Tag: Mental Models

The Three Buckets of Knowledge

The three most fundamental sources of knowledge are physics, math, and human history. They offer us endless learning and mental models. Here’s how mastering the three buckets of knowledge can give you a deeper understanding of the world.

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When we seek to understand the world, we’re faced with a basic question: Where do I start? Which sources of knowledge are the most useful and the most fundamental?

FS takes its lead here from Charlie Munger, who argued that the “base” of your intellectual pyramid should be the great ideas from the big academic disciplines: Mental models (For a great book on the subject check out The Great Mental Models series.)

Similarly, Peter Kaufman’s idea, presented above, is that we can learn the most fundamental knowledge from the three oldest and most invariant forms of knowledge: Physics and math, from which we derive the rules the universe plays by; biology, from which we derive the rules life on Earth plays by; and human history, from which we derive the rules humans have played by.

“Every statistician knows that a large, relevant sample size is their best friend. What are the three largest, most relevant sample sizes for identifying universal principles? Bucket number one is inorganic systems, which are 13.7 billion years in size. It’s all the laws of math and physics, the entire physical universe. Bucket number two is organic systems, 3.5 billion years of biology on Earth. And bucket number three is human history, you can pick your own number, I picked 20,000 years of recorded human behavior. Those are the three largest sample sizes we can access and the most relevant.”

— Peter Kaufman

With that starting point, we’ve explored a lot of ideas and read a lot of books, looking for connections among the big, broad areas of useful knowledge. Our search led us to a wonderful book called The Lessons of History, which we’ve previously discussed.

The book is a hundred-page distillation of the lessons learned in 50 years of work by two brilliant historians, Will and Ariel Durant. The Durants’ spent those years writing a sweeping 11-book, 10,000-page synthesis of the major figures and periods in human history, with an admitted focus on Western civilization.(Although they admirably tackle Eastern civilization up to 1930 or so in the epic Our Oriental Heritage.) With The Lessons of History, the pair sought to derive a few major lessons learned from the long pull.

Let’s explore a few ways in which the Durants’ work connects with the three buckets of human knowledge that help us understand the world at a deep level.

Lessons of Geologic Time

Durant has a classic introduction for this kind of “big synthesis” historical work:

Since man is a moment in astronomic time, a transient guest of the earth, a spore of his species, a scion of his race, a composite of body, character, and mind, a member of a family and a community, a believer or doubter of a faith, a unit in an economy, perhaps a citizen in a state or a soldier in an army, we may ask under the corresponding heads—astronomy, geology, geography, biology, ethnology, psychology, morality, religion, economics, politics, and war—what history has to say about the nature, conduct, and prospects of man. It is a precarious enterprise, and only a fool would try to compress a hundred centuries into a hundred pages of hazardous conclusions. We proceed.

The first topic Durant approaches is our relationship to the physical Earth, a group of knowledge we can place in the second bucket, in Kaufman’s terms. We must recognize that the varieties of geology and physical climate we live in have to a large extent determined the course of human history. (Jared Diamond would agree, that being a major component of his theory of human history.)

History is subject to geology. Every day the sea encroaches somewhere upon the land, or the land upon the sea; cities disappear under the water, and sunken cathedrals ring their melancholy bells. Mountains rise and fall in the rhythm of emergence and erosion; rivers swell and flood, or dry up, or change their course; valleys become deserts, and isthmuses become straits. To the geologic eye all the surface of the earth is a fluid form, and man moves upon it as insecurely as Peter walking on the waves to Christ.

There are some big, useful lessons we can draw from studying geologic time. The most obvious might be the concept of gradualism, or slow incremental change over time. This was most well-understood by Darwin, who applied that form of reasoning to understand the evolution of species. His hero was Charles Lyell, whose Principles of Geology created our understanding of a slow, move-ahead process on the long scale of geology.

And of course, that model is quite practically useful to us today — it is through slow, incremental, grinding change, punctuated at times by large-scale change when necessary and appropriate, that things move ahead most reliably. We might be reminded in the modern corporate world of General Electric, which ground ahead from an electric lamp company to an industrial giant, step-by-step over a long period which destroyed many thousands of lesser companies with less adaptive cultures.

We can also use this model to derive the idea of human nature as nearly fixed; it changes in geologic time, not human time. This explains why the fundamental problems of history tend to recur. We’re basically the same as we’ve always been:

History repeats itself in the large because human nature changes with geological leisureliness, and man is equipped to respond in stereotyped ways to frequently occurring situations and stimuli like hunger, danger, and sex. But in a developed and complex civilization individuals are more differentiated and unique than in a primitive society, and many situations contain novel circumstances requiring modifications of instinctive response; custom recedes, reasoning spreads; the results are less predictable. There is no certainty that the future will repeat the past. Every year is an adventure.

Lastly, Mother Nature’s long history also teaches us something of resilience, which is connected to the idea of grind-ahead change. Studying evolution helps us understand that what is fragile will eventually break under the stresses of competition: Most importantly, fragile relationships break, but strong win-win relationships have super glue that keeps parties together. We also learn that weak competitive positions are eventually rooted out due to competition and new environments, and that a lack of adaptiveness to changing reality is a losing strategy when the surrounding environment shifts enough. These and others are fundamental knowledge and work the same in human organizations as in Nature.

The Biology of History

Durant moves from geology into the realm of human biology: Our nature determines the “arena” in which the human condition can play out. Human biology gives us the rules of the chessboard, and the Earth and its inhabitants provide the environment in which we play the game. The variety of outcomes approaches infinity from this starting point. That’s why this “bucket” of human knowledge is such a crucial one to study. We need to know the rules.

Thinking with the first “bucket” of knowledge — the mathematics and physics that drive all things in the universe — it’s easy to derive that compounding multiplication can take a small population and make it a very large one over a comparatively short time. 2 becomes 4 becomes 8 becomes 16, and so on. But because we also know that the spoils of the physical world are finite, the “Big Model” of Darwinian natural selection flows naturally from the compounding math: As populations grow but their surroundings offer limitations, there must be a way to derive who gets the spoils.

Not only does this provide the basis for biological competition over resources, a major lesson in the second bucket, it also provides the basis for the political and economic systems in bucket three of human history: Our various systems of political and economic organization are fundamentally driven by decisions on how to give order and fairness to the brutal reality created by human competition.

In this vein, we have previously discussed Durant’s three lessons of biological history: Life is Competition. Life is Selection. Life must Replicate. These simple precepts lead to the interesting results in biology, and most relevant to us, to similar interesting results in human culture itself:

Like other departments of biology, history remains at bottom a natural selection of the fittest individuals and groups in a struggle wherein goodness receives no favors, misfortunes abound, and the final test is the ability to survive.

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We do, however, need to be careful to think with the right “bucket” at the right time. Durant offers us a cautionary tale here: The example of the growth and decay of societies shows an area where the third bucket, human culture, offers a different reality than what a simple analogy from physics or biology might show. Cultural decay is not inevitable, as it might be with an element or a physical organism:

If these are the sources of growth, what are the causes of decay? Shall we suppose, with Spengler and many others, that each civilization is an organism, naturally and yet mysteriously endowed with the power of development and the fatality of death? It is tempting to explain the behavior of groups through analogy with physiology or physics, and to ascribe the deterioration of a society to some inherent limit in its loan and tenure of life, or some irreparable running down of internal force. Such analogies may offer provisional illumination, as when we compare the association of individuals with an aggregation of cells, or the circulation of money from banker back to banker with the systole and diastole of the heart.

But a group is no organism physically added to its constituent individuals; it has no brain or stomach of its own; it must think or feel with the brains or nerves of its members. When the group or a civilization declines, it is through no mystic limitation of a corporate life, but through the failure of its political or intellectual leaders to meet the challenges of change.

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But do civilizations die? Again, not quite. Greek civilization is not really dead; only its frame is gone and its habitat has changed and spread; it survives in the memory of the race, and in such abundance that no one life, however full and long, could absorb it all. Homer has more readers now than in his own day and land. The Greek poets and philosophers are in every library and college; at this moment Plato is being studied by a hundred thousand discoverers of the “dear delight” of philosophy overspreading life with understanding thought. This selective survival of creative minds is the most real and beneficent of immortalities.

In this sense, the ideas that thrive in human history are not bound by the precepts of physics. Knowledge — the kind which can be passed from generation to generation in an accumulative way — is a unique outcome in the human culture bucket. Other biological creatures only pass down DNA, not accumulated learning. (Yuval Harari similarly declared that“The Cognitive Revolution is accordingly the point when history declared its independence from biology.”)

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With that caveat in mind, the concept of passed-down ideas does have some predictable overlap with major mental models of the first two buckets of physics/math and biology.

The first is compounding: Ideas and knowledge compound in the same mathematical way that money or population does. If I have an idea and tell my idea to you, we both have the idea. If we each take that idea and recombine it with another idea we already had, we now have three ideas from a starting point of only one. If we can each connect that one idea to two ideas we had, we now have five ideas between us. And so on — you can see how compounding would take place as we told our friends about the five ideas and they told theirs. So the Big Model of compound interest works on ideas too.

The second interplay is to see that human ideas go through natural selection in the same way biological life does.

Intellect is therefore a vital force in history, but it can also be a dissolvent and destructive power. Out of every hundred new ideas ninety-nine or more will probably be inferior to the traditional responses which they propose to replace. No one man, however brilliant or well-informed, can come in one lifetime to such fullness of understanding as to safely judge and dismiss the customs or institutions of his society, for these are the wisdom of generations after centuries of experiment in the laboratory of history.

This doesn’t tell us that the best ideas survive any more than natural selection tells us that the best creatures survive. It just means, at the risk of being circular, that the ideas most fit for propagation are the ones that survive for a long time. Most truly bad ideas tend to get tossed out in the vicissitudes of time either through the early death of their proponents or basic social pressure. But any idea that strikes a fundamental chord in humanity can last a very long time, even if it’s wrong or harmful. It simply has to be memorable and have at least a kernel of intuitive truth.

For more, start thinking about the three buckets of knowledge, read Durant, and start getting to work on synthesizing as much as possible.

Joseph Tussman: Getting the World to Do the Work for You

“What the pupil must learn, if he learns anything at all, is that the world will do most of the work for you, provided you cooperate with it by identifying how it really works and aligning with those realities. If we do not let the world teach us, it teaches us a lesson.”

— Joseph Tussman

Nothing better sums up the ethos of Farnam Street than the quote above by Joseph Tussman.

How’s that for a guiding principle?

Tussman was a philosophy professor at Cal Berkley and an educational reformer. We got this beautiful quote from a friend of ours in California. Isn’t it brilliant?

The world will do a lot of the work for us if we only align with how it works and stop fighting it. Most of the time we want the world to work differently so we work against it. What Tussman really does is identify a leverage point.

Leverage amplifies an input to provide greater output. There are leverage points in all systems. To know the leverage point is to know where to apply your effort. Focusing on the leverage point will yield non-linear results. Doesn’t that sound like something we want to look for?

Working hard and being busy is not enough. Most people are taking two steps forward and one step back. They’re busy, but they haven’t moved anywhere.

We need to work smarter not harder.

What Tussman has done is identify a leverage point in life. One that will increase what you can accomplish (through tailwinds) and reduced friction. When we work smart rather than hard, we apply energy in the same direction.

The person who needs a new mental tool and doesn’t have it is already paying for it. This is how we should be thinking about the acquisition of worldly wisdom. We’re like plumbers who show up with a lot of wrenches but no blowtorches, and our results largely reflect that. We get the job half done in twice the time.

A better approach is the one Tussman suggests. Learn from the world. The best way to identify how the world works is to find the general principles that line up with historically significant sample sizes — those that apply, in the words of Peter Kaufman, “across the geological time scale of human, organic, and inorganic history.”

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Still Curious? Pair with Andy Benoit’s wisdom and make some time to think about them.

Three Filters Needed to Think Through Problems

One of the best parts of Garrett Hardin‘s wonderful Filters Against Folly is when he explores the three filters that help us interpret reality. No matter how much we’d like it to, the world does not only operate in our circle of competence. Thus we must learn ways to distinguish reality in areas where we lack even so much as a map.

“Most geniuses—especially those who lead others—prosper not by deconstructing intricate complexities but by exploiting unrecognized simplicities.”

— Andy Benoit

Mental Tools

We need not be a genius in every area but we should understand the big ideas of most disciplines and try to avoid fooling ourselves. That’s the core to the mental models approach. When you’re not an expert in a field, often the best approach is one that avoids stupidity. There are few better ways of avoiding stupidity than understanding how the world works.

Hardin begins by outlining his goal: to understand reality and understand human nature as it really is, removing premature judgment from the analysis.

He appropriately quotes Spinoza, who laid out his principles for political science thusly:

That I might investigate the subject matter of this science with the same freedom of spirit we generally use in mathematics, I have labored carefully not to mock, lament, or execrate human actions, but to understand them; and to this end I have looked upon passions such as love, hatred, anger, envy, ambition, pity, and other perturbations of the mind, not in the light of vices of human nature, but as properties just as pertinent to it as are heat, cold, storm, thunder, and the like to the nature of the atmosphere.

The goal of these mental filters, then, is to understand reality by improving our ability to judge the statements of experts, promoters, and persuaders of all kinds. As the saying goes, we are all laymen in some field.

Hardin writes:

What follows is one man’s attempt to show that there is more wisdom among the laity than is generally concluded, and that there are some rather simple methods of checking on the validity of the statements of experts.

1. The Literate Filter

The first filter through which we must interpret reality, says Hardin, is the literate filter: What do the words really mean? The key to remember is that Language is action. Language is not just a way to communicate or interpret; language acts as a call to, or just as importantly, an inhibitor to action.

The first step is to try to understand what is really being said. What do the words and the labels actually mean? If a politician proposes a “Poverty Assistance Plan,” that sounds almost inarguably good, no? Many a pork-barrel program has passed based on such labels alone.

But when you examine the rhetoric, you must ask what those words are trying to do: Promote understanding, or inhibit it? If the program had a rational method of assistance to the deserving poor, the label might be appropriate. If it was simply a way to reward undeserving people in his or her district for their vote, the label might be simply a way to fool. The literate filter asks if we understand the true intent behind the words.

In a chapter called “The Sins of the Literate,” Hardin discusses the misuse of language by examining literate, but innumerate, concepts like “indefinite” or “infinite”:

He who introduces the words “infinity” or any of its derivatives (“forever” or “never” for instance) is also trying to escape discussion. Unfortunately he does not honestly admit the operational meaning of the high-flown language used to close off discussion. “Non-negotiable” is a dated term, no longer in common use, but “infinity” endures forever.

Like old man Proteus of Greek mythology, the wish to escape debate disguises itself under a multitude of verbal forms: infinity, non-negotiable, never, forever, irresistible, immovable, indubitable, and the recent variant “not meaningfully finite.” All these words have the effect of moving discussion out of the numerate realm, where it belongs, and into a wasteland of pure literacy, where counting and measuring are repudiated.

Later, in the final chapter, Hardin repeats:

The talent for handling words is called “eloquence.” Talent is always desirable, but the talent may have an unfair, even dangerous, advantage over those with less talent. More than a century ago Ralph Waldo Emerson said, “The curse of this country is eloquent men.” The curse can be minimized by using words themselves to point out the danger of words. One of their functions is to act as inhibitors of thought. People need to be made allergic to such thought-stoppers as infinity, sacred, and absolute. The real world is a world of quantified entities: “infinity” and its like are no words for quantities but utterances used to divert attention from quantities and limits.

It is not just innumerate exaggeration we are guarding against, but the literate tendency to replace actors with abstractions, as Hardin calls it. He uses the example of donating money to a poor country (Country X), which on its face sounds noble:

Country X, which is an abstraction, cannot act. Those who act in its name are rich and powerful people. Human nature being what it is, we can be sure that these people will not voluntarily do anything to diminish either their power or their riches…

Not uncommonly, the major part of large quantities of food sent in haste to a poor country in the tropics rot on the docks or is eaten up by rats before it can be moved to the people who need it. The wastage is seldom adequately reported back to the sending country…(remember), those who gain personally from the shipping of food to poor nations gain whether fungi, rats, or people eat the food.

2. The Numerate Filter

Hardin is clear on his approach to numerical fluency: The ability to count, weigh, and compare values in a general or specific way is essential to understanding the claims of experts or assessing any problem rationally:

The numerate temperament is one that habitually looks for approximate dimensions, ratios, proportions, and rates of change in trying to grasp what is going on in the wold. Given effective education–a rare commodity, of course–a numerate orientation is probably within the reach of most people.

[…]

Just as “literacy” is used here to mean more than merely reading and writing, so also will “numeracy” be used to mean more than measuring and counting. Examination of the origins of the sciences shows that many major discoveries were made with very little measuring and counting. The attitude science requires of its practitioners is respect, bordering on reverence, for ration, proportions, and rates of change.

Rough and ready back-of-the-envelope calculations are often sufficient to reveal the outline of a new and important scientific discovery….In truth, the essence of many of the major insights of science can be grasped with no more than child’s ability to measure, count, and calculate.

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To explain the use of the literate and numerate filters together, Hardin uses the example of the Delaney Amendment, passed in 1958 to restrict food additives. This example should be familiar to us today:

Concerned with the growing evidence that many otherwise useful substances can cause cancer, Congress degreed that henceforth, whenever a chemical at any concentration was found to cause cancer–in any fraction of any species of animal–that substance must be totally banned as an additive to human food.

From a literate standpoint, this sounds logical. The Amendment sought to eradicate harmful food additives that the free market had allowed to surface. However, says Hardin:

The Delaney Amendment is a monument to innumerate thought. “Safe” and “unsafe” are literate distinctions; nature is numerate. Everything is dangerous at some level. Even molecular oxygen, essential to human life, becomes lethal as the concentration approaches 100 percent.

[…]

Sensitivity is ordinarily expressed as “1 part per X,” where X is a large number. If a substance probably increases the incidence of cancer at a concentration of 1 part per 10,000, one should probably ban it at that concentration in food, and perhaps at 1 in 100,000. But what about 1 part per million?…In theory there is no final limit to sensitivity. What about 1 milligram per tank car? Or 1 milligram per terrestrial globe?

Obviously, some numerical limits must be applied. This is the usefulness of the numerate filter. As Charlie Munger says, “Quantify, always quantify.”

3. The Ecolacy Filter

Hardin introduces his final filter by requiring that we ask the question “And then what?”  There is perhaps no better question to prompt second-order thinking.

Even if we understand what is truly being said and have quantified the effects of a proposed policy or solution, it is imperative that we consider the second layer of effects or beyond. Hardin recognizes that this opens the door for potentially unlimited paralysis (the poorly understood and innumerate Butterfly Effect), which he boxes in by introducing his own version of the First Law of Ecology:

We can never merely do one thing.

This is to say, all proposed solutions and interventions will have a multitude of effects, and we must try our best to consider them in their totality. Most unintended consequences are just unanticipated consequences.

In proposing this filter, Hardin is very careful to guard against the Slippery Slope argument or the idea that one step in the wrong direction will lead us directly to Hell. This, he says, is a purely literate but wholly innumerate approach to thinking.

Those who take the wedge (Slippery Slope) argument with the utmost seriousness act as though they think human beings are completely devoid of practical judgment. Countless examples from everyday life show the pessimists are wrong…If we took the wedge argument seriously, we would pass a law forbidding all vehicles to travel at any speed greater than zero. That would be an easy way out of the moral problem. But we pass no such law.

In reality, the ecolate filter helps us understand the layers of unintended consequences. Take inflation:

The consequences of hyperinflation beautifully illustrate the meaning of the First Law of Ecology. A government that is unwilling or unable to stop the escalation of inflation does more than merely change the price of things; it turns loose a cascade of consequences the effects of which reach far into the future.

Prudent citizens who have saved their money in bank accounts and government bonds are ruined. In times of inflation people spend wildly with little care for value, because the choice and price of an object are less important than that one put his money into material things. Fatalism takes over as society sinks down into a culture of poverty….

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In the end, the filters must be used wisely together. They are ways to understand reality, and cannot be divorced from one another. Hardin’s general approach to thinking sums up much like his multi-disciplinary friend Munger’s:

No single filter is sufficient for reaching a reliable decision, so invidious comparisons between the three is not called for. The well-educated person uses all of them.

Check out our prior posts about Filters Against Folly:

The Map Is Not the Territory

The Map is Not the Territory

The Great Mental Models Volumes One and Two are out.
Learn more about the project here.

The map of reality is not reality. Even the best maps are imperfect. That’s because they are reductions of what they represent. If a map were to represent the territory with perfect fidelity, it would no longer be a reduction and thus would no longer be useful to us. A map can also be a snapshot of a point in time, representing something that no longer exists. This is important to keep in mind as we think through problems and make better decisions.

“The map appears to us more real than the land.”

— D.H. Lawrence

The Relationship Between Map and Territory

In 1931, in New Orleans, Louisiana, mathematician Alfred Korzybski presented a paper on mathematical semantics. To the non-technical reader, most of the paper reads like an abstruse argument on the relationship of mathematics to human language, and of both to physical reality. Important stuff certainly, but not necessarily immediately useful for the layperson.

However, in his string of arguments on the structure of language, Korzybski introduced and popularized the idea that the map is not the territory. In other words, the description of the thing is not the thing itself. The model is not reality. The abstraction is not the abstracted. This has enormous practical consequences.

In Korzybski’s words:

A.) A map may have a structure similar or dissimilar to the structure of the territory.

B.) Two similar structures have similar ‘logical’ characteristics. Thus, if in a correct map, Dresden is given as between Paris and Warsaw, a similar relation is found in the actual territory.

C.) A map is not the actual territory.

D.) An ideal map would contain the map of the map, the map of the map of the map, etc., endlessly…We may call this characteristic self-reflexiveness.

Maps are necessary, but flawed. (By maps, we mean any abstraction of reality, including descriptions, theories, models, etc.) The problem with a map is not simply that it is an abstraction; we need abstraction. A map with the scale of one mile to one mile would not have the problems that maps have, nor would it be helpful in any way.

To solve this problem, the mind creates maps of reality in order to understand it, because the only way we can process the complexity of reality is through abstraction. But frequently, we don’t understand our maps or their limits. In fact, we are so reliant on abstraction that we will frequently use an incorrect model simply because we feel any model is preferable to no model. (Reminding one of the drunk looking for his keys under the streetlight because “That’s where the light is!”)

The Map Is Not the Territory

Even the best and most useful maps suffer from limitations, and Korzybski gives us a few to explore: (A.) The map could be incorrect without us realizing it(B.) The map is, by necessity, a reduction of the actual thing, a process in which you lose certain important information; and (C.) A map needs interpretation, a process that can cause major errors. (The only way to truly solve the last would be an endless chain of maps-of-maps, which he called self-reflexiveness.)

With the aid of modern psychology, we also see another issue: the human brain takes great leaps and shortcuts in order to make sense of its surroundings. As Charlie Munger has pointed out, a good idea and the human mind act something like the sperm and the egg — after the first good idea gets in, the door closes. This makes the map-territory problem a close cousin of man-with-a-hammer tendency.

This tendency is, obviously, problematic in our effort to simplify reality. When we see a powerful model work well, we tend to over-apply it, using it in non-analogous situations. We have trouble delimiting its usefulness, which causes errors.

Let’s check out an example.

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By most accounts, Ron Johnson was one the most successful and desirable retail executives by the summer of 2011. Not only was he handpicked by Steve Jobs to build the Apple Stores, a venture which had itself come under major scrutiny – one retort printed in Bloomberg magazine: “I give them two years before they’re turning out the lights on a very painful and expensive mistake” – but he had been credited with playing a major role in turning Target from a K-Mart look-alike into the trendy-but-cheap Tar-zhey by the late 1990s and early 2000s.

Johnson’s success at Apple was not immediate, but it was undeniable. By 2011, Apple stores were by far the most productive in the world on a per-square-foot basis, and had become the envy of the retail world. Their sales figures left Tiffany’s in the dust. The gleaming glass cube on Fifth Avenue became a more popular tourist attraction than the Statue of Liberty. It was a lollapalooza, something beyond ordinary success. And Johnson had led the charge.

“(History) offers a ridiculous spectacle of a fragment expounding the whole.”

— Will Durant

With that success, in 2011 Johnson was hired by Bill Ackman, Steven Roth, and other luminaries of the financial world to turn around the dowdy old department store chain JC Penney. The situation of the department store was dour: Between 1992 and 2011, the retail market share held by department stores had declined from 57% to 31%.

Their core position was a no-brainer though. JC Penney had immensely valuable real estate, anchoring malls across the country. Johnson argued that their physical mall position was valuable if for no other reason that people often parked next to them and walked through them to get to the center of the mall. Foot traffic was a given. Because of contracts signed in the ’50s, ’60s, and ’70s, the heyday of the mall building era, rent was also cheap, another major competitive advantage. And unlike some struggling retailers, JC Penney was making (some) money. There was cash in the register to help fund a transformation.

The idea was to take the best ideas from his experience at Apple; great customer service, consistent pricing with no markdowns and markups, immaculate displays, world-class products, and apply them to the department store. Johnson planned to turn the stores into little malls-within-malls. He went as far as comparing the ever-rotating stores-within-a-store to Apple’s “apps.” Such a model would keep the store constantly fresh, and avoid the creeping staleness of retail.

Johnson pitched his idea to shareholders in a series of trendy New York City meetings reminiscent of Steve Jobs’ annual “But wait, there’s more!” product launches at Apple. He was persuasive: JC Penney’s stock price went from $26 in the summer of 2011 to $42 in early 2012 on the strength of the pitch.

The idea failed almost immediately. His new pricing model (eliminating discounting) was a flop. The coupon-hunters rebelled. Much of his new product was deemed too trendy. His new store model was wildly expensive for a middling department store chain – including operating losses purposefully endured, he’d spent several billion dollars trying to effect the physical transformation of the stores. JC Penney customers had no idea what was going on, and by 2013, Johnson was sacked. The stock price sank into the single digits, where it remains two years later.

What went wrong in the quest to build America’s Favorite Store? It turned out that Johnson was using a map of Tulsa to navigate Tuscaloosa. Apple’s products, customers, and history had far too little in common with JC Penney’s. Apple had a rabid, young, affluent fan-base before they built stores; JC Penney’s was not associated with youth or affluence. Apple had shiny products, and needed a shiny store; JC Penney was known for its affordable sweaters. Apple had never relied on discounting in the first place; JC Penney was taking away discounts given prior, triggering massive deprival super-reaction.

“All models are wrong but some are useful.”

— George Box

In other words, the old map was not very useful. Even his success at Target, which seems like a closer analogue, was misleading in the context of JC Penney. Target had made small, incremental changes over many years, to which Johnson had made a meaningful contribution. JC Penney was attempting to reinvent the concept of the department store in a year or two, leaving behind the core customer in an attempt to gain new ones. This was a much different proposition. (Another thing holding the company back was simply its base odds: Can you name a retailer of great significance that has lost its position in the world and come back?)

The main issue was not that Johnson was incompetent. He wasn’t. He wouldn’t have gotten the job if he was. He was extremely competent. But it was exactly his competence and past success that got him into trouble. He was like a great swimmer that tried to tackle a grand rapid, and the model he used successfully in the past, the map that had navigated a lot of difficult terrain, was not the map he needed anymore. He had an excellent theory about retailing that applied in some circumstances, but not in others. The terrain had changed, but the old idea stuck.

***

One person who well understands this problem of the map and the territory is Nassim Taleb, author of the Incerto series – Antifragile , The Black SwanFooled by Randomness, and The Bed of Procrustes.

Taleb has been vocal about the misuse of models for many years, but the earliest and most vivid I can recall is his firm criticism of a financial model called Value-at Risk, or VAR. The model, used in the banking community, is supposed to help manage risk by providing a maximum potential loss within a given confidence interval. In other words, it purports to allow risk managers to say that, within 95%, 99%, or 99.9% confidence, the firm will not lose more than $X million dollars in a given day. The higher the interval, the less accurate the analysis becomes. It might be possible to say that the firm has $100 million at risk at any time at a 99% confidence interval, but given the statistical properties of markets, a move to 99.9% confidence might mean the risk manager has to state the firm has $1 billion at risk. 99.99% might mean $10 billion. As rarer and rarer events are included in the distribution, the analysis gets less useful. So, by necessity, the “tails” are cut off somewhere and the analysis is deemed acceptable.

Elaborate statistical models are built to justify and use the VAR theory. On its face, it seems like a useful and powerful idea; if you know how much you can lose at any time, you can manage risk to the decimal. You can tell your board of directors and shareholders, with a straight face, that you’ve got your eye on the till.

The problem, in Nassim’s words, is that:

A model might show you some risks, but not the risks of using it. Moreover, models are built on a finite set of parameters, while reality affords us infinite sources of risks.

In order to come up with the VAR figure, the risk manager must take historical data and assume a statistical distribution in order to predict the future. For example, if we could take 100 million human beings and analyze their height and weight, we could then predict the distribution of heights and weights on a different 100 million, and there would be a microscopically small probability that we’d be wrong. That’s because we have a huge sample size and we are analyzing something with very small and predictable deviations from the average.

But finance does not follow this kind of distribution. There’s no such predictability. As Nassim has argued, the “tails” are fat in this domain, and the rarest, most unpredictable events have the largest consequences. Let’s say you deem a highly threatening event (for example, a 90% crash in the S&P 500) to have a 1 in 10,000 chance of occurring in a given year, and your historical data set only has 300 years of data. How can you accurately state the probability of that event? You would need far more data.

Thus, financial events deemed to be 5, or 6, or 7 standard deviations from the norm tend to happen with a certain regularity that nowhere near matches their supposed statistical probability.  Financial markets have no biological reality to tie them down: We can say with a useful amount of confidence that an elephant will not wake up as a monkey, but we can’t say anything with absolute confidence in an Extremistan arena.

We see several issues with VAR as a “map,” then. The first that the model is itself a severe abstraction of reality, relying on historical data to predict the future. (As all financial models must, to a certain extent.) VAR does not say “The risk of losing X dollars is Y, within a confidence of Z.” (Although risk managers treat it that way). What VAR actually says is “the risk of losing X dollars is Y, based on the given parameters.” The problem is obvious even to the non-technician: The future is a strange and foreign place that we do not understand. Deviations of the past may not be the deviations of the future. Just because municipal bonds have never traded at such-and-such a spread to U.S. Treasury bonds does not mean that they won’t in the future. They just haven’t yet. Frequently, the models are blind to this fact.

In fact, one of Nassim’s most trenchant points is that on the day before whatever “worst case” event happened in the past, you would have not been using the coming “worst case” as your worst case, because it wouldn’t have happened yet.

Here’s an easy illustration. October 19, 1987, the stock market dropped by 22.61%, or 508 points on the Dow Jones Industrial Average. In percentage terms, it was then and remains the worst one-day market drop in U.S. history. It was dubbed “Black Monday.” (Financial writers sometimes lack creativity — there are several other “Black Monday’s” in history.) But here we see Nassim’s point: On October 18, 1987, what would the models use as the worst possible case? We don’t know exactly, but we do know the previous worst case was 12.82%, which happened on October 28, 1929. A 22.61% drop would have been considered so many standard deviations from the average as to be near impossible.

But the tails are very fat in finance — improbable and consequential events seem to happen far more often than they should based on naive statistics. There is also a severe but often unrecognized recursiveness problem, which is that the models themselves influence the outcome they are trying to predict. (To understand this more fully, check out our post on Complex Adaptive Systems.)

A second problem with VAR is that even if we had a vastly more robust dataset, a statistical “confidence interval” does not do the job of financial risk management. Says Taleb:

There is an internal contradiction between measuring risk (i.e. standard deviation) and using a tool [VAR] with a higher standard error than that of the measure itself.

I find that those professional risk managers whom I heard recommend a “guarded” use of the VAR on grounds that it “generally works” or “it works on average” do not share my definition of risk management. The risk management objective function is survival, not profits and losses. A trader according to the Chicago legend, “made 8 million in eight years and lost 80 million in eight minutes”. According to the same standards, he would be, “in general”, and “on average” a good risk manager.

This is like a GPS system that shows you where you are at all times but doesn’t include cliffs. You’d be perfectly happy with your GPS until you drove off a mountain.

It was this type of naive trust of models that got a lot of people in trouble in the recent mortgage crisis. Backward-looking, trend-fitting models, the most common maps of the financial territory, failed by describing a territory that was only a mirage: A world where home prices only went up. (Lewis Carroll would have approved.)

This was navigating Tulsa with a map of Tatooine.

***

The logical response to all this is, “So what?” If our maps fail us, how do we operate in an uncertain world? This is its own discussion for another time, and Taleb has gone to great pains to try and address the concern. Smart minds disagree on the solution. But one obvious key must be building systems that are robust to model error.

The practical problem with a model like VAR is that the banks use it to optimize. In other words, they take on as much exposure as the model deems OK. And when banks veer into managing to a highly detailed, highly confident model rather than to informed common sense, which happens frequently, they tend to build up hidden risks that will un-hide themselves in time.

If one were to instead assume that there were no precisely accurate maps of the financial territory, they would have to fall back on much simpler heuristics. (If you assume detailed statistical models of the future will fail you, you don’t use them.)

In short, you would do what Warren Buffett has done with Berkshire Hathaway. Mr. Buffett, to our knowledge, has never used a computer model in his life, yet manages an institution half a trillion dollars in size by assets, a large portion of which are financial assets. How?

The approach requires not only assuming a future worst case far more severe than the past, but also dictates building an institution with a robust set of backup systems, and margins-of-safety operating at multiple levels. Extra cash, rather than extra leverage. Taking great pains to make sure the tails can’t kill you. Instead of optimizing to a model, accepting the limits of your clairvoyance.

When map and terrain differ, follow the terrain.

The trade-off, of course, is short-run rewards much less great than those available under more optimized models. Speaking of this, Charlie Munger has noted:

Berkshire’s past record has been almost ridiculous. If Berkshire had used even half the leverage of, say, Rupert Murdoch, it would be five times its current size.

For Berkshire at least, the trade-off seems to have been worth it.

***

The salient point then is that in our march to simplify reality with useful models, of which Farnam Street is an advocate, we confuse the models with reality. For many people, the model creates its own reality. It is as if the spreadsheet comes to life. We forget that reality is a lot messier. The map isn’t the territory. The theory isn’t what it describes, it’s simply a way we choose to interpret a certain set of information. Maps can also be wrong, but even if they are essentially correct, they are an abstraction, and abstraction means that information is lost to save space. (Recall the mile-to-mile scale map.)

How do we do better? This is fodder for another post, but the first step is to realize that you do not understand a model, map, or reduction unless you understand and respect its limitations. We must always be vigilant by stepping back to understand the context in which a map is useful, and where the cliffs might lie. Until we do that, we are the turkey.

The Mind’s Search Algorithm: Sorting Mental Models

Mental models are tools for the mind.

In his talk: Academic Economics: Strengths and Weaknesses, after Considering Interdisciplinary Needs, at the University of California at Santa Barbara, in 2003, Charlie Munger honed in on why we like to specialize.

The big general objection to economics was the one early described by Alfred North Whitehead when he spoke of the fatal unconnectedness of academic disciplines, wherein each professor didn’t even know of the models of the other disciplines, much less try to synthesize those disciplines with his own … The nature of this failure is that it creates what I always call ‘man with a hammer’ syndrome. To a man with only a hammer, every problem looks pretty much like a nail. And that works marvellously to gum up all professions, and all departments of academia, and indeed most practical life. So, what do we do, Charlie? The only antidote for being an absolute klutz due to the presence of a man with a hammer syndrome is to have a full kit of tools. You don’t have just a hammer. You’ve got all the tools.

The more models you have from outside your discipline and the more you iterate through them when faced with a challenge in a checklist sort of fashion, the better you’ll be able to solve problems.

Models are additive. Like LEGO. The more you have the more things you can build, the more connections you can make between them and the more likely you are to be able to determine the relevant variables that govern the situation.

And when you learn these models you need to ask yourself under what conditions will this tool fail? That way you’re not only looking for situations where the tool is useful but also situations where something interesting is happening that might warrant further attention.

The Mind’s Search Engine

In Diaminds: Decoding the Mental Habits of Successful Thinkers, Roger Martin looks at our mental search engine.

Now for the final step in the design of the mentally choiceful stance: the search engine, as in ‘How did I solve these problems?’ ‘Obviously,’ you will answer yourself, ‘I was using a simple search engine in my mind to go through checklist style, and I was using some rough algorithms that work pretty well in many complex systems.’ What does a search engine do? It searches. And how do you organize an efficient search? Well, algorithm designers tell us you have to have an efficient organization of the contents of whatever it is you are searching. And a tree structure allows you to search more efficiently than most alternative structures.

How a tree structure helps simplify search: A detection algorithm for ‘Fox.’
How a tree structure helps simplify search: A detection algorithm for ‘Fox.’

So what’s Munger’s search algorithm?

(from an interview with Munger via Diaminds: Decoding the Mental Habits of Successful Thinkers:)

Extreme success is likely to be caused by some combination of the following factors: a) Extreme maximization or minimization of one or two variables. Example[:] Costco, or, [Berkshire Hathaway’s] furniture and appliance store. b) Adding success factors so that a bigger combination drives success, often in nonlinear fashion, as one is reminded of the concept of breakpoint or the concept of critical mass in physics. You get more mass, and you get a lollapalooza result. And of course I’ve been searching for lollapalooza results all my life, so I’m very interested in models that explain their occurrence. [Remember the Black Swan?] c) an extreme of good performance over many factors. Examples: Toyota or Les Schwab. d) Catching and riding some big wave.

Charlie Munger’s lollapalooza detection algorithm, represented as a tree search.
Charlie Munger’s lollapalooza detection algorithm represented as a tree search.

(via Diaminds: Decoding the Mental Habits of Successful Thinkers)

A good search algorithm allows you to make your mental choices clear. It makes it easier for you to be mentally choiceful and to understand the reasons why you’re making these mental choices.

Now, what should go on the branches of your tree of mental models? Well, how about basic mental models from a whole bunch of different disciplines? Such as: physics (non-linearity, criticality), economics (what Munger calls the ‘super-power’ of incentives), the multiplicative effects of several interacting causes (biophysics), and collective phenomena – or ‘catching the wave’ (plasma physics). How’s that for a science that rocks, by placing at the disposal of the mind a large library of forms created by thinkers across hundreds of years and marshalling them for the purpose of detecting, building, and profiting from Black Swans?

The ‘tree trick’ has one more advantage – a big one: it lets you quickly visualize interactions among the various models and identify cumulative effects. Go northwest in your search, starting from the ’0’ node, and the interactions double with every step. Go southwest, on the other hand, and the interactions decrease in number at the same rate. Seen in this rather sketchy way, Black Swan hunting is no longer as daunting a sport as it might seem at first sight.

Developing a Mental Framework for Effective Thinking

Becoming a better thinker means understanding the way you think and developing a way of approaching problems that allows you to see things from multiple lenses. These lenses, or mental models, are built on the foundations of physics, biology, math, psychology, as well as history and economics. The more lenses you have, the more you can see. The more you can see the more you can understand. The more you understand reality the more you will know what to do.

These tools also allow you to better understand when to follow and when to reject conventional wisdom. Ideally, you want to go through them checklist-style — just run right through them — asking what applies.

***

John Snow was a doctor based in London during the acute cholera outbreak of the summer of 1854. He represents a powerful example of the impact a lollapalooza effect can have. A lollapalooza is when several ideas combine to produce an unusually powerful result. Snow developed systems to ease the pain of surgery with ether and chloroform.

In the book The Ghost Map, author Steven Johnson explains:

Snow was a truly consilient thinker, in the sense of the term as it was originally formulated by the Cambridge philosopher William Whewell in the 1840s (and recently popularized by Harvard biologist E. O. Wilson). “The Consilience of Inductions,” Whewell wrote, “takes place when an Induction, obtained from one class of facts, coincides with an Induction obtained from another different class. This Consilience is a test of the truth of the Theory in which it occurs.” Snow’s work was constantly building bridges between different disciplines, some which barely existed as functional sciences in his day, using data on one scale of investigation to make predictions about behavior on other scales. In studying ether and chloroform, he had moved from the molecular properties of the gas itself, to its circulation of those properties throughout the body’s overall system, to the psychological effects produced by these biological changes. He even ventured beyond the natural world into the design of technology that would best reflect our understanding of the anesthetics. Snow was not interested in individual, isolated phenomena; he was interested in chains and networks in the movement from scale to scale. His mind tripped happily from molecules to cells to brains to machines, and it was precisely that consilient study that helped Snow uncover so much about this nascent field in such a shockingly short amount of time.

Suspending belief in the common theory at the time on how diseases were spread, Snow ended up rejecting miasma theory, which said the disease was spread via “bad air.” He did this through science. He conducted interviews with residents and traced the majority of cases back to a single water source. His willingness to challenge conventional thinking, along with approaching the problem through multiple lenses, resulted in finding the deadly source and changes in municipal water systems from that day forward.

***

Elements of the mental framework for Thinking

Charlie Munger is a strong advocate of a mental framework. In Damn Right: Behind the Scenes with Berkshire Hathaway Billionaire Charlie Munger, he offered five-simple notions that help solve complex problems.

In The Focused Few: Taking a Multidisciplinary Approach to Focus Investing, Richard Rockwood explores the concepts from many disciplines. Adding them together can yield a useful mental checklist.

Element 1: Invert

In The Focused Few, Rockwood writes:

Inverting, or thinking problems through backward, is a great way to understand information. Charlie Munger provides the best illustration I have ever seen of this type of thinking.

During a speech he offered an example of how a situation could be examined using the inversion process. He discussed the development process of Coca-Cola from the perspective of a person creating a soda company from scratch and examining the key issues that would need to be resolved to make it a reality.

He listed some of the issues the entrepreneur would need to address:

  • What kind of properties should the new drink strive for, and what are those it should avoid? One property the drink should not have is an aftertaste. Consumers should be able to consume large quantities over a period of time and not be deterred by an unpleasant aftertaste.
  • The soda should be developed in such a manner that it can be shipped in large quantities at minimal costs. This makes it easier to develop an efficient, large-scale distribution system.
  • Keeping the soda formulation a secret will help alleviate competition and create a certain aura of mystique around the product.
  • The company also can deter competition by expanding the business as quickly as possible. For example, the distribution system could be expanded until it reaches a critical mass that competitors would find hard to duplicate without massive capital expenditures.

(Read more about inversion)

Element 2: First-and second-level thinking

In The Focused Few, Rockwood writes:

Let’s examine the decision-making process by breaking it down into two components. The first component, first-level thinking, generally occurs when you make decisions quickly based on a simple theme or common sense. For example, a person may decide to invest in a company simply because its products are trendy. Making decisions based on first-level reasoning has significant problems, however. Common sense “is wonderful at making sense of the world, but not necessarily at understanding it.”

The danger is that you may think you understand a particular situation when in fact you have only developed a likely story.

Second-level thinkers, in contrast, approach decisions differently. What kinds of questions should a second-level thinker ask?

In his book, The Most Important Thing: Uncommon Sense for the Thoughtful Investor, Howard Marks provides a useful list of questions to ask.

  1. What is the range of likely future outcomes?
  2. Which outcome do I think will occur?
  3. What is the probability that I’m right?
  4. What is the prevailing consensus?
  5. How does my expectation differ from the consensus?
  6. How does the current price for the asset comport with the consensus view of the future— and with mine?
  7. Is the consensus psychology that is incorporated into the price too bullish or bearish?
  8. What will happen to the asset’s price if the consensus turns out to be right, and what if I’m right?

(Read more about second-level thinking)

Element 3: Use decision trees

decision trees

In The Focused Few, Rockwood writes:

Decision trees are excellent tools for helping you decide on a course of action. They enable you to lay out several possible scenarios, investigate their possible outcomes, and create a balanced picture of the risks and rewards associated with each.

[…]

Let’s examine the decision-tree process in greater detail. First, identify the decision and the outcome alternatives available at each point. After you lay out each course of action, determine which option has the greatest value to you. Start by assigning a cash value to each possible outcome (i.e., what the expected value would be if that particular outcome were to occur). Next, look at each break, or point of uncertainty, in the tree and estimate the probability of each outcome occurring. If you use percentages, the combined total must equal 100% at each break point. If you use fractions, these must add up to 1.

After these two steps have been taken (i.e., the values of the outcomes have been entered and the probabilities have been estimated), it is time to begin calculating the expected values of the various branches in the decision tree.

Element 4: The multidisciplinary approach

When trying to resolve a difficult situation or determining exactly why a product has been, and may continue to be, successful, it helps to think about the problem by creating a checklist that incorporates the vital components of other disciplines.

(read more about multidisciplinary thinking)

The Focused Few is a tune up for your mind.