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Algorithms are all the rage these days. AI researchers are taking more and more ground from humans in areas like rules-based games, visual recognition, and medical diagnosis. However, the idea that algorithms make better predictive decisions than humans in many fields is a very old one.
In 1954, the psychologist Paul Meehl published a controversial book with a boring sounding name: Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence.
The controversy? After reviewing the data, Meehl claimed that mechanical, data-driven algorithms could better predict human behavior than trained clinical psychologists — and with much simpler criteria. He was right.
The passing of time has not been friendly to humans in this game: Studies continue to show that the algorithms do a better job than experts in a range of fields. In Daniel Kahneman's Thinking Fast and Slow, he details a selection of fields which have demonstrated inferior human judgment compared to algorithms:
The range of predicted outcomes has expanded to cover medical variables such as the longevity of cancer patients, the length of hospital stays, the diagnosis of cardiac disease, and the susceptibility of babies to sudden infant death syndrome; economic measures such as the prospects of success for new businesses, the evaluation of credit risks by banks, and the future career satisfaction of workers; questions of interest to government agencies, including assessments of the suitability of foster parents, the odds of recidivism among juvenile offenders, and the likelihood of other forms of violent behavior; and miscellaneous outcomes such as the evaluation of scientific presentations, the winners of football games, and the future prices of Bordeaux wine.
The connection between them? Says Kahneman: “Each of these domains entails a significant degree of uncertainty and unpredictability.” He called them “low-validity environments”, and in those environments, simple algorithms matched or outplayed humans and their “complex” decision making criteria, essentially every time.
A typical case is described in Michael Lewis' book on the relationship between Daniel Kahneman and Amos Tversky, The Undoing Project. He writes of work done at the Oregon Research Institute on radiologists and their x-ray diagnoses:
The Oregon researchers began by creating, as a starting point, a very simple algorithm, in which the likelihood that an ulcer was malignant depended on the seven factors doctors had mentioned, equally weighted. The researchers then asked the doctors to judge the probability of cancer in ninety-six different individual stomach ulcers, on a seven-point scale from “definitely malignant” to “definitely benign.” Without telling the doctors what they were up to, they showed them each ulcer twice, mixing up the duplicates randomly in the pile so the doctors wouldn't notice they were being asked to diagnose the exact same ulcer they had already diagnosed. […] The researchers' goal was to see if they could create an algorithm that would mimic the decision making of doctors.
This simple first attempt, [Lewis] Goldberg assumed, was just a starting point. The algorithm would need to become more complex; it would require more advanced mathematics. It would need to account for the subtleties of the doctors' thinking about the cues. For instance, if an ulcer was particularly big, it might lead them to reconsider the meaning of the other six cues.
But then UCLA sent back the analyzed data, and the story became unsettling. (Goldberg described the results as “generally terrifying”.) In the first place, the simple model that the researchers had created as their starting point for understanding how doctors rendered their diagnoses proved to be extremely good at predicting the doctors' diagnoses. The doctors might want to believe that their thought processes were subtle and complicated, but a simple model captured these perfectly well. That did not mean that their thinking was necessarily simple, only that it could be captured by a simple model.
More surprisingly, the doctors' diagnoses were all over the map: The experts didn't agree with each other. Even more surprisingly, when presented with duplicates of the same ulcer, every doctor had contradicted himself and rendered more than one diagnosis: These doctors apparently could not even agree with themselves.
If you wanted to know whether you had cancer or not, you were better off using the algorithm that the researchers had created than you were asking the radiologist to study the X-ray. The simple algorithm had outperformed not merely the group of doctors; it had outperformed even the single best doctor.
The fact that doctors (and psychiatrists, and wine experts, and so forth) cannot even agree with themselves is a problem called decision making “noise”: Given the same set of data twice, we make two different decisions. Noise. Internal contradiction.
Algorithms win, at least partly, because they don't do this: The same inputs generate the same outputs every single time. They don't get distracted, they don't get bored, they don't get mad, they don't get annoyed. Basically, they don't have off days. And they don't fall prey to the litany of biases that humans do, like the representativeness heuristic.
The algorithm doesn't even have to be a complex one. As demonstrated above with radiology, simple rules work just as well as complex ones. Kahneman himself addresses this in Thinking, Fast and Slow when discussing Robyn Dawes's research on the superiority of simple algorithms using a few equally-weighted predictive variables:
The surprising success of equal-weighting schemes has an important practical implication: it is possible to develop useful algorithms without prior statistical research. Simple equally weight formulas based on existing statistics or on common sense are often very good predictors of significant outcomes. In a memorable example, Dawes showed that marital stability is well predicted by a formula: Frequency of lovemaking minus frequency of quarrels.
You don't want your result to be a negative number.
The important conclusion from this research is that an algorithm that is constructed on the back of an envelope is often good enough to compete with an optimally weighted formula, and certainly good enough to outdo expert judgment. This logic can be applied in many domains, ranging from the selection of stocks by portfolio managers to the choices of medical treatments by doctors or patients.
Stock selection, certainly a “low validity environment”, is an excellent example of the phenomenon.
As John Bogle pointed out to the world in the 1970's, a point which has only strengthened with time, the vast majority of human stock-pickers cannot outperform a simple S&P 500 index fund, an investment fund that operates on strict algorithmic rules about which companies to buy and sell and in what quantities. The rules of the index aren't complex, and many people have tried to improve on them with less success than might be imagined.
Another interesting area where this holds is interviewing and hiring, a notoriously difficult “low-validity” environment. Even elite firms often don't do it that well, as has been well documented.
Fortunately, if we take heed of the advice of the psychologists, operating in a low-validity environment has rules that can work very well. In Thinking Fast and Slow, Kahneman recommends fixing your hiring process by doing the following (or some close variant), in order to replicate the success of the algorithms:
Suppose you need to hire a sales representative for your firm. If you are serious about hiring the best possible person for the job, this is what you should do. First, select a few traits that are prerequisites for success in this position (technical proficiency, engaging personality, reliability, and so on). Don't overdo it — six dimensions is a good number. The traits you choose should be as independent as possible from each other, and you should feel that you can assess them reliably by asking a few factual questions. Next, make a list of questions for each trait and think about how you will score it, say on a 1-5 scale. You should have an idea of what you will call “very weak” or “very strong.”
These preparations should take you half an hour or so, a small investment that can make a significant difference in the quality of the people you hire. To avoid halo effects, you must collect the information one at a time, scoring each before you move on to the next one. Do not skip around. To evaluate each candidate, add up the six scores. […] Firmly resolve that you will hire the candidate whose final score is the highest, even if there is another one whom you like better–try to resit your wish to invent broken legs to change the ranking. A vast amount of research offers a promise: you are much more likely to find the best candidate if you use this procedure than if you do what people normally do in such situations, which is to go into the interview unprepared and to make choices by an overall intuitive judgment such as “I looked into his eyes and liked what I saw.”
In the battle of man vs algorithm, unfortunately, man often loses. The promise of Artificial Intelligence is just that. So if we're going to be smart humans, we must learn to be humble in situations where our intuitive judgment simply is not as good as a set of simple rules.
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.
Let me give you an example.
A high-level takeaway from reading Kahneman’s Thinking Fast, and Slow would be that we are subject to something he and Amos Tversky call the Representativeness Heuristic. We create models of things in our head, and then fit our real-world experiences to the model, often over-fitting drastically. A very useful idea.
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.
Or take the rise of human species as laid out by Yuval Harari. It’s great to post on his theory; how myths laid the foundation for our success, how “natural” is probably a useless concept the way it’s typically used, and how biology is the great enabler.
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.
The great difficulty, of course, is that we lack the time to dig deep into everything. Opportunity costs and trade-offs are quite real.
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.
“The premise of this book is that it is easier to recognize other people's mistakes than our own.”
A reliable way to make people believe in falsehoods is frequent repetition, because familiarity is not easily distinguished from truth. Authoritarian institutions and marketers have always known this fact. But it was psychologists who discovered that you do not have to repeat the entire statement of a fact or idea to make it appear true. People who were repeatedly exposed to the phrase “the body temperature of a chicken” were more likely to accept as true the statement that “the body temperature of a chicken is 144°” (or any other arbitrary number). The familiarity of one phrase in the statement sufficed to make the whole statement feel familiar, and therefore true. If you cannot remember the source of a statement, and have no way to relate it to other things you know, you have no option but to go with the sense of cognitive ease.
This is due, in part, to the fact that repetition causes familiarity and familiarity distorts our thinking.
People tend to assess the relative importance of issues by the ease with which they are retrieved from memory—and this is largely determined by the extent of coverage in the media. Frequently mentioned topics populate the mind even as others slip away from awareness. In turn, what the media choose to report corresponds to their view of what is currently on the public's mind. It is no accident that authoritarian regimes exert substantial pressure on independent media. Because public interest is most easily aroused by dramatic events and by celebrities, media feeding frenzies are common. For several weeks after Michael Jackson's death, for example, it was virtually impossible to find a television channel reporting on another topic. In contrast, there is little coverage of critical but unexciting issues that provide less drama, such as declining educational standards or overinvestment of medical resources in the last year of life. (As I write this, I notice that my choice of “little-covered” examples was guided by availability. The topics I chose as examples are mentioned often; equally important issues that are less available did not come to my mind.)
Daniel Kahneman and Amos Tversky spent decades in psychology research to disentangle patterns in errors of human reasoning. Over the course of their work they discovered a variety of logical fallacies that we tend to make, when facing information that appears vaguely familiar. These fallacies lead to bias – irrational behavior based on beliefs that are not always grounded in reality.
In his book Thinking Fast and Slow, which summarizes his and Tversky’s life work, Kahneman introduces biases that stem from the conjunction fallacy – the false belief that a conjunction of two events is more probable than one of the events on its own.
Probability can be a difficult concept. Most of us have an intuitive understanding of what probability is, but there is little consensus on what it actually means. It is just as vague and subjective a concept as democracy, beauty or freedom. However, this is not always troublesome – we can still easily discuss the notion with others. Kahneman reflects:
In all the years I spent asking questions about the probability of events, no one ever raised a hand to ask me, “Sir, what do you mean by probability?” as they would have done if I had asked them to assess a strange concept such as globability.
Everyone acted as if they knew how to answer my questions, although we all understood that it would be unfair to ask them for an explanation of what the word means.
While logicians and statisticians might disagree, probability to most of us is simply a tool that describes our degree of belief. For instance, we know that the sun will rise tomorrow and we consider it near impossible that there will be two suns up in the sky instead of one. In addition to the extremes, there are also events which lie somewhere in the middle on the probability spectrum, such as the degree of belief that it will rain tomorrow.
Despite its vagueness, probability has its virtues. Assigning probabilities helps us make the degree of belief actionable and also communicable to others. If we believe that the probability it will rain tomorrow is 90%, we are likely to carry an umbrella and suggest our family do so as well.
Most of us are already familiar with representativeness and base rates. Consider the classic example of x number of black and y number of white colored marbles in a jar. It is a simple exercise to tell what the probabilities of drawing each color are if you know their base rates (proportion). Using base rates is the obvious approach for estimations when no other information is provided.
However, Kahneman managed to prove that we have a tendency to ignore base rates in light of specific descriptions. He calls this phenomenon the Representativeness Bias. To illustrate representativeness bias, consider the example of seeing a person reading The New York Times on the New York subway. Which do you think would be a better bet about the reading stranger?
1) She has a PhD.
2) She does not have a college degree.
Representativeness would tell you to bet on the PhD, but this is not necessarily a good idea. You should seriously consider the second alternative, because many more non-graduates than PhDs ride in New York subways. While a larger proportion of PhDs may read The New York Times, the total number of New York Times readers with only high school degrees is likely to be much larger, even if the proportion itself is very slim.
In a series of similar experiments, Kahneman’s subjects failed to recognize the base rates in light of individual information. This is unsurprising. Kahneman explains:
On most occasions, people who act friendly are in fact friendly. A professional athlete who is very tall and thin is much more likely to play basketball than football. People with a PhD are more likely to subscribe to The New York Times than people who ended their education after high school. Young men are more likely than elderly women to drive aggressively.
While following representativeness bias might improve your overall accuracy, it will not always be the statistically optimal approach.
Michael Lewis in his bestseller Moneyball tells a story of Oakland A’s baseball team coach, Billy Beane, who recognized this fallacy and used it to his advantage. When recruiting new players for the team, instead of relying on scouts he relied heavily on statistics of past performance. This approach allowed him to build a team of great players that were passed up by other teams because they did not look the part. Needless to say, the team achieved excellent results at a low cost.
While representativeness bias occurs when we fail to account for low base rates, conjunction fallacy occurs when we assign a higher probability to an event of higher specificity. This violates the laws of probability.
Consider the following study:
Participants were asked to rank four possible outcomes of the next Wimbledon tournament from most to least probable. Björn Borg was the dominant tennis player of the day when the study was conducted. These were the outcomes:
A. Borg will win the match.
B. Borg will lose the first set.
C. Borg will lose the first set but win the match.
D. Borg will win the first set but lose the match.
How would you order them?
Kahneman was surprised to see that most subjects ordered the chances by directly contradicting the laws of logic and probability. He explains:
The critical items are B and C. B is the more inclusive event and its probability must be higher than that of an event it includes. Contrary to logic, but not to representativeness or plausibility, 72% assigned B a lower probability than C.
If you thought about the problem carefully you drew the following diagram in your head. Losing the first set will always, by definition, be a more probable event than losing the first set and winning the match.
As discussed in our piece on the Narrative Fallacy, the best-known and most controversial of Kahneman and Tversky’s experiments involved a fictitious lady called Linda. The fictional character was created to illustrate the role heuristics play in our judgement and how it can be incompatible with logic. This is how they described Linda.
Linda is thirty-one years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations.
Kahneman conducted a series of experiments, in which he showed that representativeness tends to cloud our judgements and that we ignore the base rates in light of stories. The Linda problem started off with the task to estimate the plausibility of 9 different scenarios that subjects were supposed to rank in order of likelihood.
Linda is a teacher in elementary school.
Linda works in a bookstore and takes yoga classes.
Linda is active in the feminist movement.
Linda is a psychiatric social worker.
Linda is a member of the League of Women Voters.
Linda is a bank teller.
Linda is an insurance salesperson.
Linda is a bank teller and is active in the feminist movement.
Kahneman was startled to see that his subjects judged the likelihood of Linda being a bank teller and a feminist more likely than her being just a bank teller. As explained earlier, doing so makes little sense. He went on to explore the phenomenon further:
In what we later described as “increasingly desperate” attempts to eliminate the error, we introduced large groups of people to Linda and asked them this simple question:
Which alternative is more probable?
Linda is a bank teller.
Linda is a bank teller and is active in the feminist movement.
This stark version of the problem made Linda famous in some circles, and it earned us years of controversy. About 85% to 90% of undergraduates at several major universities chose the second option, contrary to logic.
What is especially interesting about these results is that, even when aware of the biases in place, we do not discard them.
When I asked my large undergraduate class in some indignation, “Do you realize that you have violated an elementary logical rule?” someone in the back row shouted, “So what?” and a graduate student who made the same error explained herself by saying, “I thought you just asked for my opinion.”
The issue is not constrained to students and but also affects professionals.
The naturalist Stephen Jay Gould described his own struggle with the Linda problem. He knew the correct answer, of course, and yet, he wrote, “a little homunculus in my head continues to jump up and down, shouting at me—‘but she can’t just be a bank teller; read the description.”
Our brains simply seem to prefer consistency over logic.
Representativeness and conjunction fallacy occur, because we make the mental shortcut from our perceived plausibility of a scenario to its probability.
The most coherent stories are not necessarily the most probable, but they are plausible, and the notions of coherence, plausibility, and probability are easily confused by the unwary. Representativeness belongs to a cluster of closely related basic assessments that are likely to be generated together. The most representative outcomes combine with the personality description to produce the most coherent stories.
Kahneman warns us about the effects of these biases on our perception of expert opinion and forecasting. He explains that we are more likely to believe scenarios that are illustrative rather than probable.
The uncritical substitution of plausibility for probability has pernicious effects on judgments when scenarios are used as tools of forecasting. Consider these two scenarios, which were presented to different groups, with a request to evaluate their probability:
A massive flood somewhere in North America next year, in which more than 1,000 people drown
An earthquake in California sometime next year, causing a flood in which more than 1,000 people drown
The California earthquake scenario is more plausible than the North America scenario, although its probability is certainly smaller. As expected, probability judgments were higher for the richer and more detailed scenario, contrary to logic. This is a trap for forecasters and their clients: adding detail to scenarios makes them more persuasive, but less likely to come true.
In order to appreciate the role of plausibility, he suggests we have a look at an example without an accompanying explanation.
Which alternative is more probable?
Jane is a teacher.
Jane is a teacher and walks to work.
In this case, when evaluating plausibility and coherence there are no quick answers to the probability question and we can easily conclude that the first one is more likely. The rule goes that in the absence of a competing intuition, logic prevails.
The first lesson to thinking clearly is to question how you think. We should not simply believe whatever comes to our mind – our beliefs must be constrained by logic. You don’t have to become an expert in probability to tame your intuition, but having a grasp of simple concepts will help. There are two main rules that are worth repeating in light of representativeness bias:
1) All probabilities add up to 100%.
This means that if you believe that there’s a 90% chance it will rain tomorrow, there’s a 10% of chance that it will not rain tomorrow.
However, since you believe that there is only 90% chance that it will rain tomorrow, you cannot be 95% certain that it will rain tomorrow morning.
We typically make this type of error, when we mean to say that, if it rains, there’s 95% probability it will happen in the morning. That’s a different claim and the probability of raining tomorrow morning under such premises is 0.9*0.95=85.5%.
This also means the odds that, if it rains, it will not rain in the morning, are 90.0%-85.5% = 4.5%.
2) The second principle is called the Bayes rule.
It allows us to correctly adjust our beliefs with the diagnosticity of the evidence. Bayes rule follows the formula:
In essence the formula states that the posterior odds are proportional to prior odds times the likelihood. Kahneman crystallizes two keys to disciplined Bayesian reasoning:
• Anchor your judgment of the probability of an outcome on a plausible base rate.
• Question the diagnosticity of your evidence.
Kahnmenan explains it with an example:
If you believe that 3% of graduate students are enrolled in computer science (the base rate), and you also believe that the description of Tom is 4 times more likely for a graduate student in computer science than in other fields, then Bayes’s rule says you must believe that the probability that Tom is a computer science student is now 11%.
Four times as likely means that we expect roughly 80% of all computer science students to resemble Tom. We use this proportion to obtain the adjusted odds. (The calculation goes as follows: 0.03*0.8/(0.03*0.8+((1-0.03)*(1-0.8)))=11%)
The easiest way to become better at making decisions is by making sure you question your assumptions and follow strong evidence. When evidence is anecdotal, adjust minimally and trust the base rates. Odds are, you will be pleasantly surprised.
Want More? Check out our ever-growing collection of mental models and biases and get to work.
Ever want to be the fly on the wall for a fascinating conversation. Well, here's your chance. Santa Fe Institute Board of Trustees Chair Michael Mauboussin interviews Nobel Prize winner Daniel Kahneman. The wide-ranging conversation talks about disciplined intuition, causality, base rates, loss aversion and so much more. You don't want to miss this.
Here's an excerpt from Kahneman I think you'll enjoy. You can read the entire transcript here.
The Sources of Power is a very eloquent book on expert intuition with magnificent examples, and so he is really quite hostile to my point of view, basically.
We spent years working on that, on the question of when can intuitions be trusted? What's the boundary between trustworthy and untrustworthy intuitions?
I would summarize the answer as saying there is one thing you should not do. People's confidence in their intuition is not a good guide to their validity. Confidence is something else entirely, and maybe we can talk about confidence separately later, but confidence is not it.
What there is, if you want to know whether you can trust intuition, it really is like deciding on a painting, whether it's genuine or not. You can look at the painting all you want, but asking about the provenance is usually the best guide about whether a painting is genuine or not.
Similarly for expertise and intuition, you have to ask not how happy the individual is with his or her own intuitions, but first of all, you have to ask about the domain. Is the domain one where there is enough regularity to support intuitions? That's true in some medical domains, it certainly is true in chess, it is probably not true in stock picking, and so there are domains in which intuition can develop and others in which it cannot. Then you have to ask whether, if it's a good domain, one in which there are regularities that can be picked up by the limited human learning machine. If there are regularities, did the individual have an opportunity to learn those regularities? That primarily has to do with the quality of the feedback.
Those are the questions that I think should be asked, so there is a wide domain where intuitions can be trusted, and they should be trusted, and in a way, we have no option but to trust them because most of the time, we have to rely on intuition because it takes too long to do anything else.
Then there is a wide domain where people have equal confidence but are not to be trusted, and that may be another essential point about expertise. People typically do not know the limits of their expertise, and that certainly is true in the domain of finances, of financial analysis and financial knowledge. There is no question that people who advise others about finances have expertise about finance that their advisees do not have. They know how to look at balance sheets, they understand what happens in conversations with analysts.
There is a great deal that they know, but they do not really know what is going to happen to a particular stock next year. They don't know that, that is one of the typical things about expert intuition in that we know domains where we have it, there are domains where we don't, but we feel the same confidence and we do not know the limits of our expertise, and that sometimes is quite dangerous.
John Pollack is a former Presidential Speechwriter. If anyone knows the power of words to move people to action, shape arguments, and persuade, it is he.
In Shortcut: How Analogies Reveal Connections, Spark Innovation, and Sell Our Greatest Ideas, he explores the powerful role of analogy in persuasion and creativity.
One of the key tools he uses for this is analogy.
While they often operate unnoticed, analogies aren’t accidents, they’re arguments—arguments that, like icebergs, conceal most of their mass and power beneath the surface. In arguments, whoever has the best argument wins.
But analogies do more than just persuade others — they also play a role in innovation and decision making.
From the bloody Chicago slaughterhouse that inspired Henry Ford’s first moving assembly line, to the “domino theory” that led America into the Vietnam War, to the “bicycle for the mind” that Steve Jobs envisioned as a Macintosh computer, analogies have played a dynamic role in shaping the world around us.
Despite their importance, many people have only a vague sense of the definition.
In broad terms, an analogy is simply a comparison that asserts a parallel—explicit or implicit—between two distinct things, based on the perception of a share property or relation. In everyday use, analogies actually appear in many forms. Some of these include metaphors, similes, political slogans, legal arguments, marketing taglines, mathematical formulas, biblical parables, logos, TV ads, euphemisms, proverbs, fables and sports clichés.
Because they are so disguised they play a bigger role than we consciously realize. Not only do analogies effectively make arguments, but they trigger emotions. And emotions make it hard to make rational decisions.
While we take analogies for granted, the ideas they convey are notably complex.
All day every day, in fact, we make or evaluate one analogy after the other, because some comparisons are the only practical way to sort a flood of incoming data, place it within the content of our experience, and make decisions accordingly.
Remember the powerful metaphor — that arguments are war. This shapes a wide variety of expressions like “your claims are indefensible,” “attacking the weakpoints,” and “You disagree, OK shoot.”
Or consider the Map and the Territory — Analogies give people the map but explain nothing of the territory.
Warren Buffett is one of the best at using analogies to communicate effectively. One of my favorite analogies is when he noted “You never know who’s swimming naked until the tide goes out.” In other words, when times are good everyone looks amazing. When times suck, hidden weaknesses are exposed. The same could be said for analogies:
We never know what assumptions, deceptions, or brilliant insights they might be hiding until we look beneath the surface.
Most people underestimate the importance of a good analogy. As with many things in life, this lack of awareness comes at a cost. Ignorance is expensive.
Evidence suggests that people who tend to overlook or underestimate analogy’s influence often find themselves struggling to make their arguments or achieve their goals. The converse is also true. Those who construct the clearest, most resonant and apt analogies are usually the most successful in reaching the outcomes they seek.
The key to all of this is figuring out why analogies function so effectively and how they work. Once we know that, we should be able to craft better ones.
Effective, persuasive analogies frame situations and arguments, often so subtly that we don’t even realize there is a frame, let alone one that might not work in our favor. Such conceptual frames, like picture frames, include some ideas, images, and emotions and exclude others. By setting a frame, a person or organization can, for better or worse, exert remarkable influence on the direction of their own thinking and that of others.
He who holds the pen frames the story. The first person to frame the story controls the narrative and it takes a massive amount of energy to change the direction of the story. Sometimes even the way that people come across information, shapes it — stories that would be a non-event if disclosed proactively became front page stories because someone found out.
In Don’t Think of an Elephant, George Lakoff explores the issue of framing. The book famously begins with the instruction “Don’t think of an elephant.”
What’s the first thing we all do? Think of an elephant, of course. It’s almost impossible not to think of an elephant. When we stop consciously thinking about it, it floats away and we move on to other topics — like the new email that just arrived. But then again it will pop back into consciousness and bring some friends — associated ideas, other exotic animals, or even thoughts of the GOP.
“Every word, like elephant, evokes a frame, which can be an image of other kinds of knowledge,” Lakoff writes. This is why we want to control the frame rather than be controlled by it.
In Shortcut Pollack tells of Lakoff talking about an analogy that President George W. Bush made in the 2004 State of the Union address, in which he argued the Iraq war was necessary despite the international criticism. Before we go on, take Bush’s side here and think about how you would argue this point – how would you defend this?
In the speech, Bush proclaimed that “America will never seek a permission slip to defend the security of our people.”
As Lakoff notes, Bush could have said, “We won’t ask permission.” But he didn’t. Instead he intentionally used the analogy of permission slip and in so doing framed the issue in terms that would “trigger strong, more negative emotional associations that endured in people’s memories of childhood rules and restrictions.”
Commenting on this, Pollack writes:
Through structure mapping, we correlate the role of the United States to that of a young student who must appeal to their teacher for permission to do anything outside the classroom, even going down the hall to use the toilet.
But is seeking diplomatic consensus to avoid or end a war actually analogous to a child asking their teacher for permission to use the toilet? Not at all. Yet once this analogy has been stated (Farnam Street editorial: and tweeted), the debate has been framed. Those who would reject a unilateral, my-way-or-the-highway approach to foreign policy suddenly find themselves battling not just political opposition but people’s deeply ingrained resentment of childhood’s seemingly petty regulations and restrictions. On an even subtler level, the idea of not asking for a permission slip also frames the issue in terms of sidestepping bureaucratic paperwork, and who likes bureaucracy or paperwork.
Deconstructing analogies, we find out how they function so effectively. Pollack argues they meet five essential criteria.
Let’s explore how these work in greater detail. Let’s use the example of master-thief, Bruce Reynolds, who described the Great Train Robbery as his Sistine Chapel.
In the dark early hours of August 8, 1963, an intrepid gang of robbers hot-wired a six-volt battery to a railroad signal not far from the town of Leighton Buzzard, some forty miles north of London. Shortly, the engineer of an approaching mail train, spotting the red light ahead, slowed his train to a halt and sent one of his crew down the track, on foot, to investigate. Within minutes, the gang overpowered the train’s crew and, in less than twenty minutes, made off with the equivalent of more than $60 million in cash.
Years later, Bruce Reynolds, the mastermind of what quickly became known as the Great Train Robbery, described the spectacular heist as “my Sistine Chapel.”
Use the familiar to explain something less familiar
Reynolds exploits the public’s basic familiarity with the famous chapel in the Vatican City, which after Leonardo da Vinci’s Mona Lisa is perhaps the best-known work of Renaissance art in the world. Millions of people, even those who aren’t art connoisseurs, would likely share the cultural opinion that the paintings in the chapel represent “great art” (as compared to a smaller subset of people who might feel the same way about Jackson Pollock’s drip paintings, or Marcel Duchamp’s upturned urinal).
Highlight similarities and obscure differences
Reynold’s analogy highlights, through implication, similarities between the heist and the chapel—both took meticulous planning and masterful execution. After all, stopping a train and stealing the equivalent of $60m—and doing it without guns—does require a certain artistry. At the same time, the analogy obscures important differences. By invoking the image of a holy sanctuary, Reynolds triggers a host of associations in the audience’s mind—God, faith, morality, and forgiveness, among others—that camouflage the fact that he’s describing an action few would consider morally commendable, even if the artistry involved in robbing that train was admirable.
Identify useful abstractions
The analogy offers a subtle but useful abstraction: Genius is genius and art is art, no matter what the medium. The logic? If we believe that genius and artistry can transcend genre, we must concede that Reynolds, whose artful, ingenious theft netted millions, is an artist.
Tell a coherent story
The analogy offers a coherent narrative. Calling the Great Train Robbery his Sistine Chapel offers the audience a simple story that, at least on the surface makes sense: Just as Michelangelo was called by God, the pope, and history to create his greatest work, so too was Bruce Reynolds called by destiny to pull off the greatest robbery in history. And if the Sistine Chapel endures as an expression of genius, so too must the Great Train Robbery. Yes, robbing the train was wrong. But the public perceived it as largely a victimless crime, committed by renegades who were nothing if not audacious. And who but the most audacious in history ever create great art? Ergo, according to this narrative, Reynolds is an audacious genius, master of his chosen endeavor, and an artist to be admired in public.
There is an important point here. The narrative need not be accurate. It is the feelings and ideas the analogy evokes that make it powerful. Within the structure of the analogy, the argument rings true. The framing is enough to establish it succulently and subtly. That’s what makes it so powerful.
The analogy resonates emotionally. To many people, mere mention of the Sistine Chapel brings an image to mind, perhaps the finger of Adam reaching out toward the finger of God, or perhaps just that of a lesser chapel with which they are personally familiar. Generally speaking, chapels are considered beautiful, and beauty is an idea that tends to evoke positive emotions. Such positive emotions, in turn, reinforce the argument that Reynolds is making—that there’s little difference between his work and that of a great artist.
Daniel Kahneman explains the two thinking structures that govern the way we think: System one and system two . In his book, Thinking Fast and Slow, he writes “Jumping to conclusions is efficient if the conclusions are likely to be correct and the costs of an occasional mistake are acceptable, and if the jump saves much time and effort.”
“A good analogy serves as an intellectual springboard that helps us jump to conclusions,” Pollack writes. He continues:
And once we’re in midair, flying through assumptions that reinforce our preconceptions and preferences, we’re well on our way to a phenomenon known as confirmation bias. When we encounter a statement and seek to understand it, we evaluate it by first assuming it is true and exploring the implications that result. We don’t even consider dismissing the statement as untrue unless enough of its implications don’t add up. And consider is the operative word. Studies suggest that most people seek out only information that confirms the beliefs they currently hold and often dismiss any contradictory evidence they encounter.
The ongoing battle between fact and fiction commonly takes place in our subconscious systems. In The Political Brain: The Role of Emotion in Deciding the Fate of the Nation, Drew Westen, an Emory University psychologist, writes: “Our brains have a remarkable capacity to find their way toward convenient truths—even if they are not all true.”
This also helps explain why getting promoted has almost nothing to do with your performance.
Remember Apollo Robbins? He’s a professional pickpocket. While he has unique skills, he succeeds largely through the choreography of people’s attention. “Attention,” he says “is like water. It flows. It’s liquid. You create channels to divert it, and you hope that it flows the right way.”
“Pickpocketing and analogies are in a sense the same,” Pollack concludes, “as the misleading analogy picks a listener’s mental pocket.”
And this is true whether someone else diverts our attention through a resonant but misleading analogy—“Judges are like umpires”—or we simply choose the wrong analogy all by ourselves.
We rarely stop to see how much of our reasoning is done by analogy. In a 2005 study published in the Harvard Business Review, Giovanni Gavettie and Jan Rivkin wrote: “Leaders tend to be so immersed in the specifics of strategy that they rarely stop to think how much of their reasoning is done by analogy.” As a result they miss things. They make connections that don’t exist. They don’t check assumptions. They miss useful insights. By contrast “Managers who pay attention to their own analogical thinking will make better strategic decisions and fewer mistakes.”
Shortcut goes on to explore when to use analogies and how to craft them to maximize persuasion.