate response. It is an indispensable requirement for success.

One of the best things we can do to improve the quality of our thinking is to understand when we gain an advantage from slow thinking and when we don’t.

Ask yourself “does this decision require fast or slow thinking?” 

Shopping for toothpaste is a situation where we derive little benefit from slow thinking. On the other hand if we’re making an acquisition or investment we want to be deliberate. Where do we draw the line? A good shortcut is to consider the consequences. Telling your boss he’s an idiot when he says something stupid is going to feel really good in the moment but carry lasting consequences. Don’t React.

Pause. Think. Act. 

This sounds easy but it’s not. One habit you can develop is to continually ask “How do we know this is true?” for the pieces of information you think are relevant to the decision.

3. Informed Thinking Versus Uninformed Thinking

“Beware of endeavouring to be a great man in a hurry.
One such attempt in ten thousand may succeed: these are fearful odds.”
—Benjamin Disraeli

 

I know a lot of entrepreneurs and most of them religiously say the same two words “due diligence.” In fact, a great friend of mine has a 20+ page due diligence checklist. This means taking the time to make the right decision. You may be wrong but it won’t be because you rushed. Of course, most of the people who preach due diligence have skin in the game. It’s easier to be cavalier (or stupid) when it’s heads I win and tails I don’t lose much (hello government).

Harold Geneen, who formed a conglomerate at ITT, said, “The most important elements in business are facts. Get the real facts, not the obvious facts or assumed facts or hoped-for facts. Get the real facts. Facts don’t lie.”

Heck, use the scientific method. Tracy writes:

Create a hypothesis— a yet-to-be-proven theory. Then seek ways to invalidate this hypothesis, to prove that your idea is wrong. This is what scientists do.

This is exactly the opposite of what most people do. They come up with an idea, and then they seek corroboration and proof that their idea is a good one. They practice “confirmation bias.” They only look for confirmation of the validity of the idea, and they simultaneously reject all input or information that is inconsistent with what they have already decided to believe.

Create a negative or reverse hypothesis. This is the opposite of your initial theory. For example, you are Isaac Newton, and the idea of gravity has just occurred to you. Your initial hypothesis would be that “things fall down.” You then attempt to prove the opposite—“things fall up.”

If you cannot prove the reverse or negative hypothesis of your idea, you can then conclude that your hypothesis is correct.

 

***

One of the reasons why Charles Darwin was such an effective thinker is that he relentlessly sought out disconfirming evidence.

As the psychologist Jerry Jampolsky once wrote, “Do you want to be right or do you want to be happy?”

It is amazing how many people come up with a new product or service idea and then fall in love with the idea long before they validate whether or not this is something that a sufficient number of customers are willing to buy and pay for.

Keep gathering information until the proper course of action becomes clear, as it eventually will. Check and double-check your facts. Assume nothing on faith. Ask, “How do we know that this is true?”

Finally, search for the hidden flaw, the one weak area in the decision that could prove fatal to the product or business if it occurred. J. Paul Getty, once the richest man in the world, was famous for his approach to making business decisions. He said, “We first determine that it is a good business opportunity. Then we ask, ‘What is the worst possible thing that could happen to us in this business opportunity?’ We then go to work to make sure that the worst possible outcome does not occur.”

Most importantly, never stop gathering information. One of the reasons that Warren Buffett is so successful is that he spends most of his day reading and thinking. I call this the Buffett Formula.

 

***

If you’re a knowledge worker decisions are your product. Milton Friedman, the economist, wrote: “The best measure of quality thinking is your ability to accurately predict the consequences of your ideas and subsequent actions.”

If there were a single message to Get Smart, it’s another plus in the Farnam Street mold of being conscious. Stop and think before deciding — especially if the consequences are serious. The more ways you have to look at a problem, the more likely you are to better understand. And when you understand a problem — when you really understand a problem — the solution becomes obvious. A friend of mine has a great expression: “To understand is to know what to do.”

Get Smart goes on to talk about goal and result orientated thinking, positive and negative thinking, entrepreneurial vs. corporate thinking and more.

Do Algorithms Beat Us at Complex Decision Making?

Decision-making algorithms are undoubtedly controversial. If a decision is being made that will have a major influence on your life, most people would prefer a human make it. But what if algorithms really can make better decisions?

***

Algorithms are all the rage these days. AI researchers are taking more and more ground from humans in areas like rules-based games, visual recognition, and medical diagnosis. However, the idea that algorithms make better predictive decisions than humans in many fields is a very old one.

In 1954, the psychologist Paul Meehl published a controversial book with a boring sounding name: Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence.

The controversy? After reviewing the data, Meehl claimed that mechanical, data-driven algorithms could better predict human behavior than trained clinical psychologists — and with much simpler criteria. He was right.

The passing of time has not been friendly to humans in this game: Studies continue to show that the algorithms do a better job than experts in a range of fields. In Daniel Kahneman’s Thinking Fast and Slow, he details a selection of fields which have demonstrated inferior human judgment compared to algorithms:

The range of predicted outcomes has expanded to cover medical variables such as the longevity of cancer patients, the length of hospital stays, the diagnosis of cardiac disease, and the susceptibility of babies to sudden infant death syndrome; economic measures such as the prospects of success for new businesses, the evaluation of credit risks by banks, and the future career satisfaction of workers; questions of interest to government agencies, including assessments of the suitability of foster parents, the odds of recidivism among juvenile offenders, and the likelihood of other forms of violent behavior; and miscellaneous outcomes such as the evaluation of scientific presentations, the winners of football games, and the future prices of Bordeaux wine.

The connection between them? Says Kahneman: “Each of these domains entails a significant degree of uncertainty and unpredictability.” He called them “low-validity environments”, and in those environments, simple algorithms matched or outplayed humans and their “complex” decision making criteria, essentially every time.

***

A typical case is described in Michael Lewis’ book on the relationship between Daniel Kahneman and Amos Tversky, The Undoing Project. He writes of work done at the Oregon Research Institute on radiologists and their x-ray diagnoses:

The Oregon researchers began by creating, as a starting point, a very simple algorithm, in which the likelihood that an ulcer was malignant depended on the seven factors doctors had mentioned, equally weighted. The researchers then asked the doctors to judge the probability of cancer in ninety-six different individual stomach ulcers, on a seven-point scale from “definitely malignant” to “definitely benign.” Without telling the doctors what they were up to, they showed them each ulcer twice, mixing up the duplicates randomly in the pile so the doctors wouldn’t notice they were being asked to diagnose the exact same ulcer they had already diagnosed. […] The researchers’ goal was to see if they could create an algorithm that would mimic the decision making of doctors.

This simple first attempt, [Lewis] Goldberg assumed, was just a starting point. The algorithm would need to become more complex; it would require more advanced mathematics. It would need to account for the subtleties of the doctors’ thinking about the cues. For instance, if an ulcer was particularly big, it might lead them to reconsider the meaning of the other six cues.

But then UCLA sent back the analyzed data, and the story became unsettling. (Goldberg described the results as “generally terrifying”.) In the first place, the simple model that the researchers had created as their starting point for understanding how doctors rendered their diagnoses proved to be extremely good at predicting the doctors’ diagnoses. The doctors might want to believe that their thought processes were subtle and complicated, but a simple model captured these perfectly well. That did not mean that their thinking was necessarily simple, only that it could be captured by a simple model.

More surprisingly, the doctors’ diagnoses were all over the map: The experts didn’t agree with each other. Even more surprisingly, when presented with duplicates of the same ulcer, every doctor had contradicted himself and rendered more than one diagnosis: These doctors apparently could not even agree with themselves.

[…]

If you wanted to know whether you had cancer or not, you were better off using the algorithm that the researchers had created than you were asking the radiologist to study the X-ray. The simple algorithm had outperformed not merely the group of doctors; it had outperformed even the single best doctor.

The fact that doctors (and psychiatrists, and wine experts, and so forth) cannot even agree with themselves is a problem called decision making “noise”: Given the same set of data twice, we make two different decisions. Noise. Internal contradiction.

Algorithms win, at least partly, because they don’t do this: The same inputs generate the same outputs every single time. They don’t get distracted, they don’t get bored, they don’t get mad, they don’t get annoyed. Basically, they don’t have off days. And they don’t fall prey to the litany of biases that humans do, like the representativeness heuristic.

The algorithm doesn’t even have to be a complex one. As demonstrated above with radiology, simple rules work just as well as complex ones. Kahneman himself addresses this in Thinking, Fast and Slow when discussing Robyn Dawes’s research on the superiority of simple algorithms using a few equally-weighted predictive variables:

The surprising success of equal-weighting schemes has an important practical implication: it is possible to develop useful algorithms without prior statistical research. Simple equally weight formulas based on existing statistics or on common sense are often very good predictors of significant outcomes. In a memorable example, Dawes showed that marital stability is well predicted by a formula: Frequency of lovemaking minus frequency of quarrels.

You don’t want your result to be a negative number.

The important conclusion from this research is that an algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula, and certainly good enough to outdo expert judgment. This logic can be applied in many domains, ranging from the selection of stocks by portfolio managers to the choices of medical treatments by doctors or patients.

Stock selection, certainly a “low validity environment”, is an excellent example of the phenomenon.

As John Bogle pointed out to the world in the 1970’s, a point which has only strengthened with time, the vast majority of human stock-pickers cannot outperform a simple S&P 500 index fund, an investment fund that operates on strict algorithmic rules about which companies to buy and sell and in what quantities. The rules of the index aren’t complex, and many people have tried to improve on them with less success than might be imagined.

***

Another interesting area where this holds is interviewing and hiring, a notoriously difficult “low-validity” environment. Even elite firms often don’t do it that well, as has been well documented.

Fortunately, if we take heed of the advice of the psychologists, operating in a low-validity environment has rules that can work very well. In Thinking Fast and Slow, Kahneman recommends fixing your hiring process by doing the following (or some close variant), in order to replicate the success of the algorithms:

Suppose you need to hire a sales representative for your firm. If you are serious about hiring the best possible person for the job, this is what you should do. First, select a few traits that are prerequisites for success in this position (technical proficiency, engaging personality, reliability, and so on). Don’t overdo it — six dimensions is a good number. The traits you choose should be as independent as possible from each other, and you should feel that you can assess them reliably by asking a few factual questions. Next, make a list of questions for each trait and think about how you will score it, say on a 1-5 scale. You should have an idea of what you will call “very weak” or “very strong.”

These preparations should take you half an hour or so, a small investment that can make a significant difference in the quality of the people you hire. To avoid halo effects, you must collect the information one at a time, scoring each before you move on to the next one. Do not skip around. To evaluate each candidate, add up the six scores. […] Firmly resolve that you will hire the candidate whose final score is the highest, even if there is another one whom you like better–try to resit your wish to invent broken legs to change the ranking. A vast amount of research offers a promise: you are much more likely to find the best candidate if you use this procedure than if you do what people normally do in such situations, which is to go into the interview unprepared and to make choices by an overall intuitive judgment such as “I looked into his eyes and liked what I saw.”

In the battle of man vs algorithm, unfortunately, man often loses. The promise of Artificial Intelligence is just that. So if we’re going to be smart humans, we must learn to be humble in situations where our intuitive judgment simply is not as good as a set of simple rules.

The Probability Distribution of the Future

The best colloquial definition of risk may be the following:

“Risk means more things can happen than will happen.”

We found it through the inimitable Howard Marks, but it’s a quote from Elroy Dimson of the London Business School. Doesn’t that capture it pretty well?

Another way to state it is: If there were only one thing that could happen, how much risk would there be, except in an extremely banal sense? You’d know the exact probability distribution of the future. If I told you there was a 100% probability that you’d get hit by a car today if you walked down the street, you simply wouldn’t do it. You wouldn’t call walking down the street a “risky gamble” right? There’s no gamble at all.

But the truth is that in practical reality, there aren’t many 100% situations to bank on. Way more things can happen than will happen. That introduces great uncertainty into the future, no matter what type of future you’re looking at: An investment, your career, your relationships, anything.

How do we deal with this in a pragmatic way? The investor Howard Marks starts it this way:

Key point number one in this memo is that the future should be viewed not as a fixed outcome that’s destined to happen and capable of being predicted, but as a range of possibilities and, hopefully on the basis of insight into their respective likelihoods, as a probability distribution.

This is the most sensible way to think about the future: A probability distribution where more things can happen than will happen. Knowing that we live in a world of great non-linearity and with the potential for unknowable and barely understandable Black Swan events, we should never become too confident that we know what’s in store, but we can also appreciate that some things are a lot more likely than others. Learning to adjust probabilities on the fly as we get new information is called Bayesian updating.

But.

Although the future is certainly a probability distribution, Marks makes another excellent point in the wonderful memo above: In reality, only one thing will happen. So you must make the decision: Are you comfortable if that one thing happens, whatever it might be? Even if it only has a 1% probability of occurring? Echoing the first lesson of biology, Warren Buffett stated that “In order to win, you must first survive.” You have to live long enough to play out your hand.

Which leads to an important second point: Uncertainty about the future does not necessarily equate with risk, because risk has another component: Consequences. The world is a place where “bad outcomes” are only “bad” if you know their (rough) magnitude. So in order to think about the future and about risk, we must learn to quantify.

It’s like the old saying (usually before something terrible happens): What’s the worst that could happen? Let’s say you propose to undertake a six month project that will cost your company $10 million, and you know there’s a reasonable probability that it won’t work. Is that risky?

It depends on the consequences of losing $10 million, and the probability of that outcome. It’s that simple! (Simple, of course, does not mean easy.) A company with $10 billion in the bank might consider that a very low-risk bet even if it only had a 10% chance of succeeding.

In contrast, a company with only $10 million in the bank might consider it a high-risk bet even if it only had a 10% of failing. Maybe five $2 million projects with uncorrelated outcomes would make more sense to the latter company.

In the real world, risk = probability of failure x consequences. That concept, however, can be looked at through many lenses. Risk of what? Losing money? Losing my job? Losing face? Those things need to be thought through. When we observe others being “too risk averse,” we might want to think about which risks they’re truly avoiding. Sometimes the risk is not only financial. 

***

Let’s cover one more under-appreciated but seemingly obvious aspect of risk, also pointed out by Marks: Knowing the outcome does not teach you about the risk of the decision.

This is an incredibly important concept:

If you make an investment in 2012, you’ll know in 2014 whether you lost money (and how much), but you won’t know whether it was a risky investment – that is, what the probability of loss was at the time you made it.

To continue the analogy, it may rain tomorrow, or it may not, but nothing that happens tomorrow will tell you what the probability of rain was as of today. And the risk of rain is a very good analogue (although I’m sure not perfect) for the risk of loss.

How many times do we see this simple dictum violated? Knowing that something worked out, we argue that it wasn’t that risky after all. But what if, in reality, we were simply fortunate? This is the Fooled by Randomness effect.

The way to think about it is the following: The worst thing that can happen to a young gambler is that he wins the first time he goes to the casinoHe might convince himself he can beat the system.

The truth is that most times we don’t know the probability distribution at all. Because the world is not a predictable casino game — an error Nassim Taleb calls the Ludic Fallacy — the best we can do is guess.

With intelligent estimations, we can work to get the rough order of magnitude right, understand the consequences if we’re wrong, and always be sure to never fool ourselves after the fact.

If you’re into this stuff, check out Howard Marks’ memos to his clients, or check out his excellent book, The Most Important Thing. Nate Silver also has an interesting similar idea about the difference between risk and uncertainty. And lastly, another guy that understands risk pretty well is Jason Zweig, who we’ve interviewed on our podcast before.

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If you liked this article you’ll love:

Nassim Taleb on the Notion of Alternative Histories — “The quality of a decision cannot be solely judged based on its outcome.”

The Four Types of Relationships — As Seneca said, “Time discovers truth.”

Moving the Finish Line: The Goal Gradient Hypothesis

Imagine a sprinter running an Olympic race. He’s competing in the 1600 meter run.

The first two laps he runs at a steady but hard pace, trying to keep himself consistently near the head, or at least the middle, of the pack, hoping not to fall too far behind while also conserving energy for the whole race.

About 800 meters in, he feels himself start to fatigue and slow. At 1000 meters, he feels himself consciously expending less energy. At 1200, he’s convinced that he didn’t train enough.

Now watch him approach the last 100 meters, the “mad dash” for the finish. He’s been running what would be an all-out sprint to us mortals for 1500 meters, and yet what happens now, as he feels himself neck and neck with his competitors, the finish line in sight?

He speeds up. That energy drag is done. The goal is right there, and all he needs is one last push. So he pushes.

This is called the Goal Gradient Effect, or more precisely, the Goal Gradient Hypothesis. Its effect on biological creatures is not just a feeling, but a real and measurable thing.

The Math of Human Behavior

The first person to try explaining the goal gradient hypothesis was an early behavioral psychologist named Clark L. Hull.

As with other animals, when it came to humans, Hull was a pretty hardcore behaviorist, thinking that human behavior could eventually be reduced to mathematical prediction based on rewards and conditioning. As insane as this sounds now, he had a neat mathematical formula for human behavior:

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Some of his ideas eventually came to be seen as extremely limiting Procrustean Bed type models of human behavior, but the Goal Gradient Hypothesis was replicated many times over the years.

Hull himself wrote papers with titles like The Goal-Gradient Hypothesis and Maze Learning to explore the effect of the idea in rats. As Hull put it, “...animals in traversing a maze will move at a progressively more rapid pace as the goal is approached.” Just like the runner above.

Most of the work Hull focused on were animals rather than humans, showing somewhat unequivocally that in the context of approaching a reward, the animals did seem to speed up as the goal approached, enticed by the end of the maze. The idea was, however, resurrected in the human realm in 2006 with a paper entitled The Goal-Gradient Hypothesis Resurrected: Purchase Acceleration, Illusionary Goal Progress, and Customer Retention. (link)

The paper examined consumer behavior in the “goal gradient” sense and found, alas, it wasn’t just rats that felt the tug of the “end of the race” — we do too. Examining a few different measurable areas of human behavior, the researchers found that consumers would work harder to earn incentives as the goal came within sight and that after the reward was earned, they’d slow down their efforts:

We found that members of a café RP accelerated their coffee purchases as they progressed toward earning a free coffee. The goal-gradient effect also generalized to a very different incentive system, in which shorter goal distance led members to visit a song-rating Web site more frequently, rate more songs during each visit, and persist longer in the rating effort. Importantly, in both incentive systems, we observed the phenomenon of post-reward resetting, whereby customers who accelerated toward their first reward exhibited a slowdown in their efforts when they began work (and subsequently accelerated) toward their second reward. To the best of our knowledge, this article is the first to demonstrate unequivocal, systematic behavioural goal gradients in the context of the human psychology of rewards.

Fascinating.

Putting The Goal Gradient Hypothesis to Work

If we’re to take the idea seriously, the Goal Gradient Hypothesis has some interesting implications for leaders and decision-makers.

The first and most important is probably that incentive structures should take the idea into account. This is a fairly intuitive (but often unrecognized) idea: Far-away rewards are much less motivating than near term ones. Given a chance to earn $1,000 at the end of this month, and each after that, or $12,000 at the end of the year, which would you be more likely to work hard for?

What if I pushed it back even more but gave you some “interest” to compensate: Would you work harder for the potential to earn $90,000 five years from now or to earn $1,000 this month, followed by $1,000 the following month, and so on, every single month during five year period?

Companies like Nucor take the idea seriously: They pay bonuses to lower-level employees based on monthly production, not letting it wait until the end of the year. Essentially, the end of the maze happens every 30 days rather than once per year. The time between doing the work and the reward is shortened.

The other takeaway comes to consumer behavior, as referenced in the marketing paper. If you’re offering rewards for a specific action from your customer, do you reward them sooner, or later?

The answer is almost always going to be “sooner.” In fact, the effect may be strong enough that you can get away with less total rewards by increasing their velocity.

Lastly, we might be able to harness the Hypothesis in our personal lives.

Let’s say we want to start reading more. Do we set a goal to read 52 books this year and hold ourselves accountable, or to read 1 book a week? What about 25 pages per day?

Not only does moving the goalposts forward tend to increase our motivation, but we repeatedly prove to ourselves that we’re capable of accomplishing them. This is classic behavioral psychology: Instant rewards rather than delayed. (Even if they’re psychological.) Not only that, but it forces us to avoid procrastination — leaving 35 books to be read in the last two months of the year, for example.

Those three seem like useful lessons, but here’s a challenge: Try synthesizing a new rule or idea of your own, combining the Goal Gradient Effect with at least one other psychological principle from The Psychology of Human Misjudgment, and start testing it out in your personal life or in your organization. Don’t let useful nuggets sit around; instead, start eating the broccoli.

Peter Bevelin on Seeking Wisdom, Mental Models, Learning, and a Lot More

One of the most impactful books we’ve ever come across is the wonderful Seeking Wisdom: From Darwin to Munger, written by the Swedish investor Peter Bevelin. In the spirit of multidisciplinary learning, Seeking Wisdom is a compendium of ideas from biology, psychology, statistics, physics, economics, and human behavior.

Mr. Bevelin is out with a new book full of wisdom from Warren Buffett & Charlie Munger: All I Want to Know is Where I’m Going to Die So I Never Go There. We were fortunate enough to have a chance to interview Peter recently, and the result is the wonderful discussion below.

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What was the original impetus for writing these books?

The short answer: To improve my thinking. And when I started writing on what later became Seeking Wisdom I can express it even simpler: “I was dumb and wanted to be less dumb.” As Munger says: “It’s ignorance removal…It’s dishonorable to stay stupider than you have to be.” And I had done some stupid things and I had seen a lot of stupidity being done by people in life and in business.

A seed was first planted when I read Charlie Munger’s talk, A Lesson on Worldly Wisdom, and another one where he referred to Darwin as a great thinker. So I said to myself: I am 42 now. Why not take some time off business and spend a year learning, reflecting and write about the subject Munger introduced to me – human behavior and judgments.

None of my writings started out as a book project. I wrote my first book – Seeking Wisdom – as a memorandum for myself with the expectation that I could transfer some of its essentials to my children. I learn and write because I want to be a little wiser day by day. I don’t want to be a great-problem-solver. I want to avoid problems – prevent them from happening and doing right from the beginning. And I focus on consequential decisions. To paraphrase Buffett and Munger – decision-making is not about making brilliant decisions, but avoiding terrible ones. Mistakes and dumb decisions are a fact of life and I’m going to make more, but as long as I can avoid the big or “fatal” ones I’m fine.

So I started to read and write to learn what works and not and why. And I liked Munger’s “All I want to know is where I’m going to die so I’ll never go there” approach. And as he said, “You understand it better if you go at it the way we do, which is to identify the main stupidities that do bright people in and then organize your patterns for thinking and developments, so you don’t stumble into those stupidities.” Then I “only” had to a) understand the central “concept” and its derivatives and describe it in as simple way as possible for me and b) organize what I learnt in a way that was logical and useful for me.

And what better way was there to learn this from those who already knew this?

After I learnt some things about our brain, I understood that thinking doesn’t come naturally to us humans – most is just unconscious automatic reactions. Therefore I needed to set up the environment and design a system that helped me make it easier to know what to do and prevent and avoid harm. Things like simple rules of thumbs, tricks and filters. Of course, I could only do that if I first had the foundation. And as the years have passed, I’ve found that filters are a great way to save time and misery. As Buffett says, “I process information very quickly since I have filters in my mind.” And they have to be simple – as the proverb says, “Beware of the door that has too many keys.” The more complicated a process is, the less effective it is.

Why do I write? Because it helps me understand and learn better. And if I can’t write something down clearly, then I have not really understood it. As Buffett says, “I learn while I think when I write it out. Some of the things, I think I think, I find don’t make any sense when I start trying to write them down and explain them to people … And if it can’t stand applying pencil to paper, you’d better think it through some more.”

My own test is one that a physicist friend of mine told me many years ago, ‘You haven’t really understood an idea if you can’t in a simple way describe it to almost anyone.’ Luckily, I don’t have to understand zillion of things to function well.

And even if some of mine and others thoughts ended up as books, they are all living documents and new starting points for further, learning, un-learning and simplifying/clarifying. To quote Feynman, “A great deal of formulation work is done in writing the paper, organizational work, organization. I think of a better way, a better way, a better way of getting there, of proving it. I never do much — I mean, it’s just cleaner, cleaner and cleaner. It’s like polishing a rough-cut vase. The shape, you know what you want and you know what it is. It’s just polishing it. Get it shined, get it clean, and everything else.

Which book did you learn the most from the experience of writing/collecting?

Seeking Wisdom because I had to do a lot of research – reading, talking to people etc. Especially in the field of biology and brain science since I wanted to first understand what influences our behavior. I also spent some time at a Neurosciences Institute to get a better understanding of how our anatomy, physiology and biochemistry constrained our behavior.

And I had to work it out my own way and write it down in my own words so I really could understand it. It took a lot of time but it was a lot of fun to figure it out and I learnt much more and it stuck better than if I just had tried to memorize what somebody else had already written. I may not have gotten everything letter perfect but good enough to be useful for me.

As I said, the expectation wasn’t to create a book. In fact, that would have removed a lot of my motivation. I did it because I had an interest in becoming better. It goes back to the importance of intrinsic motivation. As I wrote in Seeking Wisdom: “If we reward people for doing what they like to do anyway, we sometimes turn what they enjoy doing into work. The reward changes their perception. Instead of doing something because they enjoy doing it, they now do it because they are being paid. The key is what a reward implies. A reward for our achievements makes us feel that we are good at something thereby increasing our motivation. But a reward that feels controlling and makes us feel that we are only doing it because we’re paid to do it, decreases the appeal.

It may sound like a cliché but the joy was in the journey – reading, learning and writing – not the destination – the finished book. Has the book made a difference for some people? Yes, I hope so but often people revert to their old behavior. Some of them are the same people who – to paraphrase something that is attributed to Churchill – occasionally should check their intentions and strategies against their results. But reality is what Munger once said, “Everyone’s experience is that you teach only what a reader almost knows, and that seldom.” But I am happy that my books had an impact and made a difference to a few people. That’s enough.

Why did the new book (All I Want To Know Is Where I’m Going To Die So I’ll Never Go There) have a vastly different format?

It was more fun to write about what works and not in a dialogue format. But also because vivid and hopefully entertaining “lessons” are easier to remember and recall. And you will find a lot of quotes in there that most people haven’t read before.

I wanted to write a book like this to reinforce a couple of concepts in my head. So even if some of the text sometimes comes out like advice to the reader, I always think about what the mathematician Gian-Carlo Rota once said, “The advice we give others is the advice that we ourselves need.”

How do you define Mental Models?

Some kind of representation that describes how reality is (as it is known today) – a principle, an idea, basic concepts, something that works or not – that I have in my head that helps me know what to do or not. Something that has stood the test of time.

For example, some timeless truths are:

  • Reality is that complete competitors – same product/niche/territory – cannot coexist (Competitive exclusion principle). What works is going where there is no or very weak competition + differentiation/advantages that others can’t copy (assuming of course we have something that is needed/wanted now and in the future)
  • Reality is that we get what we reward for. What works is making sure we reward for what we want to achieve.

I favor underlying principles and notions that I can apply broadly to different and relevant situations. Since some models don’t resemble reality, the word “model” for me is more of an illustration/story of an underlying concept, trick, method, what works etc. that agrees with reality (as Munger once said, “Models which underlie reality”) and help me remember and more easily make associations.

But I don’t judge or care how others label it or do it – models, concepts, default positions … The important thing is that whatever we use, it reflects and agrees with reality and that it works for us to help us understand or explain a situation or know what to do or not do. Useful and good enough guide me. I am pretty pragmatic – whatever works is fine. I follow Deng Xiaoping, “I don’t care whether the cat is black or white as long as it catches mice.” As Feynman said, “What is the best method to obtain the solution to a problem? The answer is, any way that works.

I’ll tell you about a thing Feynman said on education which I remind myself of from time to time in order not to complicate things (from Richard P. Feynman, Michael A. Gottlieb, Ralph Leighton, Feynman’s Tips on Physics: A Problem-Solving Supplement to the Feynman Lectures on Physics):

“There’s a round table on three legs. Where should you lean on it, so the table will be the most unstable?”
The student’s solution was, “Probably on top of one of the legs, but let me see: I’ll calculate how much force will produce what lift, and so on, at different places.”
Then I said, “Never mind calculating. Can you imagine a real table?”
“But that’s not the way you’re supposed to do it!”
“Never mind how you’re supposed to do it; you’ve got a real table here with the various legs, you see? Now, where do you think you’d lean? What would happen if you pushed down directly over a leg?”
“Nothin’!”
I say, “That’s right; and what happens if you push down near the edge, halfway between two of the legs?”
“It flips over!”
I say, “OK! That’s better!”
The point is that the student had not realized that these were not just mathematical problems; they described a real table with legs. Actually, it wasn’t a real table, because it was perfectly circular, the legs were straight up and down, and so on. But it nearly described, roughly speaking, a real table, and from knowing what a real table does, you can get a very good idea of what this table does without having to calculate anything – you know darn well where you have to lean to make the table flip over. So, how to explain that, I don’t know! But once you get the idea that the problems are not mathematical problems but physical problems, it helps a lot.
Anyway, that’s just two ways of solving this problem. There’s no unique way of doing any specific problem. By greater and greater ingenuity, you can find ways that require less and less work, but that takes experience.

Which mental models “carry the most freight?” (Related follow up: Which concepts from Buffett/Munger/Mental Models do you find yourself referring to or appreciating most frequently?)

Ideas from biology and psychology since many stupidities are caused by not understanding human nature (and you get illustrations of this nearly every day). And most of our tendencies were already known by the classic writers (Publilius Syrus, Seneca, Aesop, Cicero etc.)

Others that I find very useful both in business and private is the ideas of Quantification (without the fancy math), Margin of safety, Backups, Trust, Constraints/Weakest link, Good or Bad Economics slash Competitive advantage, Opportunity cost, Scale effects. I also think Keynes idea of changing your mind when you get new facts or information is very useful.

But since reality isn’t divided into different categories but involves a lot of factors interacting, I need to synthesize many ideas and concepts.

Are there any areas of the mental models approach you feel are misunderstood or misapplied?

I don’t know about that but what I often see among many smart people agrees with Munger’s comment: “All this stuff is really quite obvious and yet most people don’t really know it in a way where they can use it.”

Anyway, I believe if you really understand an idea and what it means – not only memorizing it – you should be able to work out its different applications and functional equivalents. Take a simple big idea – think on it – and after a while you see its wider applications. To use Feynman’s advice, “It is therefore of first-rate importance that you know how to “triangulate” – that is, to know how to figure something out from what you already know.” As a good friend says, “Learn the basic ideas, and the rest will fill itself in. Either you get it or you don’t.”

Most of us learn and memorize a specific concept or method etc. and learn about its application in one situation. But when the circumstances change we don’t know what to do and we don’t see that the concept may have a wider application and can be used in many situations.

Take for example one big and useful idea – Scale effects. That the scale of size, time and outcomes changes things – characteristics, proportions, effects, behavior…and what is good or not must be tied to scale. This is a very fundamental idea from math. Munger described some of this idea’s usefulness in his worldly wisdom speech. One effect from this idea I often see people miss and I believe is important is group size and behavior. That trust, feeling of affection and altruistic actions breaks down as group size increases, which of course is important to know in business settings. I wrote about this in Seeking Wisdom (you can read more if you type in Dunbar Number on Google search). I know of some businesses that understand the importance of this and split up companies into smaller ones when they get too big (one example is Semco).

Another general idea is “Gresham’s Law” that can be generalized to any process or system where the bad drives out the good. Like natural selection or “We get what we select for” (and as Garrett Hardin writes, “The more general principle is: We get whatever we reward for).

While we are on the subject of mental models etc., let me bring up another thing that distinguishes the great thinkers from us ordinary mortals. Their ability to quickly assess and see the essence of a situation – the critical things that really matter and what can be ignored. They have a clear notion of what they want to achieve or avoid and then they have this ability to zoom in on the key factor(s) involved.

One reason to why they can do that is because they have a large repertoire of stored personal and vicarious experiences and concepts in their heads. They are masters at pattern recognition and connection. Some call it intuition but as Herbert Simon once said, “The situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition.

It is about making associations. For example, roughly like this:
Situation X Association (what does this remind me of?) to experience, concept, metaphor, analogy, trick, filter… (Assuming of course we are able to see the essence of the situation) What counts and what doesn’t? What works/not? What to do or what to explain?

Let’s take employing someone as an example (or looking at a business proposal). This reminds me of one key factor – trustworthiness and Buffett’s story, “If you’re looking for a manager, find someone who is intelligent, energetic and has integrity. If he doesn’t have the last, make sure he lacks the first two.”

I believe Buffett and Munger excel at this – they have seen and experienced so much about what works and not in business and behavior.

Buffett referred to the issue of trust, chain letters and pattern recognition at the latest annual meeting:

You can get into a lot of trouble with management that lacks integrity… If you’ve got an intelligent, energetic guy or woman who is pursuing a course of action, which gets put on the front page it could make you very unhappy. You can get into a lot of trouble. ..We’ve seen patterns…Pattern recognition is very important in evaluating humans and businesses. Pattern recognition isn’t one hundred percent and none of the patterns exactly repeat themselves, but there are certain things in business and securities markets that we’ve seen over and over and frequently come to a bad end but frequently look extremely good in the short run. One which I talked about last year was the chain letter scheme. You’re going to see chain letters for the rest of your life. Nobody calls them chain letters because that’s a connotation that will scare you off but they’re disguised as chain letters and many of the schemes on Wall Street, which are designed to fool people, have that particular aspect to it…There were patterns at Valeant certainly…if you go and watch the Senate hearings, you will see there are patterns that should have been picked up on.

This is what he wrote on chain letters in the 2014 annual report:

In the late 1960s, I attended a meeting at which an acquisitive CEO bragged of his “bold, imaginative accounting.” Most of the analysts listening responded with approving nods, seeing themselves as having found a manager whose forecasts were certain to be met, whatever the business results might be. Eventually, however, the clock struck twelve, and everything turned to pumpkins and mice. Once again, it became evident that business models based on the serial issuances of overpriced shares – just like chain-letter models – most assuredly redistribute wealth, but in no way create it. Both phenomena, nevertheless, periodically blossom in our country – they are every promoter’s dream – though often they appear in a carefully-crafted disguise. The ending is always the same: Money flows from the gullible to the fraudster. And with stocks, unlike chain letters, the sums hijacked can be staggering.

And of course, the more prepared we are or the more relevant concepts and “experiences” we have in our heads, the better we all will be at this. How do we get there? Reading, learning and practice so we know it “fluently.” There are no shortcuts. We have to work at it and apply it to the real world.

As a reminder to myself so I understand my limitation and “circle”, I keep a paragraph from Munger’s USC Gould School of Law Commencement Address handy so when I deal with certain issues, I don’t fool myself into believing I am Max Planck when I’m really the Chauffeur:

In this world I think we have two kinds of knowledge: One is Planck knowledge, that of the people who really know. They’ve paid the dues, they have the aptitude. Then we’ve got chauffeur knowledge. They have learned to prattle the talk. They may have a big head of hair. They often have fine timbre in their voices. They make a big impression. But in the end what they’ve got is chauffeur knowledge masquerading as real knowledge.

Which concepts from Buffett/Munger/Mental Models do you find most counterintuitive?

One trick or notion I see many of us struggling with because it goes against our intuition is the concept of inversion – to learn to think “in negatives” which goes against our normal tendency to concentrate on for example, what we want to achieve or confirmations instead of what we want to avoid and disconfirmations. Another example of this is the importance of missing confirming evidence (I call it the “Sherlock trick”) – that negative evidence and events that don’t happen, matter when something implies they should be present or happen.

Another example that is counterintuitive is Newton’s 3d law that forces work in pairs. One object exerts a force on a second object, but the second object also exerts a force equal and opposite in direction to the force acting on it – the first object. As Newton wrote, “If you press a stone with your finger, the finger is also pressed by the stone.” Same as revenge (reciprocation).

Who are some of the non-obvious, or under-the-radar thinkers that you greatly admire?

One that immediately comes to mind is one I have mentioned in the introduction in two of my books is someone I am fortunate to have as a friend – Peter Kaufman. An outstanding thinker and a great businessman and human being. On a scale of 1 to 10, he is a 15.

What have you come to appreciate more with Buffett/Munger’s lessons as you’ve studied them over the years?

Their ethics and their ethos of clarity, simplicity and common sense. These two gentlemen are outstanding in their instant ability to exclude bad ideas, what doesn’t work, bad people, scenarios that don’t matter, etc. so they can focus on what matters. Also my amazement that their ethics and ideas haven’t been more replicated. But I assume the answer lies in what Munger once said, “The reason our ideas haven’t spread faster is they’re too simple.”

This reminds me something my father-in-law once told me (a man I learnt a lot from) – the curse of knowledge and the curse of academic title. My now deceased father-in-law was an inventor and manager. He did not have any formal education but was largely self-taught. Once a big corporation asked for his services to solve a problem their 60 highly educated engineers could not solve. He solved the problem. The engineers said, “It can’t be that simple.” It was like they were saying that, “Here we have 6 years of school, an academic title, lots of follow up education. Therefore an engineering problem must be complicated”. Like Buffett once said of Ben Graham’s ideas, “I think that it comes down to those ideas – although they sound so simple and commonplace that it kind of seems like a waste to go to school and get a PhD in Economics and have it all come back to that. It’s a little like spending eight years in divinity school and having somebody tell you that the 10 commandments were all that counted. There is a certain natural tendency to overlook anything that simple and important.”

(I must admit that in the past I had a tendency to be extra drawn to elegant concepts and distracting me from the simple truths.)

What things have you come to understand more deeply in the past few years?

  • That I don’t need hundreds of concepts, methods or tricks in my head – there are a few basic, time-filtered fundamental ones that are good enough. As Munger says, “The more basic knowledge you have the less new knowledge you have to get.” And when I look at something “new”, I try to connect it to something I already understand and if possible get a wider application of an already existing basic concept that I already have in my head.
  • Neither do I have to learn everything to cover every single possibility – not only is it impossible but the big reason is well explained by the British statistician George Box. He said that we shouldn’t be preoccupied with optimal or best procedures but good enough over a range of possibilities likely to happen in practice – circumstances which the world really present to us.
  • The importance of “Picking my battles” and focus on the long-term consequences of my actions. As Munger said, “A majority of life’s errors are caused by forgetting what one is really trying to do.”
  • How quick most of us are in drawing conclusions. For example, I am often too quick in being judgmental and forget how I myself behaved or would have behaved if put in another person’s shoes (and the importance of seeing things from many views).
  • That I have to “pick my poison” since there is always a set of problems attached with any system or approach – it can’t be perfect. The key is try to move to a better set of problems one can accept after comparing what appear to be the consequences of each.
  • How efficient and simplified life is when you deal with people you can trust. This includes the importance of the right culture.
  • The extreme importance of the right CEO – a good operator, business person and investor.
  • That luck plays a big role in life.
  • That most predictions are wrong and that prevention, robustness and adaptability is way more important. I can’t help myself – I have to add one thing about the people who give out predictions on all kinds of things. Often these are the people who live in a world where their actions have no consequences and where their ideas and theories don’t have to agree with reality.
  • That people or businesses that are foolish in one setting often are foolish in another one (“The way you do anything, is the way you do everything”).
  • Buffett’s advice that “A checklist is no substitute for thinking.” And that sometimes it is easy to overestimate one’s competency in a) identifying or picking what the dominant or key factors are and b) evaluating them including their predictability. That I believe I need to know factor A when I really need to know B – the critical knowledge that counts in the situation with regards to what I want to achieve.
  • Close to this is that I sometimes get too involved in details and can’t see the forest for the trees and I get sent up too many blind alleys. Just as in medicine where a whole body scan sees too much and sends the doctor up blind alleys.
  • The wisdom in Buffett’s advice that “You only have to be right on a very, very few things in your lifetime as long as you never make any big mistakes…An investor needs to do very few things right as long as he or she avoids big mistakes.”

What’s the best investment of time/effort/money that you’ve ever made?

The best thing I have done is marrying my wife. As Buffett says and it is so so true, “Choosing a spouse is the most important decision in your life…You need everything to be stable, and if that decision isn’t good, it may affect every other decision in life, including your business decisions…If you are lucky on health and…on your spouse, you are a long way home.”

A good “investment” is taking the time to continuously improve. It just takes curiosity and a desire to know and understand – real interest. And for me this is fun.

What does your typical day look like? (How much time do you spend reading… and when?)

Every day is a little different but I read every day.

What book has most impacted your life?

There is not one single book or one single idea that has done it. I have picked up things from different books (still do). And there are different books and articles that made a difference during different periods of my life. Meeting and learning from certain people and my own practical experiences has been more important in my development. As an example – When I was in my 30s a good friend told me something that has been very useful in looking at products and businesses. He said I should always ask who the real customer is: “Who ultimately decides what to buy and what are their decision criteria and how are they measured and rewarded and who pays?

But looking back, if I have had a book like Poor Charlie’s Almanack when I was younger I would have saved myself some misery. And of course, when it comes to business, managing and investing, nothing beats learning from Warren Buffett’s Letters to Berkshire Hathaway Shareholders.

Another thing I have found is that it is way better to read and reread fewer books but good and timeless ones and then think. Unfortunately many people absorb too many new books and information without thinking.

Let me finish this with some quotes from my new book that I believe we all can learn from:

  • “There’s no magic to it…We haven’t succeeded because we have some great, complicated systems or magic formulas we apply or anything of the sort. What we have is just simplicity itself.” – Buffett
  • “Our ideas are so simple that people keep asking us for mysteries when all we have are the most elementary ideas…There’s nothing remarkable about it. I don’t have any wonderful insights that other people don’t have. Just slightly more consistently than others, I’ve avoided idiocy…It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” – Munger
  • “It really is simple – just avoid doing the dumb things. Avoiding the dumb things is the most important.” – Buffett

Finally, I wish you and your readers an excellent day – Everyday!