Being Wrong Less

Being Wrong Less

You make dozens of decisions every day. And when you make those decisions, whether they are personal or professional, you want to be right much more often than you are wrong. However, consistently being right more often is hard to do because the world is a complex, ever-evolving place. You are steadily faced with unfamiliar situations, usually with a large array of choices. The right answer may be apparent only in hindsight, if it ever becomes clear at all.

Carl Jacobi was a nineteenth-century German mathematician who often used to say, “Invert, always invert” (actually he said, “ Man muss immer umkehren ,” because English wasn’t his first language). He meant that thinking about a problem from an inverse perspective can unlock new solutions and strategies. For example, most people approach investing their money from the perspective of making more money; the inverse approach would be investing money from the perspective of not losing money.

Or consider healthy eating. A direct approach would be to try to construct a healthy diet, perhaps by making more food at home with controlled ingredients. An inverse approach, by contrast, would be to try to avoid unhealthy options. You might still go to all the same eating establishments but simply choose the healthier options when there.

The concept of inverse thinking can help you with the challenge of making good decisions. The inverse of being right more is being wrong less. Mental models are a tool set that can help you be wrong less. They are a collection of concepts that help you more effectively navigate our complex world. Mental models come from specific disciplines, but many also prove useful outside their original field. If you can use these mental models to help you make decisions as events unfold before you, they can help you be wrong less often.

Let us offer an example from the world of sports. In tennis, an unforced error occurs when a player makes a mistake not because the other player hit an awesome shot, but rather because of their own poor judgment or execution. For example, hitting an easy ball into the net is one kind of unforced error. To be wrong less in tennis, you need to make fewer unforced errors on the court. And to be consistently wrong less in decision making, you consistently need to make fewer unforced errors in your own life. An unforced error is a useful metaphor for any avoidable mistake. There are unforced errors in baking (using a tablespoon instead of a teaspoon) or dating (making a bad first impression) or decision making (not considering all your options). Start looking for unforced errors around you and you will see them everywhere.

An unforced error isn’t the only way to make a wrong decision, though. The best decision based on the information available at the time can easily turn out to be the wrong decision in the long run. That’s just the nature of dealing with uncertainty. No matter how hard you try, because of uncertainty, you may still be wrong when you make decisions, more frequently than you’d like. What you can do, however, is strive to make fewer unforced errors over time by using sound judgment and techniques to make the best decision at any given time.

Another useful mental model is antifragility, explored in Nassim Nicholas Taleb’s book Antifragile. Just as it pays off to make your financial portfolio antifragile in the face of economic shocks, it similarly pays off to make your thinking antifragile in the face of new decisions. If your thinking is antifragile, then it gets better over time as you learn from your mistakes and interact with your surroundings. It’s like working out at the gym—you are shocking your muscles and bones so they grow stronger over time.

The aim is to learn which models fit which situations, then practice using them. You don’t have to be an expert at tennis or financial analysis to benefit from these concepts. You just need to understand their broader meaning and apply them when appropriate. These models can help you catch recurring errors in judgment. Recognizing a bias does not eliminate it, but it gives you a chance to check your reasoning before acting.

Keep It Simple, Stupid!

Any science or math teacher worth their salt stresses the importance of knowing how to derive every formula that you use, because only then do you really know it. It’s the difference between being able to attack a math problem with a blank sheet of paper and needing a formula handed to you to begin with. It’s also the difference between being a chef—someone who can take ingredients and turn them into an amazing dish without looking at a cookbook—and being the kind of cook who just knows how to follow a recipe.

Lauren was the teaching assistant for several statistics courses during her years at MIT. One course had a textbook that came with a computer disk, containing a simple application that could be used as a calculator for the statistical formulas in the book. On one exam, a student wrote the following answer to one of the statistical problems posed: “I would use the disk and plug the numbers in to get the answer.” The student was not a chef.

The central mental model to help you become a chef with your thinking is arguing from first principles . It’s the practical starting point to being wrong less, and it means thinking from the bottom up, using basic building blocks of what you think is true to build sound (and sometimes new) conclusions. First principles are the basic assumptions on which your conclusions rest—the ingredients in a recipe or the axioms underpinning a formula. In practical decisions, those assumptions may themselves need testing. Given a set of ingredients, a chef can adapt and create new recipes, as on Chopped . If you can argue from first principles, then you can do the same thing when making decisions, coming up with novel solutions to hard problems.

Think MacGyver, or the true story depicted in the movie Apollo 13 (which you should watch if you haven’t), where a malfunction on board the spacecraft necessitated an early return to Earth and the creation of improvised devices to make sure, among other things, that there was enough usable air for the astronauts to breathe on the trip home. NASA engineers figured out a solution using only the “ingredients” on the ship. In the movie, an engineer dumps all the parts available on the spacecraft on a table and says, “We’ve got to find a way to make this [holding up square canister] fit into the hole for this [holding up round canister] using nothing but that [pointing to parts on the table].” If you can argue from first principles, then you can more easily approach unfamiliar situations, or approach familiar situations in innovative ways. Understanding how to derive formulas helps you to understand how to derive new formulas. Understanding how molecules fit together enables you to build new molecules.

When arguing from first principles, you are deliberately starting from scratch. You are explicitly avoiding the potential trap of conventional wisdom, which could turn out to be wrong. Even if you end up in agreement with conventional wisdom, by taking the first-principles approach, you will gain a much deeper understanding of the subject at hand.

Any problem can be approached from first principles. Take your next career move. It is easy to apply widely and accept the first offer without first deciding what you want from a job. When using first principles, you’ll instead begin by thinking about what you truly value in a career (e.g., autonomy, status, mission, etc.), your required job parameters (financial, location, title, etc.), and your previous experience. When you add those up, you will get a much better picture of what might work best for your next career move, and then you can actively seek that out.

Thinking alone, though, even from first principles, only gets you so far. Your first principles are merely assumptions that may be true, false, or somewhere in between. Do you really value autonomy in a job, or do you just think you do? Is it really true you need to go back to school to switch careers, or might it actually be unnecessary? Ultimately, to be wrong less, you also need to be testing your assumptions in the real world, a process known as de-risking . There is risk that one or more of your assumptions are untrue, and so the conclusions you reach could also be false.

A startup, for example, may assume that customers have a particular problem, that its product solves it, and that customers will pay enough to sustain the business. Break each of those assumptions into more specific claims you can test. Once you get specific enough with your assumptions, then you can devise a plan to test (de-risk) them.

The most important assumptions to de-risk first are the ones that are necessary conditions for success and that you are most uncertain about. For example, in the startup context, take the assumption that your solution sufficiently solves the problem it was designed to solve. If this assumption is untrue, then you will need to change what you are doing immediately before you can proceed any further, because the whole endeavor won’t work otherwise.

Once you identify the critical assumptions to de-risk, the next step is actually going out and testing these assumptions, weighing the evidence, and adjusting your strategy.

Just as the concept of first principles is universally applicable, so is de-risking. You can de-risk anything: a policy idea, a vacation plan, a workout routine. When de-risking, you want to test assumptions quickly and easily. Take a vacation plan. Assumptions could be around cost (“I can afford this vacation”), satisfaction (“I will enjoy this vacation”), coordination (“my relatives can join me on this vacation”), etc. Here, de-risking is as easy as doing a few minutes of online research, reading reviews, and sending an email to your relatives.

Unfortunately, people often make the mistake of doing way too much work before testing assumptions in the real world. In computer science this trap is called premature optimization , where you tweak or perfect code or algorithms ( optimize ) too early ( prematurely ). If your assumptions turn out to be wrong, you’re going to have to throw out all that work, rendering it ultimately a waste of time. It’s as if you booked an entire vacation assuming your family could join you, only to finally ask them and they say they can’t come. Then you have to go back and change everything, but all this work could have been avoided by a simple communication up front.

Back in startup land, there is another mental model to help you test your assumptions, called minimum viable product , or MVP . The MVP is the product you are developing with just enough features, the minimum amount, to be feasibly, or viably , tested by real people. The MVP keeps you from working by yourself for too long. LinkedIn cofounder Reid Hoffman put it like this: “If you’re not embarrassed by the first version of your product, you’ve launched too late.”

As with many useful mental models, you will frequently be reminded of the MVP now that you are familiar with it. An oft-quoted military adage says: “No battle plan survives contact with the enemy.” And boxer Mike Tyson (prior to his 1996 bout against Evander Holyfield): “Everybody has a plan until they get punched in the mouth.” No matter the context, what they’re all saying is that your first plan is probably wrong. While it is the best starting point you have right now, you must revise it often based on the real-world feedback you receive. And we recommend doing as little work as possible before getting that real-world feedback.

As with de-risking, you can extend the MVP model to fit many other contexts: minimum viable organization, minimum viable communication, minimum viable strategy, minimum viable experiment. A product vision can extend well beyond the MVP; later versions, such as MVP 2.0, can incorporate what early tests reveal.

The MVP forces you to evaluate your assumptions quickly. One way you can be wrong with your assumptions is by coming up with too many or too complicated assumptions up front when there are clearly simpler sets you can start with. Ockham’s razor helps here. When competing explanations account for the evidence equally well, start by investigating the one that requires fewer assumptions.

This model is a razor because it “shaves off” unnecessary assumptions. It’s named after fourteenth-century English philosopher William of Ockham, though the underlying concept has much older roots. The Greco-Roman astronomer Ptolemy ( circa A.D. 90–168) stated, “We consider it a good principle to explain the phenomena by the simplest hypotheses possible.” More recently, the composer Roger Sessions, paraphrasing Albert Einstein, put it like this: “Everything should be made as simple as it can be, but not simpler!” In medicine, it’s known by this saying: “When you hear hoofbeats, think of horses, not zebras.”

A practical tactic is to look at your explanation of a situation, break it down into its constituent assumptions, and for each one, ask yourself: Does this assumption really need to be here? What evidence do I have that it should remain? Is it a false dependency?

For example, Ockham’s razor would be helpful in the search for a long-term romantic partner. We’ve seen firsthand that many people have a long list of extremely specific criteria for their potential mates, enabled by online dating sites and apps. “I will only date a Brazilian man with blue eyes who loves hot yoga and raspberry ice cream, and whose favorite Avengers character is Thor.” However, this approach leads to an unnecessarily small dating pool. If instead people reflected on whom they’ve dated in the past in terms of what underlying characteristics drove their past relationships to fail, a much simpler set of dating criteria would probably emerge. It is usually okay for partners to have more varied cultural backgrounds and looks, and even to prefer different Avengers characters, but they probably do need to make each other think and laugh and find each other attractive. Therefore, a person shouldn’t narrow their dating pool unnecessarily with overly specific criteria. If it turns out that dating someone who doesn’t share their taste in superheroes really does doom the relationship, then they can always add that specific filter back in.

Ockham’s razor is not a “law” in that it is always true; it just offers guidance. Sometimes the true explanation can indeed be quite complicated. However, there is no reason to jump immediately to the complex explanation when you have simpler alternatives to explore first. If you don’t simplify your assumptions, you can fall into a couple of traps, described in our next mental models.

One trap is the conjunction fallacy, studied by Amos Tversky and Daniel Kahneman in the October 1983 Psychological Review. In their well-known Linda problem, a description suggesting an interest in social justice preceded a choice between two claims: Linda is a bank teller, or, in option 2, she is both a bank teller and active in the feminist movement. Most respondents chose the more detailed claim as more probable. Yet adding a condition cannot increase the probability. The fallacy arises because the probability of two events in conjunction is always less than or equal to the probability of either one of the events occurring alone, because the cases satisfying both conditions are a subset of the cases satisfying either condition.

You not only have a natural tendency to think something specific is more probable than something general, but you also have a similarly fallacious tendency to explain data using too many assumptions. The mental model for this second fallacy is overfitting , a concept from statistics. Adding in all those overly specific dating requirements is overfitting your dating history. In statistics, overfitting means fitting the peculiarities or noise of the observed data so closely that the model predicts new observations poorly. A curve passing through every data point may fit the sample better than a straight line while capturing less of the underlying pattern. By analogy, overly specific dating requirements can mistake the quirks of past relationships for reliable rules.

One approach to combatting both traps is to ask yourself: How much does my data really support my conclusion versus other conclusions? Am I ruling out plausible alternatives too quickly? Do I really need the curvy line to explain the data, or would a simple straight line explain just as much?

A pithy mnemonic of this advice and all the advice in this section is KISS: Keep It Simple, Stupid! When crafting a solution to a problem, whether making a decision or explaining data, you want to start with the simplest set of assumptions you can think of and de-risk them as simply as possible.

In the Eye of the Beholder

You go through life seeing everything from your perspective, which varies widely depending on your particular life experiences and current situation. In physics your perspective is called your frame of reference , a concept central to Einstein’s theory of relativity. Here’s an example from everyday life: If you are in a moving train, your reference frame is inside the train, which appears at rest to you, with objects inside the train not moving relative to one another, or to yourself. However, to someone outside the train looking in, you and all the objects in the train are moving at great speed, as seen from their different frame of reference, which is stationary to them. Observers in different reference frames can disagree about motion and even elapsed time. Not every physical quantity changes with the frame.

If you’re trying to be as objective as possible when making a decision or solving a problem, you always want to account for your frame of reference. You will of course be influenced by your perspective, but you don’t want to be unknowingly influenced. And if you think you may not have the full understanding of a situation, then you must actively try to get it by looking from a variety of different frames of reference.

A frame-of-reference mental trap (or useful trick, depending on your perspective) is framing . Framing refers to the way you present a situation or explanation. When you present an important issue to your coworker or family member, you try to frame it in a way that might help them best understand your perspective, setting the stage for a beneficial conversation. For example, if you want your organization to embark on an innovative yet expensive project, you might frame it to your colleagues as a potential opportunity to outshine the competition rather than as an endeavor that would require excessive resources. The latter framing may have it rejected out of hand.

You also need to be aware that family members and coworkers are constantly framing issues for you as well, and your perception of their ideas can vary widely based on how they are framed. When someone presents a new idea or decision to you, take a step back and consider other ways in which it could be framed. If a colleague tells you they are leaving for another job to seek a better opportunity, that may indeed be true, but it also may be true that they want to leave the organization after feeling overlooked. Multiple framings can be valid yet convey vastly different perspectives.

If you visit news sites on the internet, then you probably know all about framing, or at least you should. For example, headlines have a framing effect, affecting the meaning people take away from stories.

On August 31, 2015, three police officers responded to a 911 call about a burglary in progress. Unfortunately, the call did not specify an exact address, and the officers responded to the wrong house. Upon finding the back door unlocked, they entered, and encountered a dog. Gunfire ensued, and the dog, homeowner, and one of the officers were shot, all by officer gunfire. The homeowner and officer survived. A headline emphasizing the mistaken address would direct attention differently from one emphasizing the exchange of gunfire.

In a study by Ullrich Ecker and others, “The Effects of Subtle Misinformation in News Headlines,” presented in the December 2014 issue of the Journal of Experimental Psychology: Applied , students read an article about a small increase in burglary rates over the last year (0.2 percent) that was anomalous in a much larger decline over the past decade (10 percent). The same article came with one of two different headlines: “Number of Burglaries Going Up” or “Downward Trend in Burglary Rate.” The headline affected which facts readers remembered.

A related influence is nudging: subtle wording or environmental cues can steer a choice. Restaurants will nudge you by highlighting certain dishes on menu inserts, by having servers verbally describe specials, or by just putting boxes around certain items. Retail stores and websites nudge you to purchase certain products by placing them where they are easier to see.

Another concept useful in purchasing decisions is anchoring, which describes your tendency to rely too heavily on first impressions when making decisions. You get anchored to the first piece of framing information you encounter. This tendency is commonly exploited by businesses when making offers.

Dan Ariely, behavioral economist and author of Predictably Irrational , brings us an illustrative example of anchoring using subscription offers for The Economist . Readers were offered three ways to subscribe: web only ($59), print only ($125), and print and web ($125). Yes, you read that right: the “print only” version cost the same as the “print and web” version. Who would choose that? In Ariely’s experiment with one hundred MIT students, no one chose print only. Yet including that option made the combined subscription more attractive than it was when only web and combined subscriptions were offered. This is a decoy effect: a clearly inferior option changes the comparison between the others. The reported choices implied about 43 percent more subscription revenue; this was an experimental calculation, rather than a measured increase in the magazine’s sales.

Retailers such as Michaels or Kohl’s have advertised discounts of 40 percent or more. Whether a discount is a bargain depends on the comparison price. A manufacturer’s suggested retail price (MSRP) may be well above the prices at which competitors actually sell the item. Being aware of the MSRP anchors you so that you feel you are getting a good deal at 40 percent off. Compare actual prices before treating the discount as savings.

Anchoring isn’t just for numbers. In his 1987 book Trump: The Art of the Deal, Donald Trump described opening negotiations with extreme demands. Such an opening can anchor later discussion, though it can also lead the other side to walk away.

Availability bias is another way accessible information can distort judgment: events that come readily to mind may seem more common or probable than they are. Framing, anchoring, and decoy effects are related influences, but are not all instances of availability bias. The book used U.S. southern-border apprehensions, then near a five-decade low, to contrast a prominent political topic with a recorded trend. That is a historical comparison. Apprehensions also depend on enforcement and are not a direct count of all unauthorized immigration.

Availability bias can easily emerge from high media coverage of a topic. Rightly or wrongly, the media infamously has a mantra of “If it bleeds, it leads.” The resulting heavy coverage of violent crime causes people to think it occurs more often than it does. The polling company Gallup annually asks Americans about their perception of changing violent crime rates and found in 2014 that “federal crime statistics have not been highly relevant to the public’s crime perceptions in recent years.”

In a famous 1978 study, “Judged Frequency of Lethal Events,” from the Journal of Experimental Psychology , Sarah Lichtenstein and others asked people about forty-one leading causes of death. They found that people often overstate the risk of sensationally over-reported causes of death, like tornados, by fifty times and understate the risk of common causes of death, like stroke, by one hundred times.

Availability bias stems from overreliance on your recent experiences within your frame of reference, at the expense of the big picture. Let’s say you are a manager and you need to write an annual review for your direct report. You are supposed to think critically and objectively about her performance over the entire year. However, it’s easy to be swayed by those really bad or really good contributions over just the past few weeks. Or you might just consider the interactions you have had with her personally, as opposed to getting a more holistic view based on interactions with other colleagues with different frames of reference.

With the rise of personalized recommendations and news feeds on the internet, availability bias has become a more and more pernicious problem. Online this model is called the filter bubble , a term coined by author Eli Pariser, who wrote a book on it with the same name. Recommendation systems may show you more of what you have engaged with before, reinforcing familiarity. Since there are only so many items they can show you—only so many links on page one of the search results—they therefore filter out links they think you are unlikely to click on, such as opposing viewpoints, effectively placing you in a bubble .

In the run-up to the 2012 U.S. presidential election and again in 2018, the search engine DuckDuckGo (founded by Gabriel) conducted studies where individuals searched on Google for the same political topics, such as gun control and climate change. DuckDuckGo reported differences in the results people received for the same topics at the same time, including when they were signed out or using incognito mode. Differences alone do not establish that individual personalization caused them; factors such as location can also affect results. The broader concern is whether a ranking system repeatedly limits exposure to unfamiliar views.

When you put many similar filter bubbles together, you get echo chambers , where the same ideas seem to bounce around the same groups of people, echoing around the collective chambers of these connected filter bubbles. Echo chambers can reinforce partisanship and make a group’s opinions seem more widely shared than they are.

It’s easy to focus solely on what is put in front of you. It’s much harder to seek out an objective frame of reference, but that is what you need to do in order to be wrong less.

Walk a Mile in Their Shoes

Most of the significant problems in the world involve people, so making headway on these problems often requires a deep understanding of the people involved. For instance, enough food is produced to feed everyone on the planet, yet starvation still exists because this food cannot be distributed effectively. Conflict, poverty, and failures of governance can prevent food from reaching people who need it. However, it is very easy to be wrong about other people’s motivations. You may assume they share your perspective or context, think like you do, or have circumstances similar to yours. With such assumptions, you may conclude that they should also behave like you would or hold your beliefs. Unfortunately, often these assumptions are wrong. Consequently, to be wrong less when thinking about people, you must find ways to increase your empathy, opening up a deeper understanding of what other people are really thinking.

In any conflict between two people, there are two sides of the story. Then there is the third story , the story that a third , impartial observer would recount. Forcing yourself to think as an impartial observer can help you in any conflict situation, including difficult business negotiations and personal disagreements. The third story helps separate what both parties could observe from their interpretations. But how do you open yourself up to it? Imagine a complete recording of the situation, and then try to think about what an outside audience would say was happening if they watched or listened to the recording. What story would they tell? How much would they agree with your story?

Authors Douglas Stone, Bruce Patton, and Sheila Heen explore this model in detail in their book Difficult Conversations : “The key is learning to describe the gap—or difference—between your story and the other person’s story. Whatever else you may think and feel, you can at least agree that you and the other person see things differently.”

If you can coherently articulate other points of view, even those directly in conflict with your own, then you will be less likely to make biased or incorrect judgments. You will dramatically increase your empathy—your understanding of other people’s frames of reference—whether or not you agree. Additionally, if you acknowledge the perspective of the third story within difficult conversations, it can have a disarming effect, causing others involved to act less defensively. That’s because you are signaling your willingness and ability to consider an objective point of view. Doing so encourages others involved to do the same.

Another tactical model that can help you empathize is the most respectful interpretation , or MRI . In any situation, you can explain a person’s behavior in many ways. MRI asks you to interpret the other parties’ actions in the most respectful way possible. It’s giving people the benefit of the doubt.

For example, suppose you sent an email to your kid’s school asking for information on the science curriculum for the upcoming year, but haven’t heard back in a few days. Your first interpretation may be that they’re ignoring your request. A more respectful interpretation would be that they are actively working to get back to you but haven’t completed that work yet. Maybe they are just waiting on some crucial information before replying, like a personnel decision that hasn’t been finalized yet, and that is holding up the response.

The point is you don’t know the real answer yet, but if you approach the situation with the most respectful interpretation, then you will generally build trust with those involved rather than destroy it. With MRI, your follow-up email or call is more likely to have an inquisitive tone rather than an accusatory one. Building trust pays dividends over time, especially in difficult situations where that trust can serve as a bridge toward an amicable resolution. The next time you feel inclined to make an accusation, take a step back and think about whether that is really a fair assumption to make.

Using MRI may seem naïve, but like the third story, this model isn’t asking you to give up your point of view. Instead, MRI asks you to approach a situation from a perspective of respect. You remain open to other interpretations and withhold judgment until necessary.

Another way of giving people the benefit of the doubt for their behavior is called Hanlon’s razor : never attribute to malice that which is adequately explained by carelessness. Like Ockham’s razor, Hanlon’s razor seeks out the simplest explanation. Before assuming deliberate harm, check whether carelessness or misunderstanding adequately explains what happened. This is a starting point for inquiry, rather than proof of someone’s intentions.

Hanlon’s razor is especially useful for navigating connections in the virtual world. For example, we have all misread situations online. Since the signals of body language and voice intonation are missing, harmless lines of text can be read in a negative way. Hanlon’s razor says the person probably just didn’t take enough time and care in crafting their message. So the next time you send a message and all you get back is OK , consider that the writer is in a rush or otherwise occupied (the more likely interpretation) instead of coming from a place of dismissiveness.

The third story, most respectful interpretation, and Hanlon’s razor are all attempts to overcome what psychologists call the fundamental attribution error , where you frequently make errors by attributing others’ behaviors to their internal, or fundamental , motivations rather than external factors. You are guilty of the fundamental attribution error whenever you think someone was mean because she is mean rather than thinking she was just having a bad day. A related tendency, actor-observer bias, is to explain your own behavior through circumstances while explaining someone else’s through character. Self-serving bias more specifically concerns crediting yourself for successes and external causes for failures. For example, if someone runs a red light, you often assume that person is inherently reckless; you do not consider that she might be rushing to the hospital for an emergency. On the other hand, you will immediately rationalize your own actions when you drive like a maniac (“I’m in a hurry”).

Another tactical model to help you have greater empathy is the veil of ignorance , put forth by philosopher John Rawls. It holds that when thinking about how society should be organized, we should do so by imagining ourselves ignorant of our particular place in the world, as if there were a veil preventing us from knowing who we are. Rawls refers to this as the “original position . ” For example, you should not just consider your current position as a free person when contemplating a world where slavery is allowed. You must consider the possibility that you might have been born a slave, and how that would feel. Or, when considering policies regarding refugees, you must consider the possibility that you could have been one of those seeking refuge. The veil of ignorance encourages you to empathize with people across a variety of circumstances, so that you can make better moral judgments.

Suppose that, like many companies in recent years, you are considering ending a policy that has allowed your employees to work remotely because you believe that your teams perform better face-to-face. As a manager, it may be easy to imagine changing the policy from your perspective, especially if you personally do not highly value remote working. The veil of ignorance, though, pushes you to imagine the change from the original position, where you could be any employee. What if you were an employee caring for an elderly family member? What if you were a single parent? You may find that the new policy is warranted even after considering its repercussions holistically, but putting on the veil of ignorance helps you appreciate the challenges this might pose for your staff and might even help you come up with creative alternatives.

Speaking of privilege, we (the authors) often say we are lucky to have won the birth lottery . Not only were we not born into slavery, but we were also not born into almost any disadvantaged group. At birth , we were no more deserving of an easier run at life than a child who was born into poverty, or with a disability, or any other type of disadvantage. Yet we are the ones who won this lottery since we do not have these disadvantages.

It can be challenging to acknowledge that a good portion of your success stems from luck. Many people instead choose to believe that the world is completely fair, orderly, and predictable. This view is called the just world hypothesis , where people always get what they deserve, good or bad, because of their actions alone, with no accounting for luck or randomness. This view is summed up as you reap what you sow.

Ironically, belief in a just world can get in the way of actual justice by leading people to victim-blame : The sexual assault victim “should have worn different clothes” or the welfare recipient “is just lazy.” Victims of circumstance are actually blamed for their circumstances, with no accounting for factors of randomness like the birth lottery. The problem with the just world hypothesis and victim-blaming is that they make broad judgments about why things are happening to people that are often inaccurate at the individual level.

You should also keep in mind that the model of learned helplessness can make it hard for some people to strive for improvement without some assistance. Learned helplessness describes the tendency to stop trying to escape difficult situations because we have gotten used to difficult conditions over time. Someone learns that they are helpless to control their circumstances, so they give up trying to change them. In a series of experiments summarized in “Learned Helplessness” in the February 1972 Annual Review of Medicine, psychologist Martin Seligman placed dogs in a box where they were repeatedly shocked at random intervals. Then he placed them in a similar box where they could easily escape the shocks. However, they did not actually try to escape; they simply lay down and waited for the shocks to stop. On the other hand, dogs who were not shocked would quickly jump out of the box. Learned helplessness can be overcome when animals or people see that their actions can make a difference, that they aren’t actually helpless.

Housing First addresses a practical barrier to leaving homelessness: the lack of a stable place to live. The strategy, known as Housing First, involves giving apartments to the chronic homeless and, at the same time, assigning a social worker to help each person reintegrate into society, including finding work and living day-to-day in their apartment. The book reports a reduction in Utah’s chronic homeless population of as much as 72 percent and estimated annual savings of eight thousand dollars per person in hospital, jail, and shelter costs. Those figures describe a particular program and period; they do not establish learned helplessness as the cause of homelessness.

Learned helplessness is not found only in dire situations. People can also exhibit learned helplessness in everyday circumstances, believing they are incapable of doing or learning certain things, such as public speaking or using new technologies. In each of these cases, though, they are probably capable of improving their area of weakness if guided by the right mentor, a topic explored further in Chapter 8 of the book. You don’t want to make a fundamental attribution error by assuming that your colleague is incapable of doing something when they really just need the proper guidance.

All the mental models in this section—from the third story to learned helplessness—can help you increase your empathy. When applying them, you are effectively trying to understand people’s actual circumstances and motivations better, trying as best you can to walk a mile in their shoes .

Progress, One Funeral at a Time

Just as you can be anchored to a price, you can also be anchored to an entire way of thinking about something. In other words, it can be very difficult to convince you of a new idea when a contradictory idea is already entrenched in your thinking. Like many kids in the U.S., our sons are learning “Singapore math,” an approach to arithmetic that includes introducing pictorial steps in order to develop a deeper understanding of basic concepts. Even to mathematically inclined parents, this alternative way of doing arithmetic can feel foreign after so many years of thinking about it another way. Singapore math teaches addition using “number bonds” that break apart numbers so that students can add in groups of ten.

In science, this phenomenon is documented in Thomas Kuhn’s book The Structure of Scientific Revolutions , which popularized the paradigm shift model, describing how accepted scientific theories change over time. Instead of a gradual, evolving progression, Kuhn describes a bumpy, messy process in which initial problems with a scientific theory are either ignored or rationalized away. Eventually so many issues pile up that the scientific discipline in question is thrown into a crisis mode, and the paradigm shifts to a new explanation, entering a new stable era. Essentially, the old guard holds on to the old theories way too long, even in the face of an obvious-in-hindsight alternative. Nobel Prize–winning physicist Max Planck explained it like this in his Scientific Autobiography and Other Papers : “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,” or, more succinctly, “Science progresses one funeral at a time.”

In 1912, Alfred Wegener put forth the theory of continental drift that we know to be true today, in which continents change position over geological time. Wegener noticed that the different continents fit together nicely like a jigsaw puzzle. Upon further study, he found that fossils seemed strikingly similar across continents, as if the continents indeed were put together this way sometime in the past.

We now know this to be the case—all of our continents were previously grouped together into one supercontinent now called Pangea. However, his theory was met with harsh criticism because Wegener was an outsider—a meteorologist by training instead of a geologist—and because he couldn’t offer an explanation of the mechanism causing continental drift, just the idea that it likely had taken place. Continental drift remained controversial for decades. Paleomagnetic evidence later helped revive it, and plate tectonics supplied a broader explanation of the movement. The major theory that held during this time was that there must have been narrow land bridges (called Gondwanian bridges) sometime in the past that allowed the animals to cross between continents, even though there was never any concrete evidence of their existence. Instead of helping to investigate Wegener’s theory (which certainly wasn’t perfect but had promise), geologists chose to hold on to this incorrect land bridge theory until the evidence for continental drift was so overwhelming that a paradigm shift occurred.

The work of Ignaz Semmelweis, a nineteenth-century Hungarian doctor, met a similar fate. He worked at a teaching hospital where doctors routinely handled cadavers and also delivered babies, without appropriately washing their hands in between. The death rate of mothers who gave birth in this part of the hospital was about 10 percent! In another part of the same hospital, where babies were mostly delivered by midwives who did not routinely handle cadavers, the comparable death rate was 4 percent. Semmelweis obsessed about this difference, painstakingly eliminating all variables until he was left with just one: doctors versus midwives. After studying doctor behavior, he concluded that it must be due to their handling of the cadavers and instituted a practice of washing hands with a solution of chlorinated lime. The death rate immediately dropped to match that in the other part of the hospital.

Despite the clear drop in the death rate, his theories were completely rejected by the medical community at large. In part, doctors were offended by the idea that they were killing their patients. Others were so hung up on the perceived deficiencies of Semmelweis’s theoretical explanation that they ignored the empirical evidence that the handwashing was improving mortality. After struggling to get his ideas adopted, Semmelweis suffered a mental health crisis, was admitted to an asylum, and died at the age of forty-seven. It took another twenty years after his death for his ideas about antiseptics to start to take hold, following Louis Pasteur’s unquestionable confirmation of germ theory.

Like Wegener, Semmelweis didn’t fully understand the scientific mechanism that underpinned his theory and crafted an initial explanation that turned out to be somewhat incorrect. However, they both noticed obvious and important empirical truths that should have been investigated by other scientists but were reflexively rejected by these scientists because the suggested explanations were not in line with the conventional thinking of the time. Today, this is known as a Semmelweis reflex .

Individuals still hang on to old theories in the face of seemingly overwhelming evidence—it happens all the time in science and in life in general. The human tendency to gather and interpret new information in a biased way to confirm preexisting beliefs is called confirmation bias . Unfortunately, it’s extremely easy to succumb to confirmation bias. Correspondingly, it is hard to question your own core assumptions. Outsiders may find it easier to question established practices because they have less invested in them. That helps explain the appeal of “fresh eyes” and “outside the box” thinking, though being an outsider is no guarantee of insight. People can be “free thinkers” while still needing to test their ideas.

The backfire effect describes cases in which a correction strengthens the belief it challenges. It should not be assumed to be the usual response to corrective evidence. A belief can also persist after a correction without becoming stronger.

In one 2008 Yale study, pro-choice Democrats were asked to give their opinions of Supreme Court nominee John Roberts before and after hearing an ad claiming he supported “violent fringe groups and a convicted [abortion] clinic bomber.” Unsurprisingly, disapproval went from 56 percent to 80 percent. After participants learned that the ad had been refuted and withdrawn by its creators, disapproval remained at 72 percent. It fell after the correction but did not return to its original level—an example of incomplete correction, rather than backfire.

You may also succumb to holding on to incorrect beliefs because of disconfirmation bias , where you impose a stronger burden of proof on the ideas you don’t want to believe. Daniel Gilbert discussed this asymmetry in his April 16, 2006, New York Times article, “I’m O.K., You’re Biased”.

The pernicious effects of confirmation bias and related models can be explained by cognitive dissonance , the stress felt by holding two contradictory, dissonant , beliefs at once. Scientists have actually linked cognitive dissonance to a physical area in the brain that plays a role in helping you avoid aversive outcomes. Instead of dealing with the underlying cause of this stress—the fact that we might actually be wrong—we take the easy way out and rationalize the conflicting information away. Look for occasions when you dismiss evidence more readily because it conflicts with something you want to believe. A real trick to being wrong less is to fight your instincts to dismiss new information and instead to embrace new ways of thinking and new paradigms.

There are a couple of tactical mental models that can help you on an everyday basis to overcome your ingrained confirmation bias and tribalism. First, consider thinking gray , a concept we learned from Steven Sample’s book The Contrarian’s Guide to Leadership . Thinking gray means withholding judgment while you gather evidence. The answer need not lie midway between opposing views. This model is powerful because it forces you to be patient. Delaying commitment can reduce the urge to defend a decision already made, though it does not eliminate bias. It can be difficult to think gray because all the nuance and different points of view can cause cognitive dissonance. However, it is worth fighting through that dissonance to get closer to the objective truth.

A second mental model that can help you with confirmation bias is the Devil’s advocate position . This was once an official position in the Catholic Church used during the process of canonizing people as saints. Once someone is canonized, the decision is eternal, so it was critical to get it right. Hence this position was created for someone to advocate from the Devil’s point of view against the deceased person’s case for sainthood. More broadly, playing the Devil’s advocate means taking up an opposing side of an argument, even if it is one you don’t agree with.

One approach is to force yourself literally to write down different cases for a given decision or appoint different members in a group to do so. Another, more effective approach is to proactively include people in a decision-making process who are known to hold opposing viewpoints. Doing so will help everyone involved more easily see the strength in other perspectives and force you to craft a more compelling argument in favor of what you believe. As Charlie Munger says, “I never allow myself to have an opinion on anything that I don’t know the other side’s argument better than they do.”

Don’t Trust Your Gut

You make most of your everyday decisions using your intuition , with your subconscious automatically intuiting what to do from instinct or encoded knowledge. It’s your common or sixth sense, your gut feeling , drawing on your past experiences and natural programming to react to circumstances.

In his book Thinking, Fast and Slow , economics Nobel laureate Daniel Kahneman makes a distinction between this intuitive fast thinking and the more deliberate, logical thinking you do when you slow down and question your intuitive assumptions. He argues that when you do something frequently, it gradually gets encoded in your brain until at some point your intuition, via your fast thinking, takes over most of the time and you can do the task mindlessly: driving on the highway, doing simple arithmetic, saying your name. However, when you are in uncertain situations where you do not have encoded knowledge, you must use your slower thinking: driving on new roads, doing complex math, digging into your memory to recall someone you used to know. These are not mindless tasks.

You can run into trouble when you blindly trust your gut in situations where it is unclear whether you should be thinking fast or slow. Following your intuition alone at times like these can cause you to fall prey to anchoring, availability bias, framing, and other pitfalls. Getting physically lost often starts with you thinking you intuitively know where to go and ends with the realization that your intuition failed you. Similarly, in most situations where the mental models in this book will be useful, you will want to slow down and deliberately look for how to best apply them. You may use intuition as a guide to where to investigate, but you won’t rely on it alone to make decisions. You will need to really take out the map and study it before making the next turn.

You probably do not have the right experience intuitively to handle everything that life throws at you, and so you should be especially wary of your intuition in any new or unfamiliar situation. For example, experience around bears does not automatically prepare a hiker for an encounter with a mountain lion. Different species can respond differently to the same behavior; familiar instincts may be a poor guide in an unfamiliar encounter.

At the same time, intuition can help guide you to the right answer much more quickly. For example, the more you work with mental models, the more your intuition about which one to use in a given situation will be right, and the faster you will get to better decisions working with these models. In other words, as we explained at the beginning of this chapter, using mental models over time is a slow and steady way to become more antifragile, making you better able to deal with new situations over time.

Of course, the better the information you put into your brain, the better your intuition will be. One way to accelerate building up useful intuition like this is to try consistently to argue from first principles. Another is to take every opportunity you can to figure out what is actually causing things to happen. The remaining mental models in this chapter can help you do just that.

At 11:39 A.M. EST on January 28, 1986, the space shuttle Challenger disintegrated over the Atlantic Ocean, just seventy-three seconds into its flight, killing the seven crew members on board. It was a sad day we both remember vividly. A U.S. presidential commission was appointed to investigate the incident, ultimately producing the Rogers Commission Report, named after its chairman, William Rogers.

When something happens, the proximate cause is the thing that immediately caused it to happen. In the case of the Challenger , the Rogers Commission Report showed that the proximate cause was the external hydrogen tank igniting. Root causes are the underlying conditions that allowed the immediate failure to occur. There may be several, rather than a single hidden reason.

People’s explanations for their behavior are no different: anyone can give you a reason for their behavior, but that might not be the real reason they did something. For example, repeated problems at work may reflect inadequate training, unclear expectations, or poor working conditions. A plausible explanation for one incident may miss the recurring pattern.

The Rogers Commission, in its June 6, 1986, report to the president, identified organizational failures behind the Challenger disaster, including flawed decisions about launch risk.

As part of its work, the commission conducted a postmortem . In medicine, a postmortem is an examination of a dead body to determine the root cause of death. As a metaphor, postmortem refers to any examination of a prior situation to understand what happened and how it could go better next time. At DuckDuckGo, it is mandatory to conduct a postmortem after every project so that the organization can collectively learn and become stronger (antifragile).

One technique commonly used in postmortems is called 5 Whys , where you keep asking the question “Why did that happen?” until you reach the root causes. The number is a prompt rather than a stopping rule: keep asking while the answers identify causes you can investigate.

Nobel Prize–winning physicist Richard Feynman was on the Rogers Commission, agreeing to join upon specific request even though he was then dying of cancer. His appendix to the report criticized the gap between NASA managers’ confidence and engineers’ assessments of risk.

Sometimes you may want something to be true so badly that you fool yourself into thinking it is likely to be true. This feeling is known as optimistic probability bias , because you are too optimistic about the probability of success. NASA managers were way too optimistic about the probability of success, whereas the engineers who were closer to the analysis were much more on target.

Root cause analysis, using 5 Whys or another framework, can help you examine assumptions about what went wrong. Its explanations still need evidence; a plausible chain of answers is a hypothesis to test.

The reason that root causes are so important is that, by addressing them, you can prevent the same mistakes from happening in the future. An apt analogy is that by investigating root causes, you are not just treating the symptoms but treating the underlying disease.

We started this chapter explaining that to be wrong less, you need to both work at getting better over time (antifragile) and make fewer avoidable mistakes in your thinking (unforced errors). Unfortunately, there are a lot of mental traps that you actively need to try to avoid, such as relying too much on recent information (availability bias), being too wed to your existing position (confirmation bias), and overstating the likelihood of your desired outcome (optimistic probability bias). As Feynman warned Caltech graduates in 1974: “You must not fool yourself—and you are the easiest person to fool.”