"You can't really know anything if you just remember isolated facts. If the facts don't hang together on a latticework of theory, you don't have them in a usable form.
You've got to have models in your head."
- Charlie Munger, investor, vice chairman of Berkshire Hathaway
The world's greatest problem-solvers, forecasters, and decision-makers all rely on a set of frameworks and shortcuts that help them cut through complexity and separate good ideas from bad ones. They're called mental models, and you can find them in dense textbooks on psychology, physics, economics, and more.
Or, you can just read Super Thinking, a fun, illustrated guide to every mental model you could possibly need. How can mental models help you? Well, here are just a few examples...
• If you've ever been overwhelmed by a to-do list that's grown too long, maybe you need the Eisenhower Decision Matrix to help you prioritize.
• Use the 5 Whys model to better understand people's motivations or get to the root cause of a problem.
• Before concluding that your colleague who messes up your projects is out to sabotage you, consider Hanlon's Razor for an alternative explanation.
• Ever sat through a bad movie just because you paid a lot for the ticket? You might be falling prey to Sunk Cost Fallacy.
• Set up Forcing Functions, like standing meeting or deadlines, to help grease the wheels for changes you want to occur.
So, the next time you find yourself faced with a difficult decision or just trying to understand a complex situation, let Super Thinking upgrade your brain with mental models.
|Publisher:||Penguin Publishing Group|
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About the Author
Gabriel Weinberg is the CEO & Founder of DuckDuckGo, the Internet privacy company and private search engine. He holds a B.S. with honors from MIT in Physics and an M.S. from the MIT Technology and Policy Program. Weinberg is also the co-author of Traction.
Lauren McCann is a statistician and researcher. She spent nearly a decade at GlaxoSmithKline, where she designed and analyzed clinical trials and authored articles in medical journals, including the New England Journal of Medicine. She holds a Ph.D. in Operations Research and a B.S. with honors in mathematics, from MIT.
Lauren and Gabriel reside in Valley Forge, PA with their two children.
Read an Excerpt
Being Wrong Less
You may not realize it, but 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 establishment but simply choose the healthier options when there.
The problem of making good decisions can also benefit from this concept of inverse thinking. The inverse of being more right is being less wrong. Mental models are a tool set that can help you be less wrong. They are a collection of concepts that help you more effectively navigate our complex world.
As noted in the Introduction, mental models come from a variety of specific disciplines, but many have more value beyond the field they come from. 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 less wrong in tennis, you need to make fewer unforced errors on the court. And to be consistently less wrong in decision making, you consistently need to make fewer unforced errors in your own life.
See how this works? Unforced error is a concept from tennis, but it can be applied as a metaphor in any situation where an avoidable mistake is made. 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 mental model to help improve your thinking is called antifragile, a concept explored in a book of the same name, by financial analyst Nassim Nicholas Taleb. In his words:
Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty. Yet, in spite of the ubiquity of the phenomenon, there is no word for the exact opposite of fragile. Let us call it antifragile.
Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better.
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. We’d like to improve your thought process by helping you incorporate mental models into your day-to-day thinking, increasingly matching the right models to a given situation.
By the time you’ve finished reading this book, you will have more than three hundred mental models floating around in your head from dozens of disciplines, eager to pop up at just the right time. 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. If you apply these mental models consistently and correctly, your decisions will become wrong much less, or inverted, right much more. That’s super thinking.
In this chapter we’re going to explore solving problems without bias. Unfortunately, evolution has hardwired us with several mind traps. If you are not aware of them, you will make poor decisions by default. But if you can recognize these traps from afar and avoid them by using some tried-and-true techniques, you will be well on the path to super thinking.
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 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 group of self-evident assumptions that make up the foundation on which your conclusions rest—the ingredients in a recipe or the mathematical axioms that underpin a formula.
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. Entrepreneur Elon Musk illustrates how this process works in practice in an interview on episode 20 of the Foundation podcast:
First principles is kind of a physics way of looking at the world. . . . You kind of boil things down to the most fundamental truths and say, “What are we sure is true?” . . . and then reason up from there. . . .
Somebody could say . . . “Battery packs are really expensive and that’s just the way they will always be. . . . Historically, it has cost $600 per kilowatt-hour, and so it’s not going to be much better than that in the future.” . . .
With first principles, you say, “What are the material constituents of the batteries? What is the stock market value of the material constituents?” It’s got cobalt, nickel, aluminum, carbon, and some polymers for separation, and a seal can. Break that down on a material basis and say, “If we bought that on the London Metal Exchange, what would each of those things cost?” . . .
It’s like $80 per kilowatt-hour. So clearly you just need to think of clever ways to take those materials and combine them into the shape of a battery cell and you can have batteries that are much, much cheaper than anyone realizes.
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. Most people looking for work will apply to too many jobs and take the first job that is offered to them, which is likely not the optimal choice. 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.
As another example, any startup business idea is built upon a series of principled assumptions:
• My team can build our product.
• People will want our product.
• Our product will generate profit.
• We will be able to fend off competitors.
• The market is large enough for a long-term business opportunity.
You can break these general assumptions down into more specific assumptions:
• My team can build our product—We have the right number and type of engineers; our engineers have the right expertise; our product can be built in a reasonable amount of time; etc.
• People will want our product—Our product solves the problem we think it does; our product is simple enough to use; our product has the critical features needed for success; etc.
• Our product will generate profit—We can charge more for our product than it costs to make and market it; we have good messaging to market our product; we can sell enough of our product to cover our fixed costs; etc.
• We will be able to fend off competitors—We can protect our intellectual property; we are doing something that is difficult to copy; we can build a trusted brand; etc.
• The market is large enough for a long-term business opportunity—There are enough people out there who will want to buy our product; the market for our product is growing rapidly; the bigger we get, the more profit we can make; etc.
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, proving or disproving them, and then adjusting your strategy appropriately.
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 puts 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. Military strategist Helmuth von Moltke put it like this: “No battle plan survives contact with the enemy.” And boxer Mike Tyson: “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.