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Category Archives: Science

Principles for an Age of Acceleration

MIT Media Lab is a creative nerve center where great ideas like One Laptop per Child, LEGO Mindstorms, and Scratch programming language have emerged.

Its director, Joi Ito, has done a lot of thinking about how prevailing systems of thought will not be the ones to see us through the coming decades. In his book Whiplash: How to Survive our Faster Future, he notes that sometime late in the last century, technology began to outpace our ability to understand it.

We are blessed (or cursed) to live in interesting times, where high school students regularly use gene editing techniques to invent new life forms, and where advancements in artificial intelligence force policymakers to contemplate widespread, permanent unemployment. Small wonder our old habits of mind—forged in an era of coal, steel, and easy prosperity—fall short. The strong no longer necessarily survive; not all risk needs to be mitigated; and the firm is no longer the optimum organizational unit for our scarce resources.

Ito’s ideas are not specific to our moment in history, but adaptive responses to a world with certain characteristics:

1. Asymmetry
In our era, effects are no longer proportional to the size of their source. The biggest change-makers of the future are the small players: “start-ups and rogues, breakaways and indie labs.”

2. Complexity
The level of complexity is shaped by four inputs, all of which are extraordinarily high in today’s world: heterogeneity, interconnection, interdependency and adaptation.

3. Uncertainty
Not knowing is okay. In fact, we’ve entered an age where the admission of ignorance offers strategic advantages over expending resources–subcommittees and think tanks and sales forecasts—toward the increasingly futile goal of forecasting future events.”

When these three conditions are in place, certain guiding principles serve us best. In his book, Ito shares some of the maxims that organize his “anti-disciplinary” Media Lab in a complex and uncertain world.

Emergence over Authority

Complex systems show properties that their individual parts don’t possess, and we call this process “emergence”. For example, life is an emergent property of chemistry. Groups of people also produce a wondrous variety of emergent behaviors—languages, economies, scientific revolutions—when each intellect contributes to a whole that is beyond the abilities of any one person.

Some organizational structures encourage this kind of creativity more than others. Authoritarian systems only allow for incremental changes, whereas nonlinear innovation emerges from decentralized networks with a low barrier to entry. As Stephen Johnson describes in Emergence, when you plug more minds into the system, “isolated hunches and private obsessions coalesce into a new way of looking at the world, shared by thousands of individuals.”

Synthetic biology best exemplifies the type of new field that can arise from emergence. Not to be confused with genetic engineering, which modifies existing organisms, synthetic biology aims to create entirely new forms of life.

Having emerged in the era of open-source software, synthetic biology is becoming an exercise in radical collaboration between students, professors, and a legion of citizen scientists who call themselves biohackers. Emergence has made its way into the lab.

As a result, the cost of sequencing DNA is plummeting at six times the rate of Moore’s Law, and a large Registry of Standard Biological Parts, or BioBricks, now offers genetic components that perform well-understood functions in whatever organism is being created, like a block of Lego.

There is still a place for leaders in an organization that fosters emergence, but the role may feel unfamiliar to a manager from a traditional hierarchy. The new leader spends less time leading and more time “gardening”—pruning the hedges, watering the flowers, and otherwise getting out of the way. (As biologist Lewis Thomas puts it, a great leader must get the air right.)

Pull over Push

“Push” strategies involve directing resources from a central source to sites where, in the leader’s estimation, they are likely to be needed or useful. In contrast, projects that use “pull” strategies attract intellectual, financial and physical resources to themselves just as they are needed, rather than stockpiling them.

Ito is a proponent of the sharing economy, through which a startup might tap into the global community of freelancers and volunteers for a custom-made task force instead of hiring permanent teams of designers, programmers or engineers.

Here’s a great example:

When the Fukushima nuclear meltdown happened, Ito was living just outside of Tokyo. The Japanese government took a command-and-control (“push”) approach to the disaster, in which information would slowly climb up the hierarchy, and decisions would then be passed down stepwise to the ground-level workers.

It soon became clear that the government was not equipped to assess or communicate the radioactivity levels of each neighborhood, so Ito and his friends took the problem into their own hands. Pulling in expertise and money from far-flung scientists and entrepreneurs, they formed a citizen science group called Safecast, which built its own GPS-equipped Geiger counters and strapped them to cars for faster monitoring. They launched a website that continues to share data – more than 50 million data points so far – about local environments.

To benefit from these kinds of “pull” strategies, it pays to foster an environment that is rich with weak ties – a wide network of acquaintances from which to draw just-in-time knowledge and resources, as Ito did with Safecast.

Compasses over Maps

Detailed maps can be more misleading than useful in a fast-changing world, where a compass is the tool of choice. In the same way, organizations that plan exhaustively will be outpaced in an accelerating world by ones that are guided by a more encompassing mission.

A map implies a straightforward knowledge of the terrain, and the existence of an optimum route; the compass is a far more flexible tool and requires the user to employ creativity and autonomy in discovering his or her own path.

One advantage to the compass approach is that when a roadblock inevitably crops up, there is no need to go back to the beginning to form another plan or draw up multiple plans for each contingency. You simply navigate around the obstacle and continue in your chosen direction.

It is impossible, in any case, to make detailed plans for a complex and creative organization. The way to set a compass direction for a company is by creating a culture—or set of mythologies—that animates the parts in a common worldview.

In the case of the MIT Media Lab, that compass heading is described in three values: “Uniqueness, Impact, and Magic”. Uniqueness means that if someone is working on a similar project elsewhere, the lab moves on.

Rather than working to discover knowledge for its own sake, the lab works in the service of Impact, through start-ups and physical creations. It was expressed in the lab’s motto “Deploy or die”, but Barack Obama suggested they work on their messaging, and Ito shortened it to “Deploy.”

The Magic element, though hard to define, speaks to the delight that playful originality so often awakens.

Both students and faculty at the lab are there to learn, but not necessarily to be “educated”. Learning is something you pursue for yourself, after all, whereas education is something that’s done to you. The result is “agile, scrappy, permissionless innovation”.

The new job landscape requires more creativity from everybody. The people who will be most successful in this environment will be the ones who ask questions, trust their instincts, and refuse to follow the rules when the rules get in their way.

Other principles discussed in Whiplash include Risk over Safety, Disobedience over Compliance, Practice over Theory, Diversity over Ability, Resilience over Strength, and Systems over Objects.

The Founder Principle: A Wonderful Idea from Biology

We’ve all been taught natural selection; the mechanism by which species evolve through differential reproductive success. Most of us are familiar with the idea that random mutations in DNA cause variances in offspring, some of which survive more frequently than others. However, this is only part of the story.

Sometimes other situations cause massive changes in species populations, and they’re often more nuanced and tough to spot.

One such concept comes from one of the most influential biologists in history, Ernst Mayr. He called it The Founder Principle, a mechanism by which new species are created by a splintered population; often with lower genetic diversity and an increased risk of extinction.

In the brilliant The Song of the Dodo: Island Biography in an Age of ExtinctionDavid Quammen gives us not only the stories of many brilliant biological naturalists including Mayr, but we also get a deep dive into the core concepts of evolution and extinction, including the Founder Effect.

Quammen begins by t outtlining the basic idea:

When a new population is founded in an isolated place, the founders usually constitute a numerically tiny group – a handful of lonely pioneers, or just a pair, or maybe no more than one pregnant female. Descending from such a small number of founders, the new population will carry only a minuscule and to some extent random sample of the gene pool of the base population. The sample will most likely be unrepresentative, encompassing less genetic diversity than the larger pool. This effect shows itself whenever a small sample is taken from a large aggregation of diversity; whether the aggregation consists of genes, colored gum balls, M&M’s, the cards of a deck, or any other collection of varied items, a small sample will usually contain less diversity than the whole.

Why does the Founder Effect happen? It’s basically applied probability. Perhaps an example will help illuminate the concept.

Think of yourself playing a game of poker (five card draw) with a friend. The deck of cards is separated into four suits: Diamonds, hearts, clubs and spades, each suit having 13 cards for a total of 52 cards.

Now look at your hand of five cards. Do you have one card from each suit? Maybe. Are all five cards from the same suit? Probably not, but it is possible. Will you get the ace of spades? Maybe, but not likely.

This is a good metaphor for how the founder principle works. The gene pool carried by a small group of founders is unlikely to be precisely representative of the gene pool of the larger group. In some rare cases it will be very unrepresentative, like you getting dealt a straight flush.

It starts to get interesting when this founder population starts to reproduce, and genetic drift causes the new population to diverge significantly from its ancestors. Quammen explains:

Already isolated geographically from its base population, the pioneer population now starts drifting away genetically. Over the course of generations, its gene pool becomes more and more different from the gene pool of the base population – different both as to the array of alleles (that is, the variant forms of a given gene) and as to the commonness of each allele.

The founder population, in some cases, will become so different that it can no longer mate with the original population. This new species may even be a competitor for resources if the two populations are ever reintroduced. (Say, if a land bridge is created between two islands, or humans bring two species back in contact.)

Going back to our card metaphor, let’s pretend that you and your friend are playing with four decks of cards — 208 total cards. Say we randomly pulled out forty cards from those decks. If there are absolutely no kings in the forty cards you are playing with, you will never be able to create a royal flush (ace+king+queen+jack+10 of the same suit). It doesn’t matter how the cards are dealt, you can never make a royal flush with no kings.

Thus it is with species: If a splintered-off population isn’t carrying a specific gene variant (allele), that variant can never be represented in the newly created population, no matter how prolific that gene may have been in the original population. It’s gone. And as the rarest variants disappear, the new population becomes increasingly unlike the old one, especially if the new population is small.

Some alleles are common within a population, some are rare. If the population is large, with thousands or millions of parents producing thousands or millions of offspring, the rare alleles as well as the common ones will usually be passed along. Chance operation at high numbers tends to produce stable results, and the proportions of rarity and commonness will hold steady. If the population is small, though, the rare alleles will most likely disappear […] As it loses its rare alleles by the wayside, a small pioneer population will become increasingly unlike the base population from which it derived.

Some of this genetic loss may be positive (a gene that causes a rare disease may be missing), some may be negative (a gene for a useful attribute may be missing) and some may be neutral.

The neutral ones are the most interesting: A neutral gene at one point in time may become a useful gene at another point. It’s like playing a round of poker where 8’s are suddenly declared “wild,” and that card suddenly becomes much more important than it was the hand before. The same goes for animal traits.

Take a mammal population living on an island, having lost all of its ability to swim. That won’t mean much if all is well and it is never required to swim. But the moment there is a natural disaster such as a fire, having the ability to swim the short distance to the mainland could be the difference between survival or extinction.

That’s why the founder effect is so dangerous: The loss of genetic diversity often means losing valuable survival traits. Quammen explains:

Genetic drift compounds the founder-effect problem, stripping a small population of the genetic variation that it needs to continue evolving. Without that variation, the population stiffens toward uniformity. It becomes less capable of adaptive response. There may be no manifest disadvantages in uniformity so long as environmental circumstances remain stable; but when circumstances are disrupted, the population won’t be capable of evolutionary adjustment. If the disruption is drastic, the population may go extinct.

This loss of adaptability is one of the two major issues caused by the founder effect, the second being inbreeding depression. A founder population may have no choice but to only breed within its population and a symptom of too much inbreeding is the manifestation of harmful genetic variants among inbred individuals. (One reason humans consider incest a dangerous activity.) This too increases the fragility of species and decreases their ability to evolve.

The founder principle is just one of many amazing ideas in The Song of the Dodo. In fact, we at Farnam Street feel the book is so important that it made our list of books we recommend to improve your general knowledge of the world and it was the first book we picked for our learning community reading group.

If you have already read this book and want more we suggest Quammen’s The Reluctant Mr. Darwin or his equally thought provoking Spillover: Animal Infections and the Next Human Pandemic. Another wonderful and readable book on species evolution is The Beak of the Finch, by Jonathan Weiner.

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

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

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


What was the original impetus for writing these books?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How do you define Mental Models?

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

For example some timeless truths are:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What book has most impacted your life?

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

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

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

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

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

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


The Island of Knowledge: Science and the Meaning of Life

“As the Island of Knowledge grows, so do the shores of our ignorance—the boundary between the known and unknown. Learning more about the world doesn’t lead to a point closer to a final destination—whose existence is nothing but a hopeful assumption anyways—but to more questions and mysteries. The more we know, the more exposed we are to our ignorance, and the more we know to ask.”


Common across human history is our longing to better understand the world we live in, and how it works. But how much can we actually know about the world?

In his book, The Island of Knowledge: The Limits of Science and the Search for Meaning, Physicist Marcelo Gleiser traces our progress of modern science in the pursuit to the most fundamental questions on existence, the origin of the universe, and the limits of knowledge.

What we know of the world is limited by what we can see and what we can describe, but our tools have evolved over the years to reveal ever more pleats into our fabric of knowledge. Gleiser celebrates this persistent struggle to understand our place in the world and travels our history from ancient knowledge to our current understanding.

While science is not the only way to see and describe the world we live in, it is a response to the questions on who we are, where we are, and how we got here. “Science speaks directly to our humanity, to our quest for light, ever more light.

To move forward, science needs to fail, which runs counter to our human desire for certainty. “We are surrounded by horizons, by incompleteness.” Rather than give up, we struggle along a scale of progress. What makes us human is this journey to understand more about the mysteries of the world and explain them with reason. This is the core of our nature.

While the pursuit is never ending, the curious journey offers insight not just into the natural world, but insight into ourselves.

“What I see in Nature is a magnificent structure that we can comprehend only
very imperfectly,
and that must fill a thinking person with a feeling of humility.”
— Albert Einstein

We tend to think that what we see is all there is — that there is nothing we cannot see. We know it isn’t true when we stop and think, yet we still get lulled into a trap of omniscience.

Science is thus limited, offering only part of the story — the part we can see and measure. The other part remains beyond our immediate reach.

What we see of the world,” Gleiser begins, “is only a sliver of what’s out there.”

There is much that is invisible to the eye, even when we augment our sensorial perception with telescopes, microscopes, and other tools of exploration. Like our senses, every instrument has a range. Because much of Nature remains hidden from us, our view of the world is based only on the fraction of reality that we can measure and analyze. Science, as our narrative describing what we see and what we conjecture exists in the natural world, is thus necessarily limited, telling only part of the story. … We strive toward knowledge, always more knowledge, but must understand that we are, and will remain, surrounded by mystery. This view is neither antiscientific nor defeatist. … Quite the contrary, it is the flirting with this mystery, the urge to go beyond the boundaries of the known, that feeds our creative impulse, that makes us want to know more.

While we may broadly understand the map of what we call reality, we fail to understand its terrain. Reality, Gleiser argues, “is an ever-shifting mosaic of ideas.”


The incompleteness of knowledge and the limits of our scientific worldview only add to the richness of our search for meaning, as they align science with our human fallibility and aspirations.

What we call reality is a (necessarily) limited synthesis. It is certainly our reality, as it must be, but it is not the entire reality itself:

My perception of the world around me, as cognitive neuroscience teaches us, is synthesized within different regions of my brain. What I call reality results from the integrated sum of countless stimuli collected through my five senses, brought from the outside into my head via my nervous system. Cognition, the awareness of being here now, is a fabrication of a vast set of chemicals flowing through myriad synaptic connections between my neurons. … We have little understanding as to how exactly this neuronal choreography engenders us with a sense of being. We go on with our everyday activities convinced that we can separate ourselves from our surroundings and construct an objective view of reality.

The brain is a great filtering tool, deaf and blind to vast amounts of information around us that offer no evolutionary advantage. Part of it we can see and simply ignore. Other parts, like dust particles and bacteria, go unseen because of limitations of our sensory tools.

As the Fox said to the Little Prince in Antoine de Saint-Exupery’s fable, “What is essential is invisible to the eye.” There is no better example than oxygen.

Science has increased our view. Our measurement tools and instruments can see bacteria and radiation, subatomic particles and more. However precise these tools have become, their view is still limited.

There is no such thing as an exact measurement. Every measurement must be stated within its precision and quoted together with “error bars” estimating the magnitude of errors. High-precision measurements are simply measurements with small error bars or high confidence levels; there are no perfect, zero-error measurements.


Technology limits how deeply experiments can probe into physical reality. That is to say, machines determine what we can measure and thus what scientists can learn about the Universe and ourselves. Being human inventions, machines depend on our creativity and available resources. When successful, they measure with ever-higher accuracy and on occasion may also reveal the unexpected.

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

What we know about the world is only what we can detect and measure — even if we improve our “detecting and measuring” as time goes along. And thus we make our conclusions of reality on what we can currently “see.”

We see much more than Galileo, but we can’t see it all. And this restriction is not limited to measurements: speculative theories and models that extrapolate into unknown realms of physical reality must also rely on current knowledge. When there is no data to guide intuition, scientists impose a “compatibility” criterion: any new theory attempting to extrapolate beyond tested ground should, in the proper limit, reproduce current knowledge.


If large portions of the world remain unseen or inaccessible to us, we must consider the meaning of the word “reality” with great care. We must consider whether there is such a thing as an “ultimate reality” out there — the final substrate of all there is — and, if so, whether we can ever hope to grasp it in its totality.


We thus must ask whether grasping reality’s most fundamental nature is just a matter of pushing the limits of science or whether we are being quite naive about what science can and can’t do.

Here is another way of thinking about this: if someone perceives the world through her senses only (as most people do), and another amplifies her perception through the use of instrumentation, who can legitimately claim to have a truer sense of reality? One “sees” microscopic bacteria, faraway galaxies, and subatomic particles, while the other is completely blind to such entities. Clearly they “see” different things and—if they take what they see literally—will conclude that the world, or at least the nature of physical reality, is very different.

Asking who is right misses the point, although surely the person using tools can see further into the nature of things. Indeed, to see more clearly what makes up the world and, in the process to make more sense of it and ourselves is the main motivation to push the boundaries of knowledge. … What we call “real” is contingent on how deeply we are able to probe reality. Even if there is such thing as the true or ultimate nature of reality, all we have is what we can know of it.


Our perception of what is real evolves with the instruments we use to probe Nature. Gradually, some of what was unknown becomes known. For this reason, what we call “reality” is always changing. … The version of reality we might call “true” at one time will not remain true at another. … Given that our instruments will always evolve, tomorrow’s reality will necessarily include entitles not known to exist today. … More to the point, as long as technology advances—and there is no reason to suppose that it will ever stop advancing for as long as we are around—we cannot foresee an end to this quest. The ultimate truth is elusive, a phantom.

Gleiser makes his point with a beautiful metaphor. The Island of Knowledge.

Consider, then, the sum total of our accumulated knowledge as constituting an island, which I call the “Island of Knowledge.” … A vast ocean surrounds the Island of Knowledge, the unexplored ocean of the unknown, hiding countless tantalizing mysteries.

The Island of Knowledge grows as we learn more about the world and ourselves. And as the island grows, so too “do the shores of our ignorance—the boundary between the known and unknown.”

Learning more about the world doesn’t lead to a point closer to a final destination—whose existence is nothing but a hopeful assumption anyways—but to more questions and mysteries. The more we know, the more exposed we are to our ignorance, and the more we know to ask.

As we move forward we must remember that despite our quest, the shores of our ignorance grow as the Island of Knowledge grows. And while we will struggle with the fact that not all questions will have answers, we will continue to progress. “It is also good to remember,” Gleiser writes, “that science only covers part of the Island.”

Richard Feynman has pointed out before that science can only answer the subset of question that go, roughly, “If I do this, what will happen?” Answers to questions like Why do the rules operate that way? and Should I do it? are not really questions of scientific nature — they are moral, human questions, if they are knowable at all.

There are many ways of understanding and knowing that should, ideally, feed each other. “We are,” Gleiser concludes, “multidimensional creatures and search for answers in many, complementary ways. Each serves a purpose and we need them all.”

“The quest must go on. The quest is what makes us matter: to search for more answers, knowing that the significant ones will often generate surprising new questions.”

The Island of Knowledge is a wide-ranging tour through scientific history from planetary motions to modern scientific theories and how they affect our ideas on what is knowable.

Karl Popper on The Line Between Science and Pseudoscience

It’s not immediately clear, to the layman, what the essential difference is between science and something masquerading as science: pseudoscience. The distinction gets at the core of what comprises human knowledge: How do we actually know something to be true? Is it simply because our powers of observation tell us so? Or is there more to it?

Sir Karl Popper (1902-1994), the scientific philosopher, was interested in the same problem. How do we actually define the scientific process? How do we know which theories can be said to be truly explanatory?


He began addressing it in a lecture, which is printed in the book Conjectures and Refutations: The Growth of Scientific Knowledge (also available online):

When I received the list of participants in this course and realized that I had been asked to speak to philosophical colleagues I thought, after some hesitation and consultation, that you would probably prefer me to speak about those problems which interest me most, and about those developments with which I am most intimately acquainted. I therefore decided to do what I have never done before: to give you a report on my own work in the philosophy of science, since the autumn of 1919 when I first began to grapple with the problem, ‘When should a theory be ranked as scientific?’ or ‘Is there a criterion for the scientific character or status of a theory?’

Popper saw a problem with the number of theories he considered non-scientific that, on their surface, seemed to have a lot in common with good, hard, rigorous science. But the question of how we decide which theories are compatible with the scientific method, and those which are not, was harder than it seemed.


It is most common to say that science is done by collecting observations and grinding out theories from them. Charles Darwin once said, after working long and hard at the problem of the Origin of Species,

My mind seems to have become a kind of machine for grinding general laws out of large collections of facts.

This is a popularly accepted notion. We observe, observe, and observe, and we look for theories to best explain the mass of facts. (Although even this is not really true: Popper points out that we must start with some a priori knowledge to be able to generate new knowledge. Observation is always done with some hypotheses in mind–we can’t understand the world from a totally blank slate. More on that another time.)

The problem, as Popper saw it, is that some bodies of knowledge more properly named pseudosciences would be considered scientific if the “Observe & Deduce” operating definition were left alone. For example, a believing astrologist can ably provide you with “evidence” that their theories are sound. The biographical information of a great many people can be explained this way, they’d say.

The astrologist would tell you, for example, about how “Leos” seek to be the center of attention; ambitious, strong, seeking limelight. As proof, they might follow up with a host of real-life Leos: World-leaders, celebrities, politicians, and so on. In some sense, the theory would hold up. The observations could be explained by the theory, which is how science works, right?

Sir Karl ran into this problem in a concrete way because he lived during a time when psychoanalytic theories were all the rage at just the same time Einstein was laying out a new foundation for the physical sciences with the concept of relativity. What made Popper uncomfortable were comparisons between the two. Why did he feel so uneasy putting Marxist theories and Freudian psychology in the same category of knowledge as Einstein’s Relativity? Did all three not have vast explanatory power in the world? Each theory’s proponents certainly believed so, but Popper was not satisfied.

It was during the summer of 1919 that I began to feel more and more dissatisfied with these three theories–the Marxist theory of history, psychoanalysis, and individual psychology; and I began to feel dubious about their claims to scientific status. My problem perhaps first took the simple form, ‘What is wrong with Marxism, psycho-analysis, and individual psychology? Why are they so different from physical theories, from Newton’s theory, and especially from the theory of relativity?’

I found that those of my friends who were admirers of Marx, Freud, and Adler, were impressed by a number of points common to these theories, and especially by their apparent explanatory power. These theories appeared to be able to explain practically everything that happened within the fields to which they referred. The study of any of them seemed to have the effect of an intellectual conversion or revelation, opening your eyes to a new truth hidden from those not yet initiated. Once your eyes were thus opened you saw confirming instances everywhere: the world was full of verifications of the theory.

Whatever happened always confirmed it. Thus its truth appeared manifest; and unbelievers were clearly people who did not want to see the manifest truth; who refused to see it, either because it was against their class interest, or because of their repressions which were still ‘un-analysed’ and crying aloud for treatment.

Here was the salient problem: The proponents of these new sciences saw validations and verifications of their theories everywhere. If you were having trouble as an adult, it could always be explained by something your mother or father had done to you when you were young, some repressed something-or-other that hadn’t been analyzed and solved. They were confirmation bias machines.

What was the missing element? Popper had figured it out before long: The non-scientific theories could not be falsified. They were not testable in a legitimate way. There was no possible objection that could be raised which would show the theory to be wrong.

In a true science, the following statement can be easily made: “If happens, it would show demonstrably that theory is not true.” We can then design an experiment, a physical one or sometimes a simple thought experiment, to figure out if actually does happen It’s the opposite of looking for verification; you must try to show the theory is incorrect, and if you fail to do so, thereby strengthen it.

Pseudosciences cannot and do not do this–they are not strong enough to hold up. As an example, Popper discussed Freud’s theories of the mind in relation to Alfred Adler’s so-called “individual psychology,” which was popular at the time:

I may illustrate this by two very different examples of human behaviour: that of a man who pushes a child into the water with the intention of drowning it; and that of a man who sacrifices his life in an attempt to save the child. Each of these two cases can be explained with equal ease in Freudian and in Adlerian terms. According to Freud the first man suffered from repression (say, of some component of his Oedipus complex), while the second man had achieved sublimation. According to Adler the first man suffered from feelings of inferiority (producing perhaps the need to prove to himself that he dared to commit some crime), and so did the second man (whose need was to prove to himself that he dared to rescue the child). I could not think of any human behaviour which could not be interpreted in terms of either theory. It was precisely this fact–that they always fitted, that they were always confirmed–which in the eyes of their admirers constituted the strongest argument in favour of these theories. It began to dawn on me that this apparent strength was in fact their weakness.

Popper contrasted these theories against Relativity, which made specific, verifiable predictions, giving the conditions under which the predictions could be shown false. It turned out that Einstein’s predictions came to be true when tested, thus verifying the theory through attempts to falsify it. But the essential nature of the theory gave grounds under which it could have been wrong. To this day, physicists seek to figure out where Relativity breaks down in order to come to a more fundamental understanding of physical reality. And while the theory may eventually be proven incomplete or a special case of a more general phenomenon, it has still made accurate, testable predictions that have led to practical breakthroughs.

Thus, in Popper’s words, science requires testability: “If observation shows that the predicted effect is definitely absent, then the theory is simply refuted.”  This means a good theory must have an element of risk to it. It must be able to be proven wrong under stated conditions.

From there, Popper laid out his essential conclusions, which are useful to any thinker trying to figure out if a theory they hold dear is something that can be put in the scientific realm:

1. It is easy to obtain confirmations, or verifications, for nearly every theory–if we look for confirmations.

2. Confirmations should count only if they are the result of risky predictions; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory–an event which would have refuted the theory.

3. Every ‘good’ scientific theory is a prohibition: it forbids certain things to happen. The more a theory forbids, the better it is.

4. A theory which is not refutable by any conceivable event is nonscientific. Irrefutability is not a virtue of a theory (as people often think) but a vice.

5. Every genuine test of a theory is an attempt to falsify it, or to refute it. Testability is falsifiability; but there are degrees of testability: some theories are more testable, more exposed to refutation, than others; they take, as it were, greater risks.

6. Confirming evidence should not count except when it is the result of a genuine test of the theory; and this means that it can be presented as a serious but unsuccessful attempt to falsify the theory. (I now speak in such cases of ‘corroborating evidence’.)

7. Some genuinely testable theories, when found to be false, are still upheld by their admirers–for example by introducing ad hoc some auxiliary assumption, or by re-interpreting the theory ad hoc in such a way that it escapes refutation. Such a procedure is always possible, but it rescues the theory from refutation only at the price of destroying, or at least lowering, its scientific status. (I later described such a rescuing operation as a ‘conventionalist twist’ or a ‘conventionalist stratagem’.)

One can sum up all this by saying that the criterion of the scientific status of a theory is its falsifiability, or refutability, or testability.

Finally, Popper was careful to say that it is not possible to prove that Freudianism was not true, at least in part. But we can say that we simply don’t know whether it’s true, because it does not make specific testable predictions. It may have many kernels of truth in it, but we can’t tell. The theory would have to be restated.

This is the essential “line of demarcation, as Popper called it, between science and pseudoscience.

Erik Hollnagel: The Search For Causes

A great passage from Erik Hollnagel‘s Barriers And Accident Prevention on the search for causes:

Whenever an accident happens there is a natural concern to find out in detail exactly what happened and to determine the causes of it. Indeed, whenever the result of an action or event falls significantly short of what was expected, or whenever something unexpected happens, people try to find an explanation for it. This trait of human nature is so strong that we try to find causes even when they do not exist, such as in the case of misleading or spurious correlations. For a number of reasons humans seem to be extremely reluctant to accept that something can happen by chance. One very good reason is that we have created a way of living that depends heavily on the use of technology, and that technological systems are built to function in a deterministic, hence reliable manner. If therefore something fails, we are fully justified in trying to find the reason for it. A second reason is that our whole understanding of the world is based on the assumption of specific relations between causes and effects, as amply illustrated by the Laws of Physics. (Even in quantum physics there are assumptions of more fundamental relations that are deterministic.) A third reason is that most humans find it very uncomfortable when they do not know what to expect, i.e., when things happen in an unpredictable manner. This creates a sense of being out of control, something that is never desirable since – from an evolutionary perspective – it means that the chances of survival are reduced.

This was described by Friedrich Nietzsche when he wrote:

[T]o trace something unknown back to something known is alleviating, soothing, gratifying and gives moreover a feeling of power. Danger, disquiet, anxiety attend the unknown – the first instinct is to eliminate these distressing states. First principle: any explanation is better than none … The cause-creating drive is thus conditioned and excited by the feeling of fear.

Hollnagel, continues:

A well-known example of this is provided by the phenomenon called the gambler’s fallacy. The name refers to the fact that gamblers often seem to believe that a long row of events of one type increases the probability of the complementary event. Thus if a series of ‘red’ events occur on a roulette wheel, the gambler’s fallacy lead people to believe that the probability of ‘black’ increases. … Rather than accepting that the underlying mechanism may be random people invent all kinds of explanations to reduce the uncertainty of future events.