Loserthink, page 8
If you’re a weirdo in any way (and I say this with love), you can probably find similar weirdos if you stop hiding. And life will be better for all of you, assuming your weirdness is legal.
I’m not antiprivacy. I take it as a given that there are some situations in which society is better off with a degree of privacy. My point here is that we have a reflexive desire for privacy that is not too far in type from our reflexive desire to eat junk food. The fact that we want privacy is not related to how good it is for us. My examples with LGBTQ and shy bladder “coming out” make the case.
Now consider what it would mean to healthcare outcomes if our lifestyle choices, our health records, our DNA, and even our current health indicators were routinely collected and stored. In this imaginary scenario, let’s say the information was private from your neighbors, but available in raw data form without your identification to scientists. Armed with that kind of useful health data, I would expect healthcare costs to drop and outcomes to improve.
It is easy to imagine other situations in which trading some privacy for larger benefits makes sense. And of course there will always be situations in which privacy is unambiguously good. I’ll summarize it this way:
If you think more privacy is always better, that is a case of loserthink. Every situation is different. Sometimes privacy is the problem that prevents the solution.
CHAPTER 6
Thinking Like an Engineer
PROFESSIONAL JEALOUSY
During my corporate career, I worked with a lot of engineers, programmers, and other tech workers. That experience taught me a valuable lesson about how much to trust experts, and I thought it worth sharing.
I’ve been involved with dozens of software upgrade projects over the years, both in my cubicle days and later as a cartoonist and entrepreneur. And one thing you can always count on is that whoever is hired to work on the new version of the software will call the person who worked on the last version an idiot.
Let’s label this phenomenon professional jealousy, although there are a variety of motivations for mocking the last employee on the job. For example, mocking the last programmer is a good way to boost your perceived value, by comparing your awesomeness to the prior employee’s uselessness. And the best part is that the target of your criticism is usually already gone and unable to defend against the charges. As a general rule, it’s always smarter to criticize people who aren’t around.
Assigning blame to the last person who worked on a project is not limited to technology workers. You see it in every job and in politics. And once you have seen it often enough, you can incorporate it into your thinking. And that means whenever you are talking to an expert in any realm, be aware that the next expert is likely to tell you the work done by the last expert looked like a monkey pounding a keyboard with a banana. And the expert after that will be just as rough on the prior expert, all the way to infinity. If experts are routinely skeptical of other experts, shouldn’t you be skeptical of experts too?
I’m obviously exaggerating for effect, but I think you get the idea. For simple situations, experts usually agree. The problem comes with complicated situations in which there are opportunities for lots of judgment calls. And when I say experts, in this context I mean anyone with extra knowledge of a topic, including your coworkers.
If you are wondering how skeptical you should be about expert advice on complicated issues, keep in mind that the next expert probably has no respect for the last expert. And vice versa.
SEPARATING CAUSE AND SOLUTION
Engineers are trained to find practical solutions to problems even when emotions and politics are pushing untrained minds in the wrong direction. Non-engineers often find themselves locked in a mental prison that says the solution to a problem has to be tightly coupled with the cause. That can often be the right path. But sometimes the cause of a problem is not the best place to look for a solution, and engineers are trained to understand that. For example, there were 72,000 drug overdose deaths in the United States in 2017 alone. Some say the cause of the problem is the addicts themselves because no one is forcing them to take drugs. Therefore, say many observers, overdose deaths are solidly in the category of “not my problem.”1
If you leave it to addicts to fix the opioid overdose problem on their own, you’ll never have a solution. When the people who are at fault for a problem are unable or unwilling to fix it, your only choices are to live with the problem or find solutions unrelated to who is at fault. In the case of opioid addiction, that usually means government or charitable involvement to fund the recovery and treatment of addicts. When the annual number of deaths from opioids exceeds the number of American troops killed in Vietnam, Iraq, and Afghanistan combined, which is the current situation, it is the entire country’s problem. Every overdose death leaves at least one family with permanent damage. Waiting for the addicts to solve the opioid problem because they are the ones “most responsible” for it is loserthink.
If your home were repeatedly burglarized, most of us would say the fault is with the criminals. But the solution might not involve criminals at all. The solution might involve, for example, getting a dog, better door locks, an alarm system, and an NRA sticker for your front door. Loserthink pairs the solution with the blame. A more productive way to think is that solutions can come from anywhere.
Consider the topic of immigration. The fault for illegal immigration lies entirely with the people who do it. But it isn’t reasonable to expect they will be the ones who solve it. The only solutions to illegal immigration involve either the government making all immigration legal or finding better ways to keep people from immigrating to the country illegally.
Most people reading this book will prefer capitalism to pure socialism. And capitalism requires people to take personal responsibility for their financial success. Our legal system does the same. Society works best when people are held accountable for their own actions when it comes to money or the law. But it is a mistake to take the idea of personal accountability and apply it to every situation and every problem. Engineers are unlikely to make that sort of mistake. For them, the best solution can be independent of how we feel about the cause of the problem.
A common form of this loserthink is the “Who started it?” question. That’s another way to assign responsibility. There are plenty of reasons to assign responsibility for outcomes, but it doesn’t always tell you who is in the best position to solve the problem. If the group that started a problem is unable or unwilling to solve it, then the solution to the problem will be unrelated to the cause, and that’s okay. Engineers learn to remove emotions from their decisions, and that allows them to find the best solution without being limited by the question of who is at fault.
The best solution to a problem is often unrelated to who is at fault. It is loserthink to believe otherwise.
If you have more than one child in your house, you might know how hard it is to get them to clean up after themselves when the easier path is for them to blame a sibling. For parents, it is loserthink to allow the “Who did it?” question to influence the decision about who cleans it up. My mother was a champion of this approach.
MOM: Scott, clean up the mess in the living room.
SCOTT: I didn’t do that! It was Dave!
MOM: I didn’t ask you who made the mess.
And then I cleaned up the living room. Mom didn’t tolerate loserthink.
It is childlike thinking to insist in all cases that the people who cause problems are the only people who should solve them. A little bit of flexibility can go a long way.
ONE-VARIABLE ILLUSION
Engineers are trained to identify which combination of variables matters in a given situation. The thing that engineers know, and the general public often ignores, is that it is common for more than one variable to be important at the same time.
For most topics of national or global interest, you can’t rely on biased experts to sort things out for you, and those are the only experts you are likely to encounter. We generally have to rely on our own cleverness to discern truth from fake news. But how does one do that?
If you are like most people, you look for a one-variable shortcut. And sometimes that works. For example, years ago, when Newsweek was a physical magazine, they invited me to create a Dilbert-themed cover. The catch was that they were trying to decide between using my art versus another option in which they would use an attractive female face on the cover. I created the art, but I knew it was a waste of time. The one variable that mattered was that nearly all humans enjoy looking at attractive female faces. But perhaps 20 percent of the public would enjoy a Dilbert-themed comic. That’s enough people to make me rich (and it did), but it can’t compete with a cover that would be loved by nearly 100 percent of the public. As predicted, Dilbert did not appear on that cover. And in that case, one variable (an attractive female face) was predictive. But that is a special case. The more typical case is that you can’t tell which variables are predictive.
After the 2016 election, pundits offered their opinions on why Hillary Clinton unexpectedly lost. If you have been alive for any full hour since then, chances are that you’ve heard dozens of explanations, mostly focusing on one variable or another. Some writers produced long lists of Clinton’s missteps, as well as other “reasons” for her loss, in order to form a complete picture. But the loserthinkers, who were by far the majority, decided they could gaze into a situation that had hundreds of important variables and deduce “the one that mattered.”
Rarely is an election decided by one variable. That is especially true in a presidential election. Hillary Clinton lost exactly the way she lost because hundreds of variables were exactly what they were. If any of those variables had been appreciably different, so too would the result. So when you find yourself saying Clinton lost because of one variable, be aware that you are talking nonsense. All the variables had to tip the way they did to get the result we got.2
If you analyze a complicated situation with multiple variables in play, and you conclude that only one of them was decisive, there’s a good chance you are practicing loserthink.
You will be most tempted to default to one-variable thinking in the following situations in your life:
Figuring out why a relationship isn’t working
Understanding the motivation of friends and family
Making business decisions in complicated situations
I don’t know much about your particular life, so I will make my case using some topics we see in the news all the time. On the topic of climate change, for example, I often see skeptics declare that climate scientists will follow wherever the grant money leads them. Therefore, say the skeptics, we can’t believe scientists who say the climate is warming to disastrous levels. Money does create massive bias, but it is simplistic to think it is the one variable you need to get to a rational opinion on climate science. Few things are as complicated and variable-rich as the topic of climate.
Other climate skeptics say climate change is a conspiracy by a group of elite globalists who are trying to destroy capitalism in favor of socialism. Once you know that, they say, you don’t need to listen to the science. I dug into this belief a bit and learned it relies heavily on one misleading video clip taken out of context. But even if there were substance to the claim, it wouldn’t explain why thousands of climate scientists around the world believe they are doing real science. Even cognitive dissonance and confirmation bias can’t explain all of that.
On the topic of border security, we have observed since the election of 2016 that people are expressing one-variable opinions on opposite sides. The entire complexity of the topic comically shrunk down to “walls work” versus “walls don’t work.” Both opinions are ridiculous. Walls (or border barriers) are intended to create friction, change behavior, and perhaps reduce the need for human security in some areas. Complicating things further, a barrier that is good at slowing human traffic might be worthless for stopping drugs. Moreover, border barriers are more helpful in populated areas where illegal immigrants can disappear into a city on the other side. On barren lands with no nearby population centers, a low fence with sensors is all you need. If the sensors are tripped, border security shows up soon for the arrests because there is no good place to hide. A more nuanced opinion on border walls is that they create friction and change behavior, which border security experts say can be a helpful part of a larger mosaic of security solutions.
As a good general rule, simple situations can sometimes be predicted or explained by one key variable. But complicated situations, such as economies, climate change, and elections, are rarely one-variable situations.
CHAPTER 7
Thinking Like a Leader
Early in my career I thought I wanted to become a CEO, or at least a member of senior management somewhere. To learn the art of leadership, I went to school at night, worked a full-time job during the day, and earned my MBA degree from Berkeley. My coursework taught me how to analyze business and financial situations and make rational decisions. Unfortunately, there were no classes on persuasion, and persuasion is at least half of what a leader does all day. I learned persuasion on my own, and picked up other leadership skills over the years, mostly by observing people who did it right. Most of you know the basics of leadership, so I will focus on some concepts you are less likely to have seen.
THE DIRECTIONAL TRUTH FILTER
Let’s say you hire a personal trainer who promises to get your body fat down from 35 percent to 15 percent. You work together for a few years and get your body fat down to 20 percent. Technically, your trainer was wrong. You might even say your trainer lied. But the trainer was directionally accurate, and you came out way ahead. Most of life is like this example. You can often know you are heading in the right direction, which matters a lot, while the precision of your estimates is secondary.
Truth has two important dimensions: 1) accuracy, and 2) direction. If you don’t know which of those dimensions is more important, you might be in a mental prison.
If you are dealing with math, engineering, science, or medicine—to pick a few examples—you want your facts to be as accurate as possible. But even in those fields, it often matters more that you get the direction right.
For example, if an engineer determines that a new type of material would fail in one day under normal use, it doesn’t matter too much if that estimate is wrong by a month. What matters is that the new material is unsuitable for daily use.
Likewise, a doctor might say that improving your diet will add twenty years to your life, even though you might live only another five years. The doctor is still directionally accurate in the sense that pursuing a better diet improves your odds of a healthy life.
You can probably think of examples in which you do need total precision in your facts, such as in engineering a product that meets specifications. But those situations are obvious when you see them.
We humans have a reflexive distaste for inaccuracy and lies. And we dislike people who traffic in such factual inaccuracies. That makes perfect sense, because we evolved as social creatures. Trust is the glue that holds social groups together. We are hardwired to prefer the truth.
The catch is that any leader who hopes to move the minds of the public will soon learn their facts and reason are poor tools for doing so. I describe this phenomenon more completely in my book Win Bigly. The gist of it is that humans are irrational creatures who mistakenly believe they use logic and reason to arrive at decisions. The reality, which science has proven in lots of different ways, is that we routinely make irrational decisions and then try to rationalize them. That’s why the people who disagree with you so often appear to be not just wrong, but totally bonkers. And importantly, they think exactly the same about you.1
It’s easy to tell when another person is rationalizing (as opposed to being rational), but it is nearly impossible to know when you are doing it yourself. That’s why most of the social and political disagreements you see involve two or more idiots pointing at each other and screeching some form of “YOU IDIOT!” Both sides are right about the other being irrational, but wrong about themselves being rational.
A more productive way to see the world involves understanding that, for many types of truth, directional accuracy is all you need. For example, we might know that fair trade deals are better than unfair trade deals, but we don’t know how much a particular change would improve the GDP. We just know better trade deals are better for the economy. We can know the right direction of that truth without knowing precisely how things will turn out.
The same is true for most political decisions. We often know which direction we need to head, but we typically don’t know exactly where it will all end up. Getting the general direction right is critical, but being precisely accurate is only sometimes important.
If you find yourself complaining that a leader’s claims are not passing the fact-checking, you might be technically correct. But your accurate observations won’t necessarily matter in any important way. What will matter is whether or not that leader is persuading you and others in the right direction.
