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Category Archives: Science

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.

“As the Island of Knowledge grows, so do the shores of our ignorance.” Click To Tweet

Thomas Kuhn: The Structure of Scientific Revolutions

“The decision to reject one paradigm is always simultaneously the decision to accept another, and the judgment leading to that decision involves the comparison of both paradigms with nature and with each other.”

structure of scientific revolutions

The progress of science is commonly perceived of as a continuous, incremental advance, where new discoveries add to the existing body of scientific knowledge. This view of scientific progress, however, is challenged by the physicist and philosopher of science Thomas Kuhn, in his book The Structure of Scientific Revolutions. Kuhn argues that the history of science tells a different story, one where science proceeds with a series of revolutions interrupting normal incremental progress.

“A prevailing theory or paradigm is not overthrown by the accumulation of contrary evidence,” Richard Zeckhauser wrote, “but rather by a new paradigm that, for whatever reasons, begins to be accepted by scientists.”

Between scientific revolutions, old ideas and beliefs persist. These form the barriers of resistance to alternative explanations.

Zeckhauser continues “In this view, scientific scholars are subject to status quo persistence. Far from being objective decoders of the empirical evidence, scientists have decided preferences about the scientific beliefs they hold. From a psychological perspective, this preference for beliefs can be seen as a reaction to the tensions caused by cognitive dissonance. ”

* * *

Gary Taubes posted an excellent blog post discussing how paradigm shifts come about in science. He wrote:

…as Kuhn explained in The Structure of Scientific Revolutions, his seminal thesis on paradigm shifts, the people who invariably do manage to shift scientific paradigms are “either very young or very new to the field whose paradigm they change… for obviously these are the men [or women, of course] who, being little committed by prior practice to the traditional rules of normal science, are particularly likely to see that those rules no longer define a playable game and to conceive another set that can replace them.”

So when a shift does happen, it’s almost invariably the case that an outsider or a newcomer, at least, is going to be the one who pulls it off. This is one thing that makes this endeavor of figuring out who’s right or what’s right such a tricky one. Insiders are highly unlikely to shift a paradigm and history tells us they won’t do it. And if outsiders or newcomers take on the task, they not only suffer from the charge that they lack credentials and so credibility, but their work de facto implies that they know something that the insiders don’t – hence, the idiocy implication.

…This leads to a second major problem with making these assessments – who’s right or what’s right. As Kuhn explained, shifting a paradigm includes not just providing a solution to the outstanding problems in the field, but a rethinking of the questions that are asked, the observations that are considered and how those observations are interpreted, and even the technologies that are used to answer the questions. In fact, often the problems that the new paradigm solves, the questions it answers, are not the problems and the questions that practitioners living in the old paradigm would have recognized as useful.

“Paradigms provide scientists not only with a map but also with some of the direction essential for map-making,” wrote Kuhn. “In learning a paradigm the scientist acquires theory, methods, and standards together, usually in an inextricable mixture. Therefore, when paradigms change, there are usually significant shifts in the criteria determining the legitimacy both of problems and of proposed solutions.”

As a result, Kuhn said, researchers on different sides of conflicting paradigms can barely discuss their differences in any meaningful way: “They will inevitably talk through each other when debating the relative merits of their respective paradigms. In the partially circular arguments that regularly result, each paradigm will be shown to satisfy more or less the criteria that it dictates for itself and to fall short of a few of those dictated by its opponent.”

But Taubes’ explanation wasn’t enough to satisfy my curiosity.


The Structure of Scientific Revolutions

To learn more on how paradigm shifts happen, I purchased Kuhn’s book, The Structure of Scientific Revolutions, and started to investigate.

Kuhn writes:

“The decision to reject one paradigm is always simultaneously the decision to accept another, and the judgment leading to that decision involves the comparison of both paradigms with nature and with each other.”

Anomalies are not all bad.

Yet any scientist who pauses to examine and refute every anomaly will seldom get any work done.

…during the sixty years after Newton’s original computation, the predicted motion of the moon’s perigee remained only half of that observed. As Europe’s best mathematical physicists continued to wrestle unsuccessfully with the well-known discrepancy, there were occasional proposals for a modification of Newton’s inverse square law. But no one took these proposals very seriously, and in practice this patience with a major anomaly proved justified. Clairaut in 1750 was able to show that only the mathematics of the application had been wrong and that Newtonian theory could stand as before. … persistent and recognized anomaly does not always induce crisis. … It follows that if an anomaly is to evoke crisis, it must usually be more than just an anomaly.

So what makes an anomaly worth the effort of investigation?

To that question Kuhn responds, “there is probably no fully general answer.” Einstein knew how to sift the essential from the non-essential better than most.

When the anomaly comes to be recognized as more than another puzzle of science the transition, or revolution, has begun.

The anomaly itself now comes to be more generally recognized as such by the profession. More and more attention is devoted to it by more and more of the field’s most eminent men. If it still continues to resist, as it usually does not, many of them may come to view its resolution as the subject matter of their discipline. …

Early attacks on the anomaly will have followed the paradigm rules closely. As time passes and scrutiny increases, more of the attacks will start to diverge from the existing paradigm. It is “through this proliferation of divergent articulations,” Kuhn argues, “the rules of normal science become increasing blurred.

Though there still is a paradigm, few practitioners prove to be entirely agreed about what it is. Even formally standard solutions of solved problems are called into question.”

Einstein explained this transition, which is the structure of scientific revolutions, best. He said: “It was as if the ground had been pulled out from under one, with no firm foundation to be seen anywhere, upon which one could have built.

All scientific crises begin with the blurring of a paradigm.

In this respect research during crisis very much resembles research during the pre-paradigm period, except that in the former the locus of difference is both smaller and more clearly defined. And all crises close in one of three ways. Sometimes normal science ultimately proves able to handle the crisis—provoking problem despite the despair of those who have seen it as the end of an existing paradigm. On other occasions the problem resists even apparently radical new approaches. Then scientists may conclude that no solution will be forthcoming in the present state of their field. The problem is labelled and set aside for a future generation with more developed tools. Or, finally, the case that will most concern us here, a crisis may end up with the emergence of a new candidate for paradigm and with the ensuing battle over its acceptance.

But this isn’t easy.

The transition from a paradigm in crisis to a new one from which a new tradition of normal science can emerge is far from a cumulative process, one achieved by an articulation or extension of the old paradigm. Rather it is a reconstruction of the field from new fundamentals, a reconstruction that changes some of the field’s most elementary theoretical generalizations as well as many of its paradigm methods and applications.

Who solves these problems? Do the men and women who have invested a large portion of their lives in a field or theory suddenly confront evidence and change their mind? Sadly, no.

Almost always the men who achieve these fundamental inventions of a new paradigm have been either very young, or very new to the field whose paradigm they change. And perhaps that point need not have been made explicit, for obviously these are men who, being little committed by prior practice to the traditional rules of normal science, are particularly likely to see that those rules no longer define a playable game and to conceive another set that can replace them.


Therefore, when paradigms change, there are usually significant shifts in the criteria determining the legitimacy both of problems and of proposed solutions.

That observation returns us to the point from which this section began, for it provides our first explicit indication of why the choice between competing paradigms regularly raises questions that cannot be resolved by the criteria of normal science. To the extent, as significant as it is incomplete, that two scientific schools disagree about what is a problem and what is a solution, they will inevitably talk through each other when debating the relative merits of their respective paradigms. In the partially circular arguments that regularly result, each paradigm will be shown to satisfy more or less the criteria that it dictates for itself and to fall short of a few of those dictated by its opponent. There are other reasons, too, for the incompleteness of logical contact that consistently characterizes paradigm debates. For example, since no paradigm ever solves all the problems it defines and since no two paradigms leave all the same problems unsolved, paradigm debates always involve the question: Which problems is it more significant to have solved? Like the issue of competing standards, that questions of values can be answered only in terms of criteria that lie outside of normal science altogether.

Many years ago Max Planck offered this insight: “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”

If you’re interested in learning more about how paradigm shifts happen, read The Structure of Scientific Revolutions.