# Tag: Nassim Taleb

## The Map Is Not the Territory

“(History) offers a ridiculous spectacle of a fragment expounding the whole.”
— Will Durant in Our Oriental Heritage

“All models are wrong but some are useful.”
— George Box

***

## The Relationship Between Map and Territory

“That’s another thing we’ve learned from your Nation,” said Mein Herr, “map-making. But we’ve carried it much further than you. What do you consider the largest map that would be really useful?”

“About six inches to the mile.”

“Only six inches!” exclaimed Mein Herr. “We very soon got to six yards to the mile. Then we tried a hundred yards to the mile. And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!”

“Have you used it much?” I enquired.

“It has never been spread out, yet,” said Mein Herr: “the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.
Sylvie and Bruno Concluded

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.

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. Lewis Carroll made that clear by having Mein Herr describe a map with the scale of one mile to one mile. Such a map would not have the problems that maps have, nor would it be helpful in any way.

(See Borges for another take.)

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!”)

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.

***

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.

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.

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 analyse 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 analysing 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.

## How Warren Buffett Keeps up with a Torrent of Information

A telling excerpt from an interview of Warren Buffett (below) on the value of reading.

Seems like he's taking the opposite approach to Nassim Taleb in some ways.

Interviewer: How do you keep up with all the media and information that goes on in our crazy world and in your world of Berkshire Hathaway? What's your media routine?

Interviewer: You process information very quickly.

Warren Buffett: I have filters in my mind. If somebody calls me about an investment in a business or an investment in securities, I usually know in two or three minutes whether I have an interest. I don't waste any time with the ones which I don't have an interest.

I always worry a little bit about even appearing rude because I can tell very, very, very quickly whether it's going to be something that will lead to something, or whether it's a half an hour or an hour or two hours of chatter.

What's interesting about these filters is that Buffett has consciously developed them as heuristics to allow for rapid processing. They allow him to move quickly with few mistakes — that's what heuristics are designed to do. Most of us are trying to get rid of our heuristics to reduce error but here is one of the smartest people alive and he's doing the opposite: he's creating these filters as a means for allowing for information processing. He's moving fast and in the right direction.

## Nassim Taleb: How to Not be a Sucker From the Past

### “History is useful for the thrill of knowing the past, and for the narrative (indeed), provided it remains a harmless narrative.”

— Nassim Taleb

The fact that new information exists about the past in general means that we have an incomplete roadmap about history. There is a necessary fallibility … if you will.

In The Black Swan, Nassim Taleb writes:

History is useful for the thrill of knowing the past, and for the narrative (indeed), provided it remains a harmless narrative. One should learn under severe caution. History is certainly not a place to theorize or derive general knowledge, nor is it meant to help in the future, without some caution. We can get negative confirmation from history, which is invaluable, but we get plenty of illusions of knowledge along with it.

While I don't entirely hold Taleb's view, I think it's worth reflecting on. As a friend put it to me recently, “when people are looking into the rearview mirror of the past, they can take facts and like a string of pearls draw lines of causal relationships that facilitate their argument while ignoring disconfirming facts that detract from their central argument or point of view.”

Taleb advises us to adopt the empirical skeptic approach of Menodotus which was to “know history without theorizing from it,” and to not draw any large theoretical or scientific claims.

We can learn from history but our desire for causality can easily lead us down a dangerous rabbit hole when new facts come to light disavowing what we held to be true. In trying to reduce the cognitive dissonance, our confirmation bias leads us to reinterpret past events in a way that fits our current beliefs.

History is not stagnant — we only know what we know currently and what we do know is subject to change. The accepted beliefs about how events played out may change in light of new information and then the newly accepted beliefs may change over time as well.

## The Lucretius Problem

The Lucretius Problem is a mental defect where we assume the worst case event that has happened is the worst case event that can happen. We fail to understand that the worst event that has happened in the past surpassed the worst event that came before it. Only the fool believes all he can see is all there is to see.

It's always good to re-read books and to dip back into them periodically. When reading a new book, I often miss out on crucial information (especially books that are hard to categorize with one descriptive sentence). When you come back to a book after reading hundreds of others you can't help but make new connections with the old book and see it anew. The book hasn't changed but you have.

It has been a while since I read Anti-fragile. In the past, I've talked about an Antifragile Way of Life, Learning to Love Volatility, the Definition of Antifragility, and the Noise and the Signal.

But upon re-reading Antifragile I came across the Lucretius Problem and I thought I'd share an excerpt. (Titus Lucretius Carus was a Roman poet and philosopher, best-known for his poem On the Nature of Things).

In Antifragile, Nassim Taleb writes:

Indeed, our bodies discover probabilities in a very sophisticated manner and assess risks much better than our intellects do. To take one example, risk management professionals look in the past for information on the so-called worst-case scenario and use it to estimate future risks – this method is called “stress testing.” They take the worst historical recession, the worst war, the worst historical move in interest rates, or the worst point in unemployment as an exact estimate for the worst future outcome​. But they never notice the following inconsistency: this so-called worst-case event, when it happened, exceeded the worst [known] case at the time.

I have called this mental defect the Lucretius problem, after the Latin poetic philosopher who wrote that the fool believes that the tallest mountain in the world will be equal to the tallest one he has observed. We consider the biggest object of any kind that we have seen in our lives or hear about as the largest item that can possibly exist. And we have been doing this for millennia.

Taleb brings up an interesting point, which is that our documented history can blind us. All we know is what we have been able to record. There is an uncertainty that we don't seem to grasp.

We think because we have sophisticated data collecting techniques that we can capture all the data necessary to make decisions. We think we can use our current statistical techniques to draw historical trends using historical data without acknowledging the fact that past data recorders had fewer tools to capture the dark figure of unreported data. We also overestimate the validity of what has been recorded before and thus the trends we draw might tell a different story if we had the dark figure of unreported data.

Taleb continues:

The same can be seen in the Fukushima nuclear reactor, which experienced a catastrophic failure in 2011 when a tsunami struck. It had been built to withstand the worst past historical earthquake, with the builders not imagining much worse— and not thinking that the worst past event had to be a surprise, as it had no precedent. Likewise, the former chairman of the Federal Reserve, Fragilista Doctor Alan Greenspan, in his apology to Congress offered the classic “It never happened before.” Well, nature, unlike Fragilista Greenspan, prepares for what has not happened before, assuming worse harm is possible.

## Dealing with Uncertainty

Taleb provides an answer which is to develop layers of redundancy, that is a margin of safety, to act as a buffer against oneself. We overvalue what we have recorded and assume it tells us the worst and best possible outcomes. Redundant layers are a buffer against our tendency to think what has been recorded is a map of the whole terrain. An example of a redundant feature could be a rainy day fund which acts as an insurance policy against something catastrophic such as a job loss that allows you to survive and fight another day.

Antifragile is a great book to read and you might learn something about yourself and the world you live in by reading it or in my case re-reading it.

Nassim Taleb: The Definition of a Black Swan

## Fooled By Randomness: My Notes

I loved Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Taleb. This is the first popular book he wrote, the book that helped propel him into an intellectual celebrity. Interestingly, Fooled by Randomness contains semi-explored gems of the ideas that would later go on to become the best-selling books The Black Swan and Antifragile.

Here are some of my notes from the book.

## Hindsight Bias

Part of the argument that Fooled by Randomness presents is that when we look back at things that have happened we see them as less random than they actually were.

It is as if there were two planets: the one in which we actually live and the one, considerably more deterministic, on which people are convinced we live. It is as simple as that: Past events will always look less random than they were (it is called the hindsight bias). I would listen to someone’s discussion of his own past realizing that much of what he was saying was just backfit explanations concocted ex post by his deluded mind.

## The Courage of Montaigne

Writing on Montaigne as the role model for the modern thinker, Taleb also addresses his courage:

It certainly takes bravery to remain skeptical; it takes inordinate courage to introspect, to confront oneself, to accept one’s limitations— scientists are seeing more and more evidence that we are specifically designed by mother nature to fool ourselves.

## Probability

Fooled by Randomness is about probability, not in a mathematical way but as skepticism.

In this book probability is principally a branch of applied skepticism, not an engineering discipline. …

Probability is not a mere computation of odds on the dice or more complicated variants; it is the acceptance of the lack of certainty in our knowledge and the development of methods for dealing with our ignorance. Outside of textbooks and casinos, probability almost never presents itself as a mathematical problem or a brain teaser. Mother nature does not tell you how many holes there are on the roulette table , nor does she deliver problems in a textbook way (in the real world one has to guess the problem more than the solution).

Outside of textbooks and casinos, probability almost never presents itself as a mathematical problem” which is fascinating given how we tend to solve problems. In decisions under uncertainty, I discussed how risk and uncertainty are different things, which creates two types of ignorance.

Most decisions are not risk-based, they are uncertainty-based and you either know you are ignorant or you have no idea you are ignorant. There is a big distinction between the two. Trust me, you'd rather know you are ignorant.

## Randomness Disguised as Non-Randomness

The core of the book is about luck that we understand as skill or “randomness disguised as non-randomness (that is determinism).”

This problem manifests itself most frequently in the lucky fool, “defined as a person who benefited from a disproportionate share of luck but attributes his success to some other, generally very precise, reason.”

Such confusion crops up in the most unexpected areas, even science, though not in such an accentuated and obvious manner as it does in the world of business. It is endemic in politics, as it can be encountered in the shape of a country’s president discoursing on the jobs that “he” created, “his” recovery, and “his predecessor’s” inflation.

These lucky fools are often fragilistas — they have no idea they are lucky fools. For example:

[W]e often have the mistaken impression that a strategy is an excellent strategy, or an entrepreneur a person endowed with “vision,” or a trader a talented trader, only to realize that 99.9% of their past performance is attributable to chance, and chance alone. Ask a profitable investor to explain the reasons for his success; he will offer some deep and convincing interpretation of the results. Frequently, these delusions are intentional and deserve to bear the name “charlatanism.”

This does not mean that all success is luck or randomness. There is a difference between “it is more random than we think” and “it is all random.”

Let me make it clear here : Of course chance favors the prepared! Hard work, showing up on time, wearing a clean (preferably white) shirt, using deodorant, and some such conventional things contribute to success— they are certainly necessary but may be insufficient as they do not cause success. The same applies to the conventional values of persistence, doggedness and perseverance: necessary, very necessary. One needs to go out and buy a lottery ticket in order to win. Does it mean that the work involved in the trip to the store caused the winning? Of course skills count, but they do count less in highly random environments than they do in dentistry.

No, I am not saying that what your grandmother told you about the value of work ethics is wrong! Furthermore, as most successes are caused by very few “windows of opportunity,” failing to grab one can be deadly for one’s career. Take your luck!

That last paragraph connects to something Charlie Munger once said: “Really good investment opportunities aren't going to come along too often and won't last too long, so you've got to be ready to act. Have a prepared mind.

Taleb thinks of success in terms of degrees, so mild success might be explained by skill and labor but outrageous success “is attributable variance.”

## Luck Makes You Fragile

One thing Taleb hits on that really stuck with me is that “that which came with the help of luck could be taken away by luck (and often rapidly and unexpectedly at that). The flipside, which deserves to be considered as well (in fact it is even more of our concern), is that things that come with little help from luck are more resistant to randomness.” How Antifragile.

Taleb argues this is the problem of induction, “it does not matter how frequently something succeeds if failure is too costly to bear.”

## Noise and Signal

We are confused between noise and signal.

…the literary mind can be intentionally prone to the confusion between noise and meaning, that is, between a randomly constructed arrangement and a precisely intended message. However, this causes little harm; few claim that art is a tool of investigation of the Truth— rather than an attempt to escape it or make it more palatable. Symbolism is the child of our inability and unwillingness to accept randomness; we give meaning to all manner of shapes; we detect human figures in inkblots.

All my life I have suffered the conflict between my love of literature and poetry and my profound allergy to most teachers of literature and “critics.” The French thinker and poet Paul Valery was surprised to listen to a commentary of his poems that found meanings that had until then escaped him (of course, it was pointed out to him that these were intended by his subconscious).

If we're concerned about situations where randomness is confused with non-randomness should we also be concerned with situations where non-randomness is mistaken for randomness, which would result in the signal being ignored?

First, I am not overly worried about the existence of undetected patterns. We have been reading lengthy and complex messages in just about any manifestation of nature that presents jaggedness (such as the palm of a hand, the residues at the bottom of Turkish coffee cups, etc.). Armed with home supercomputers and chained processors, and helped by complexity and “chaos” theories, the scientists, semiscientists, and pseudoscientists will be able to find portents. Second, we need to take into account the costs of mistakes; in my opinion, mistaking the right column for the left one is not as costly as an error in the opposite direction. Even popular opinion warns that bad information is worse than no information at all.

If you haven't yet, pick up a copy of Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Don't make the same mistake I did and wait to read this important book.

## 12 Books Every Investor Should Read

If you’re looking for something to read that will improve your ability as an investor, I’d recommend any of the books below. All 12 of them are deeply informative and will leave an impact on you.

1. The Intelligent Investor by Benjamin Graham
Described as “by far the best book on investing ever written” by none other than Warren Buffett. “Chapters 8 and 20 have been the bedrock of my investing activities for more than 60 years,” he says. “I suggest that all investors read those chapters and reread them every time the market has been especially strong or weak.”

2. The Little Book that Beats the Market by Joel Greenblatt
As Buffett says, investing is simple but not easy. This book focuses on the simplicity of investing. Greenblatt, who has average annualized returns of about 40% for over 20 years, explains investing using 6th grade math and plain language. Putting it into practice is another story.

3. Fooled by Randomness by Nassim Taleb
The core of Taleb’s other books — The Black Swan and Antifragile — can be found in this early work. One of the best parts, for me, was the notion of alternative histories. “Mother Nature,” he writes, “does not tell you how many holes there are on the roulette table.” This book teaches you how to look at the world probabilistically. After you start doing that, nothing is ever the same again.

4. The Most Important Thing by Howard Marks
“This is a rarity,” Buffett writes of Howard Marks' book, “a useful book.” More than teaching you the keys to successful investment, it will teach you about critical thinking.

5. Poor Charlie's Almanack by Charlie Munger
Charlie Munger is perhaps the smartest man I don’t know. This book is a curated collection of his speeches and talks that can’t help but leave you smarter. Munger’s wit and wisdom come across on every page. This book will improve your thinking and decisions. It will also shine light upon psychological forces that make you a one-legged man in an ass-kicking contest. Read and re-read.

6. Common Stocks and Uncommon Profits by Philip Fisher
Buffett used to say that he was 85% Benjamin Graham and 15% Phil Fisher. That was a long time ago, the Buffett of today resembles more Fisher than Graham. Maybe there is something to buying and holding great companies.

7. The Dao of Capital by Mark Spitznagel
Spitznagel presents the methodology of Austrian Investing, where one looks for positional advantage. Nassim Taleb, commenting on the book wrote: “At last, a real book by a real risk-taking practitioner. You cannot afford not to read this!”

8. Buffett: The Making of an American Capitalist by Roger Lowenstein
This book, perhaps more than any other, has changed the lives of many of my friends and investors because this is how many of them first discovered Warren Buffett and value investing.

9. The Outsiders: Eight Unconventional CEOs and Their Radically Rational Blueprint for Success
An outstanding book detailing eight extraordinary CEOs and the unconventional methods they used for capital allocation. One of them, Henry Singleton, had a unique view on strategic planning.

10. The Misbehavior of Markets: A Fractal View of Financial Turbulence by Benoit Mandelbrot
A critique of modern finance theories, which usually gets built on the underlying assumption that distributions are normal. Nassim Taleb calls this “the most realistic finance book ever published.”

11. Why Stocks Go Up (and Down) by William Pike
This is a basics book on the fundamentals of equity and bond investing – financial statements, cash flows, etc. A good place to start on the nuts and bolts. If you’re looking to learn accounting also check out The Accounting Game: Basic Accounting Fresh from the Lemonade Stand, I’m serious. This is the book I recommended to classmates in business school with no accounting background to get them up to speed quickly.

12. Bull: A History of the Boom and Bust, 1982-2004 by Maggie Mahar
The first and perhaps best book written on the market’s historic run, which started in 1982 and ended in the early 2000s. Mahar reminds readers that euphoria and blindness are a regular part of bull markets – lessons we should have learned from studying history.

Keep in mind that if investing were as easy as buying a book and reading it, we’d all be rich.