“There is a huge danger in looking at life as an optimization problem.”
Rory Sutherland (@rorysutherland) is the Vice Chairman of Ogilvy & Mather Group, which is one of the largest advertising companies in the world.
Rory started the behavioral insights team and spends his days applying behavioral economics and evolutionary psychology to solve problems that conventionally advertising agencies haven't been able to solve.
In this wide-ranging interview we talk about: how advertising agencies are solving airport security problems, what Silicon Valley misses, how to mess with self-driving cars, reading habits, decision making, the intersection of advertising and psychology, and so much more.
This interview was recorded live in London, England.
Enjoy this amazing conversation.
“The problem with economics is not only that it is wrong but that it's incredibly creatively limiting.”
We have seen before that the map is not the territory — that the description of the thing is not the thing itself. Maps can be exceptionally useful. For instance, we can leverage the experiences of others to help us navigate through territories that are, to us, new and unknown. We just have to understand and respect the inherent limitations of maps whose territories may have changed. We have to put some work into really seeing what the maps can show us.
Maps are an abstraction, which means information is lost in order to save space. So perhaps the most important thing we can do before reading a map is to stop and consider what choices have been made in the representation before us.
First, there are some limitations based on the medium used, like paper or digital, and the scale of the territory you are trying to represent. Take the solar system. Our maps of the solar system typically fit on one page. This makes them useful for understanding the order of the planets from the sun but does not even come close to conveying the size of the territory of space.
Bill Bryson explains in A Short History of Nearly Everything, “such are the distances, in fact, that it isn’t possible, in any practical terms, to draw the solar system to scale. … On a diagram of the solar system to scale, with the Earth reduced to about the diameter of a pea, Jupiter would be over a thousand feet away, and Pluto would be a mile and half distant (and about the size of a bacterium, so you wouldn’t be able to see it anyway).”
Maps are furthermore a single visual perspective chosen because you believe it the best one for what you are trying to communicate. This perspective is both literal — what I actually see from my eyes, and figurative — the bias that guides the choices I make.
It’s easy to understand how unique my perspective is. Someone standing three feet away from me is going to have a different perspective than I do. I’ve been totally amazed by the view out of my neighbour’s window.
Jerry Brotton, in his book A History of the World in Twelve Maps, reveals that “the problem of defining where the viewer stands in the relation to a map of the world is one geographers have struggled with for centuries.” Right from the beginning, your starting point becomes your frame of reference, the centre of understanding that everything else links back to.
In an example that should be a classic, but isn’t because of a legacy of visual representation that has yet to change, most of us seriously underestimate the size of Africa. Why? Because, as Tim Marshall explains in his book Prisoners of Geography, most of us use the standard Mercator world map, and “this, as do other maps, depicts a sphere on a flat surface and thus distorts shapes.” A world map always has to be distorted, with a bent toward the view you are trying to present. Which has led to a northern hemisphere centric vision of the world that has been burned into our brains.
Even though Africa looks roughly the size of Greenland, in fact, it is actually about 14 times larger. Don’t use the standard Mercator map to plan your hiking trip!
Knowing a map’s limitations in perspective points you to where you need to bring context. Consider this passage from Marshall’s book: “Africa’s coastline? Great beaches – really, really lovely beaches – but terrible natural harbors. Amazing rivers, but most of them are worthless for actually transporting anything, given that every few miles you go over a waterfall.” A lot of maps wouldn’t show you this – the lines that are rivers are all drawn the same. So you’d look at the success the Europeans had with the Danube or the Rhine and think, why didn’t Africans think to use their rivers in the same way? And then maybe you decide to invest in an African mineral company, bringing to the table the brilliant idea of getting your products to market via river. And then they take you to the waterfalls.
Consider who draws the maps. A map of the modern day Middle East will probably tell you more about the British and French than any inhabitants of the region. In 1916 a British diplomat named Sykes and a French diplomat name Picot drew a line dividing the territory between their countries based on their interests in the region and not on the cultures of the people living there, or the physical formations that give it form.
Marshall explains, “The region’s very name is based on a European view of the world, and it is a European view of the region that shaped it. The Europeans used ink to draw lines on maps: they were lines that did not exist in reality and created some of the most artificial borders the world has seen. An attempt is now being made to redraw them in blood.”
The map creator is going to bring not only their understanding but also their biases and agenda. Even if your goal is to create the most accurate, unbiased map ever, that intent frames the decisions you make on what to represent and what to leave out. Our relatively new digital mapping makes a decision to respect some privacy at the outset and so Google doesn’t include images of people in its ‘streetview'.
Brotton argues that “a map always manages the reality it tries to show.” And as we have seen before, because there really isn’t one objective reality, maps need to be understood as portraying personal or cultural realities.
“No world map is, or can be, a definitive, transparent depiction of its subject that offers a disembodied eye onto the world.” All maps reflect our understanding of the territory at that moment in time. We change, and maps change with us.
This leads to another pitfall. Get the right map. Or better yet, get multiple maps of the same territory. Different explorations require different maps. Don’t get comfortable with one and assume that’s going to explain everything you need. Change the angle.
Derek Hayes, in his Historical Atlas of Toronto, has put together a fascinating pictorial representation of the history of Toronto in maps. Sewer maps, transit maps, maps from before there were any roads, and planning maps for the future. Maps of buildings that were, and maps of buildings that are only dreams. Putting all these together starts to flesh out the context, allowing for an appreciation of a complex city versus a dot on a piece of paper. Maps may never be able to describe the whole territory, but the more you can combine them, the fewer blind spots you will have.
If you compare a map of American naval bases in 1947 with one from 1937, you would notice a huge discrepancy. The number increased significantly. Armed only with this map you might conclude that in addition to fighting in WWII, the Americans invested a lot of resources in base building during the 40s. But if you could get your hands on a map of British naval bases from 1937 you would conclude something entirely different.
As Marshall explains, “In the autumn of 1940, the British desperately needed more warships. The Americans had fifty to spare and so, with what was called the Destroyers for Bases Agreement, the British swapped their ability to be a global power for help in remaining in the war. Almost every British naval base in the Western Hemisphere was handed over.”
The message here is not to give up on maps. They can be wonderful and provide many useful insights. It is rather to understand their limitations. Each map carries the perspective of its creator and is limited by the medium it’s presented in. The more maps you have of a territory, the increased understanding you will have of the complexities of the terrain, allowing you to make better decisions as you navigate through it.
John Brockman's online scientific roundtable Edge.org does something fantastic every year: It asks all of its contributors (hundreds of them) to answer one meaningful question. Questions like What Have You Changed Your Mind About? and What is Your Dangerous Idea?
The answers give us a window into over 200 brilliant minds, with the simple filtering mechanism that there's something they know that we should probably know, too. We wanted to highlight a few of our favorites for you.
The Second Law of Thermodynamics states that in an isolated system (one that is not taking in energy), entropy never decreases. (The First Law is that energy is conserved; the Third, that a temperature of absolute zero is unreachable.) Closed systems inexorably become less structured, less organized, less able to accomplish interesting and useful outcomes, until they slide into an equilibrium of gray, tepid, homogeneous monotony and stay there.
In its original formulation the Second Law referred to the process in which usable energy in the form of a difference in temperature between two bodies is dissipated as heat flows from the warmer to the cooler body. Once it was appreciated that heat is not an invisible fluid but the motion of molecules, a more general, statistical version of the Second Law took shape. Now order could be characterized in terms of the set of all microscopically distinct states of a system: Of all these states, the ones that we find useful make up a tiny sliver of the possibilities, while the disorderly or useless states make up the vast majority. It follows that any perturbation of the system, whether it is a random jiggling of its parts or a whack from the outside, will, by the laws of probability, nudge the system toward disorder or uselessness. If you walk away from a sand castle, it won’t be there tomorrow, because as the wind, waves, seagulls, and small children push the grains of sand around, they’re more likely to arrange them into one of the vast number of configurations that don’t look like a castle than into the tiny few that do.
The Second Law of Thermodynamics is acknowledged in everyday life, in sayings such as “Ashes to ashes,” “Things fall apart,” “Rust never sleeps,” “Shit happens,” You can’t unscramble an egg,” “What can go wrong will go wrong,” and (from the Texas lawmaker Sam Rayburn), “Any jackass can kick down a barn, but it takes a carpenter to build one.”
Scientists appreciate that the Second Law is far more than an explanation for everyday nuisances; it is a foundation of our understanding of the universe and our place in it. In 1915 the physicist Arthur Eddington wrote:
Why the awe for the Second Law? The Second Law defines the ultimate purpose of life, mind, and human striving: to deploy energy and information to fight back the tide of entropy and carve out refuges of beneficial order. An underappreciation of the inherent tendency toward disorder, and a failure to appreciate the precious niches of order we carve out, are a major source of human folly.
To start with, the Second Law implies that misfortune may be no one’s fault. The biggest breakthrough of the scientific revolution was to nullify the intuition that the universe is saturated with purpose: that everything happens for a reason. In this primitive understanding, when bad things happen—accidents, disease, famine—someone or something must have wanted them to happen. This in turn impels people to find a defendant, demon, scapegoat, or witch to punish. Galileo and Newton replaced this cosmic morality play with a clockwork universe in which events are caused by conditions in the present, not goals for the future. The Second Law deepens that discovery: Not only does the universe not care about our desires, but in the natural course of events it will appear to thwart them, because there are so many more ways for things to go wrong than to go right. Houses burn down, ships sink, battles are lost for the want of a horseshoe nail.
Poverty, too, needs no explanation. In a world governed by entropy and evolution, it is the default state of humankind. Matter does not just arrange itself into shelter or clothing, and living things do everything they can not to become our food. What needs to be explained is wealth. Yet most discussions of poverty consist of arguments about whom to blame for it.
More generally, an underappreciation of the Second Law lures people into seeing every unsolved social problem as a sign that their country is being driven off a cliff. It’s in the very nature of the universe that life has problems. But it’s better to figure out how to solve them—to apply information and energy to expand our refuge of beneficial order—than to start a conflagration and hope for the best.
Modern scientific psychology insists that explanation of the behavior of humans always requires reference to the situation the person is in. The failure to do so sufficiently is known as the Fundamental Attribution Error. In Milgram’s famous obedience experiment, two-thirds of his subjects proved willing to deliver a great deal of electric shock to a pleasant-faced middle-aged man, well beyond the point where he became silent after begging them to stop on account of his heart condition. When I teach about this experiment to undergraduates, I’m quite sure I‘ve never convinced a single one that their best friend might have delivered that amount of shock to the kindly gentleman, let alone that they themselves might have done so. They are protected by their armor of virtue from such wicked behavior. No amount of explanation about the power of the unique situation into which Milgram’s subject was placed is sufficient to convince them that their armor could have been breached.
My students, and everyone else in Western society, are confident that people behave honestly because they have the virtue of honesty, conscientiously because they have the virtue of conscientiousness. (In general, non-Westerners are less susceptible to the fundamental attribution error, lacking as they do sufficient knowledge of Aristotle!) People are believed to behave in an open and friendly way because they have the trait of extroversion, in an aggressive way because they have the trait of hostility. When they observe a single instance of honest or extroverted behavior they are confident that, in a different situation, the person would behave in a similarly honest or extroverted way.
In actual fact, when large numbers of people are observed in a wide range of situations, the correlation for trait-related behavior runs about .20 or less. People think the correlation is around .80. In reality, seeing Carlos behave more honestly than Bill in a given situation increases the likelihood that he will behave more honestly in another situation from the chance level of 50 percent to the vicinity of 55-57. People think that if Carlos behaves more honestly than Bill in one situation the likelihood that he will behave more honestly than Bill in another situation is 80 percent!
How could we be so hopelessly miscalibrated? There are many reasons, but one of the most important is that we don’t normally get trait-related information in a form that facilitates comparison and calculation. I observe Carlos in one situation when he might display honesty or the lack of it, and then not in another for perhaps a few weeks or months. I observe Bill in a different situation tapping honesty and then not another for many months.
This implies that if people received behavioral data in such a form that many people are observed over the same time course in a given fixed situation, our calibration might be better. And indeed it is. People are quite well calibrated for abilities of various kinds, especially sports. The likelihood that Bill will score more points than Carlos in one basketball game given that he did in another is about 67 percent—and people think it’s about 67 percent.
Our susceptibility to the fundamental attribution error—overestimating the role of traits and underestimating the importance of situations—has implications for everything from how to select employees to how to teach moral behavior.
Cesar Hidalgo, author of what looks like an awesome book, Why Information Grows, wrote about Criticality, which is a very important and central concept to understanding complex systems:
In physics we say a system is in a critical state when it is ripe for a phase transition. Consider water turning into ice, or a cloud that is pregnant with rain. Both of these are examples of physical systems in a critical state.
The dynamics of criticality, however, are not very intuitive. Consider the abruptness of freezing water. For an outside observer, there is no difference between cold water and water that is just about to freeze. This is because water that is just about to freeze is still liquid. Yet, microscopically, cold water and water that is about to freeze are not the same.
When close to freezing, water is populated by gazillions of tiny ice crystals, crystals that are so small that water remains liquid. But this is water in a critical state, a state in which any additional freezing will result in these crystals touching each other, generating the solid mesh we know as ice. Yet, the ice crystals that formed during the transition are infinitesimal. They are just the last straw. So, freezing cannot be considered the result of these last crystals. They only represent the instability needed to trigger the transition; the real cause of the transition is the criticality of the state.
But why should anyone outside statistical physics care about criticality?
The reason is that history is full of individual narratives that maybe should be interpreted in terms of critical phenomena.
Did Rosa Parks start the civil rights movement? Or was the movement already running in the minds of those who had been promised equality and were instead handed discrimination? Was the collapse of Lehman Brothers an essential trigger for the Great Recession? Or was the financial system so critical that any disturbance could have made the trick?
As humans, we love individual narratives. We evolved to learn from stories and communicate almost exclusively in terms of them. But as Richard Feynman said repeatedly: The imagination of nature is often larger than that of man. So, maybe our obsession with individual narratives is nothing but a reflection of our limited imagination. Going forward we need to remember that systems often make individuals irrelevant. Just like none of your cells can claim to control your body, society also works in systemic ways.
So, the next time the house of cards collapses, remember to focus on why we were building a house of cards in the first place, instead of focusing on whether the last card was the queen of diamonds or a two of clubs.
The psychologist Adam Alter has another good one on a concept we all naturally miss from time to time, due to the structure of our mind. The Law of Small Numbers.
In 1832, a Prussian military analyst named Carl von Clausewitz explained that “three quarters of the factors on which action in war is based are wrapped in a fog of . . . uncertainty.” The best military commanders seemed to see through this “fog of war,” predicting how their opponents would behave on the basis of limited information. Sometimes, though, even the wisest generals made mistakes, divining a signal through the fog when no such signal existed. Often, their mistake was endorsing the law of small numbers—too readily concluding that the patterns they saw in a small sample of information would also hold for a much larger sample.
Both the Allies and Axis powers fell prey to the law of small numbers during World War II. In June 1944, Germany flew several raids on London. War experts plotted the position of each bomb as it fell, and noticed one cluster near Regent’s Park, and another along the banks of the Thames. This clustering concerned them, because it implied that the German military had designed a new bomb that was more accurate than any existing bomb. In fact, the Luftwaffe was dropping bombs randomly, aiming generally at the heart of London but not at any particular location over others. What the experts had seen were clusters that occur naturally through random processes—misleading noise masquerading as a useful signal.
That same month, German commanders made a similar mistake. Anticipating the raid later known as D-Day, they assumed the Allies would attack—but they weren’t sure precisely when. Combing old military records, a weather expert named Karl Sonntag noticed that the Allies had never launched a major attack when there was even a small chance of bad weather. Late May and much of June were forecast to be cloudy and rainy, which “acted like a tranquilizer all along the chain of German command,” according to Irish journalist Cornelius Ryan. “The various headquarters were quite confident that there would be no attack in the immediate future. . . . In each case conditions had varied, but meteorologists had noted that the Allies had never attempted a landing unless the prospects of favorable weather were almost certain.” The German command was mistaken, and on Tuesday, June 6, the Allied forces launched a devastating attack amidst strong winds and rain.
The British and German forces erred because they had taken a small sample of data too seriously: The British forces had mistaken the natural clustering that comes from relatively small samples of random data for a useful signal, while the German forces had mistaken an illusory pattern from a limited set of data for evidence of an ongoing, stable military policy. To illustrate their error, imagine a fair coin tossed three times. You’ll have a one-in-four chance of turning up a string of three heads or tails, which, if you make too much of that small sample, might lead you to conclude that the coin is biased to reveal one particular outcome all or almost all of the time. If you continue to toss the fair coin, say, a thousand times, you’re far more likely to turn up a distribution that approaches five hundred heads and five hundred tails. As the sample grows, your chance of turning up an unbroken string shrinks rapidly (to roughly one-in-sixteen after five tosses; one-in-five-hundred after ten tosses; and one-in-five-hundred-thousand after twenty tosses). A string is far better evidence of bias after twenty tosses than it is after three tosses—but if you succumb to the law of small numbers, you might draw sweeping conclusions from even tiny samples of data, just as the British and Germans did about their opponents’ tactics in World War II.
Of course, the law of small numbers applies to more than military tactics. It explains the rise of stereotypes (concluding that all people with a particular trait behave the same way); the dangers of relying on a single interview when deciding among job or college applicants (concluding that interview performance is a reliable guide to job or college performance at large); and the tendency to see short-term patterns in financial stock charts when in fact short-term stock movements almost never follow predictable patterns. The solution is to pay attention not just to the pattern of data, but also to how much data you have. Small samples aren’t just limited in value; they can be counterproductive because the stories they tell are often misleading.
There are many, many more worth reading. Here's a great chance to build your multidisciplinary skill-set.
If we are to make something worthwhile of ourselves, we have to take a good hard look at ourselves. And this, for Nietzsche, means many things. It means looking at ourselves in the light of everything we can learn about the world and ourselves from the natural sciences — most emphatically including evolutionary biology, physiology and even medical science. It also means looking at ourselves in the light of everything we can learn about human life from history, from the social sciences, from the study of arts, religions, literatures, mores and other features of various cultures. It further means attending to human conduct on different levels of human interaction, to the relation between what people say and seem to think about themselves and what they do, to their reactions in different sorts of situations, and to everything about them that affords clues to what makes them tick. All of this, and more, is what Nietzsche is up to in Human, All Too Human. He is at once developing and employing the various perspectival techniques that seem to him to be relevant to the understanding of what we have come to be and what we have it in us to become. This involves gathering materials for a reinterpretation and reassessment of human life, making tentative efforts along those lines and then trying them out on other human phenomena both to put them to the test and to see what further light can be shed by doing so.
Nietzsche realized that mental models were the key to not only understanding the world but understanding ourselves. Understanding how the world works is the key making more effective decisions and gaining insights. However, its through the journey of discovery of these ideas, that we learn about ourselves. Most of us want to skip the work, so we skim the surface of not only knowledge but ourselves.
We’re quite glad that you read Farnam Street, and we hope we’re always offering you a massive amount of value. (If not, email us and tell us what we can do more effectively.)
But there’s a message all of our readers should appreciate: Blog posts are not enough to generate the deep fluency you need to truly understand or get better at something. We offer a starting point, not an end point.
This goes just as well for book reviews, abstracts, cliff's notes, and a good deal of short-form journalism.
This is a hard message for some who want a shortcut. They want the “gist” and the “high level takeaways”, without doing the work or eating any of the broccoli. They think that’s all it takes: Check out a 5-minute read, and instantly their decision making and understanding of the world will improve right-quick. Most blogs, of course, encourage this kind of shallowness. Because it makes you feel that the whole thing is pretty easy.
Here’s the problem: The world is more complex than that. It doesn’t actually work this way. The nuanced detail behind every “high level takeaway” gives you the context needed to use it in the real world. The exceptions, the edge cases, and the contradictions.
However, that’s not enough. There are so many follow-up questions. Where do we make the most mistakes? Why does our mind create these models? Where is this generally useful? What are the nuanced examples of where this tendency fails us? And so on. Just knowing about the Heuristic, knowing that it exists, won't perform any work for you.
But Harari’s book itself contains the relevant detail that fleshes all of this out. And further, his bibliography is full of resources that demand your attention to get even more backup. How did he develop that idea? You have to look to find out.
Why do all this? Because without the massive, relevant detail, your mind is built on a house of cards.
What Farnam Street and a lot of other great resources give you is something like a brief map of the territory.
Welcome to Colonial Williamsburg! Check out the re-enactors, the museum, and the theatre. Over there is the Revolutionary City. Gettysburg is 4 hours north. Washington D.C. is closer to 2.5 hours.
Great – now you have a lay of the land. Time to dig in and actually learn about the American Revolution. (This book is awesome, if you actually want to do that.)
Going back to Kahneman, one of his and Tversky’s great findings was the concept of the Availability Heuristic. Basically, the mind operates on what it has close at hand.
As Kahneman puts it, “An essential design feature of the associative machine is that it represents only activated ideas. Information that is not retrieved (even unconsciously) from memory might as well not exist. System 1 excels at constructing the best possible story that incorporates ideas currently activated, but it does not (cannot) allow for information it does not have.”
That means that in the moment of decision making, when you’re thinking hard on some complex problem you face, it’s unlikely that your mind is working all that successfully without the details. It doesn't have anything to draw on. It’d be like a chess player who read a book about great chess players, but who hadn’t actually studied all of their moves. Not very effective.
That’s why you must develop excellent filters. What’s worth learning this deeply? We think it’s the first-principle style mental models. The great ideas from physical systems, biological systems, and human systems. The new-new thing you’re studying is probably either A. Wrong or B. Built on one of those great ideas anyways. Farnam Street, in a way, is just a giant filtering mechanism to get you started down the hill.
But don't stop there. Don't stop at the starting line. Resolve to increase your depth and stop thinking you can have it all in 5 minutes or less. Use our stuff, and whoever else's stuff you like, as an entrée to the real thing.