Why do so many people fail to solve problems effectively? How can you use data to solve your problems?<\/p>\n\n\n\n
Think Like a Freak<\/em> by Steven Levitt and Stephen Dubner offers a revolutionary approach to problem-solving. The authors show you how to tackle everything from personal dilemmas to global issues by adopting a data-driven mindset that questions assumptions and embraces unconventional thinking<\/a>.<\/p>\n\n\n\n Keep reading for an overview of the book Think Like a Freak<\/em>.<\/p>\n\n\n\n\n\n\n\n In Think Like a Freak<\/em><\/a>, economist Steven Levitt and journalist Stephen Dubner set out to help readers solve their problems by looking at data in unconventional\u2014yet data-driven and rational\u2014ways. They argue that when you approach problems by properly analyzing facts instead of by following emotions or morals, you\u2019ll come up with far more effective (if unexpected) solutions. This is true whether your problems are lighthearted ones like improving your competitive-eating skills or weighty ones like reducing famine.\u00a0<\/p>\n\n\n\n The authors build on the success of their 2005 book, Freakonomics<\/em><\/a>, which analyzed everyday problems through an economic, data-driven lens to question conventional wisdom. Freakonomics<\/em> has sold over 4 million copies and inspired multiple award-winning podcasts, a blog, a documentary, and several \u201cfreakquels,\u201d including SuperFreakonomics<\/em><\/a> and When to Rob a Bank<\/em><\/a>. In Think Like a Freak<\/em>, they re-examine some of their earlier insights, such as the power of incentives and the importance of questioning<\/a> common knowledge, and add practical tips for applying them to your life.\u00a0<\/p>\n\n\n\n In this guide, we\u2019ve grouped their main points by theme and will examine their advice to:<\/p>\n\n\n\n Along the way, we examine some of the psychological roots of their ideas, connecting their thoughts to advice from other psychologists who also look at problems in unconventional ways, such as Angela Duckworth, Nassim Nicholas Taleb<\/a>, and Rolf Dobelli.<\/p>\n\n\n\n Levitt and Dubner write that one reason people fail to solve problems effectively is that they don\u2019t admit when they don\u2019t know something. There are a number of reasons for this: People confuse beliefs with facts, over-estimate their skills, and suffer few consequences for saying things that are wrong. We\u2019ll review each of these points and then explain Levitt and Dubner\u2019s advice for overcoming ignorance by running experiments. <\/p>\n\n\n\n Levitt and Dubner define facts<\/em> as things that can be scientifically proven\u2014for example, what time the sun rises. Beliefs<\/em> are things we think (or feel) are true but can\u2019t be scientifically verified\u2014like the existence of angels. They write that we often confuse the two, thinking that our beliefs represent reality in the same way facts do. However, beliefs and reality can be grossly misaligned. <\/p>\n\n\n\n One reason this happens is that our beliefs can be influenced by the views of others<\/strong>\u2014such as politicians, religious leaders, or businesses who profit by influencing your opinions. Another reason our beliefs can be out of step with reality is that they\u2019re shaped by our <\/strong>moral compass<\/em><\/strong>\u2014our unshakable sense of right and wrong. Our moral compass tells us there\u2019s only one right answer to any problem, that answer is clear and obvious, and we already know everything we need to know about it. <\/p>\n\n\n\n When we mistakenly think our beliefs are facts, we tend to justify them by only paying attention<\/a> to evidence that confirms them. Thus, rather than rationally evaluating data to discover if what it indicates is true, we filter <\/em>it, ignoring what we don\u2019t like and interpreting the rest so that it supports our position. This is why two intelligent people can examine the same data but come to completely different conclusions about, for example, the effectiveness of gun control\u2014each person interprets the rise or fall of crime and gun ownership differently. <\/p>\n\n\n\n Levitt and Dubner write that another reason people don\u2019t admit what they don\u2019t know is that they overestimate their skills, both general and specialized. <\/p>\n\n\n\n First, people assess their general knowledge and skills at everyday tasks more highly than is warranted<\/strong>: One study found that 80% of respondents rate themselves as better-than-average drivers\u2014a statistical impossibility. <\/p>\n\n\n\n Second, people rate themselves more highly than they should in areas that require <\/strong>specialized<\/em><\/strong> expertise<\/strong>. Experts in various fields are especially prone to this; when someone has specialized knowledge in one area, they tend to be overly confident that they have specialized knowledge in other, unrelated areas. For example, someone with an advanced degree in mathematics may give strong opinions on political matters, as if their mathematical knowledge bleeds into that field. <\/p>\n\n\n\n Levitt and Dubner also argue that people rarely admit when they don\u2019t know something because the consequences for wrongly assessing or predicting something are usually smaller than the costs of saying \u201cI don\u2019t know.\u201d<\/strong> If you admit you don\u2019t know something, you risk other people looking down on you. On the other hand, if you confidently state a fact, you burnish your reputation by posing as informed\u2014whether or not you actually are. Then, if the thing you said turns out to be untrue, nothing bad typically happens. <\/p>\n\n\n\n There are so many bad facts and predictions thrown around that most people don\u2019t keep track of other people\u2019s bad assessments, so anything that you say that\u2019s incorrect quickly gets forgotten. This is why so many financial experts confidently predict the future<\/a> movements of the stock market and why so few of them suffer backlash when their predictions are wrong. <\/p>\n\n\n\n Levitt and Dubner argue that the only way to truly know something is to run experiments and see what works.<\/strong> Experimentation gives us real-time feedback on our ideas, allowing us to know if something that works in theory<\/em> also works in practice<\/em>. They note that this is how humans have learned everything throughout history, from what foods are poisonous to how to build houses: We can come up with a design, but we can\u2019t truly know if it will stand firm until we see it in action. <\/p>\n\n\n\n They therefore advise that when asked about something you\u2019re unsure of, the best thing to say is, \u201cI don\u2019t know,\u201d and then follow up with, \u201cBut I can try to find out.\u201d There are many benefits to this approach: The most obvious is that you\u2019re far more likely to find the right answer to the problem. Another is that you\u2019ll boost your credibility<\/strong>\u2014people will respect your honesty, and the next time you come up with an answer, they\u2019ll be more ready to believe you. <\/p>\n\n\n\n Levitt and Dubner write that there are three kinds of experiments: lab experiments, field experiments, and natural experiments<\/a>. <\/p>\n\n\n\n Lab experiments are those in which you recruit volunteers to participate in a structured, controlled test. <\/strong>These can reveal valuable information, but they also have limits: The studies often don\u2019t imitate real-world circumstances well enough to accurately predict what would happen outside of the lab, if people were to encounter the same experiment factors in real life. People who know they\u2019re being observed behave differently than they would in private.<\/p>\n\n\n\n Field experiments are when you change some factor in the real world and then watch how people react. <\/strong>These can reveal more accurate information about human behavior than lab experiments, but they also have limitations. In particular, it\u2019s not always possible\u2014or ethical\u2014to ask people to change their behavior in certain ways. For example, you couldn\u2019t ask parents to feed their children something harmful to see what would happen. <\/p>\n\n\n\n Natural experiments happen when a naturally occurring event changes how people operate.<\/strong> These allow you to see and analyze how such a change prompts people to behave differently. For example: If you\u2019re trying to identify the effects of a certain policy, you can examine different times and places where such a change occurred to see how it might have affected lives\u2014like whether car accidents became more or less frequent after speed bumps were added to certain roads. <\/p>\n\n\n\n Levitt and Dubner discuss another key reason people have trouble coming up with effective solutions: They identify the wrong problem, which they then set out to solve instead of solving the true, core problem<\/a><\/strong>. <\/p>\n\n\n\n This is partly because, for survival reasons, we\u2019ve evolved to quickly identify the most obvious problem. For example, when there\u2019s a tiger hiding in a bush, our lives depend on quickly identifying that danger, not carefully considering it. However, more complex questions have more complex, less obvious answers. But because we\u2019re hard-wired to look for the obvious problem, we mistakenly address what seems like<\/em> the problem rather than what\u2019s the actual <\/em>problem. This can lead us to treat the symptoms of the wrong issue entirely. <\/p>\n\n\n\n Levitt and Dubner write that to find the true root cause of a problem, rather than what appears to be the cause, you must redefine the problem. This means that when most people are asking the same questions or blaming the usual causes, you should try to view the question from a different perspective and look for different causes<\/strong>. <\/p>\n\n\n\n The authors cite, as an example, the success of Takeru Kobayashi, the Japanese competitive eater who broke world records in hot-dog-eating competitions by rethinking how to approach the challenge. Before him, competitors simply tried to chew faster and gulp larger portions. But instead of simply aiming to eat more hot dogs<\/em>, Kobayashi set out to make hot dogs easier to eat<\/em>. Noting that there was no rule mandating how the hot dogs must be eaten, he took them out of their buns, ate the hot dogs alone, and ate the buns after dipping them in water. By doing so, he redefined the competition as a true sport for which people can train and strategize.<\/p>\n\n\n\n Levitt and Dubner note that root causes can be difficult to spot, often because they\u2019re the results of long-running societal wrongs.<\/strong> For example, famine is more than a lack of food\u2014it\u2019s a symptom of malfunctioning political, social, and legal establishments that lead to an unhealthy economy plagued by inequality and poverty. This is a deeper, broader dysfunction that can\u2019t be solved by simply supplying food to affected people, which is a temporary solution addressing the symptom<\/em>, not the underlying cause. <\/p>\n\n\n\n Levitt and Dubner advise that to effectively see problems from a new viewpoint, or to generate ideas and questions, it helps to have the mentality of a child. They argue that children inherently see the world from a new angle<\/strong>\u2014not just metaphorically but also literally, since they\u2019re generally smaller than adults\u2014and this allows them to spot things adults overlook. <\/p>\n\n\n\n Kids are also naturally curious, and they haven\u2019t yet developed preconceptions and biases that stop them from seeking to learn\u2014such as overconfidence in their own expertise. Additionally, they don\u2019t know the \u201ccommon knowledge\u201d related to any particular problem, and they don\u2019t know what others have tried before. This allows them to approach problems with a fresh mindset<\/strong>, without the self-imposed restrictions adults often work under. <\/p>\n\n\n\n Levitt and Dubner offer a few tips on how to approach problems with the mentality of a child:<\/p>\n\n\n\n As adults, we typically don\u2019t question things we learned long ago, such as set-up steps in a process or background pieces of information. We take them for granted and assume they\u2019re true because we\u2019re used to them. But someone learning something new will often question those things and in doing so, may reveal insights we\u2019ve overlooked. Levitt and Dubner encourage you to emulate this\u2014question things you haven\u2019t thought about critically in a while.<\/strong> <\/p>\n\n\n\n As you brainstorm, don\u2019t worry about what’s good or bad. S<\/strong>it on your ideas for a day and see how you feel about them\u2014t<\/strong>his will usually reveal which ideas are better than others. <\/p>\n\n\n\n There are several ways that having fun<\/a> can lead to better solutions.<\/strong> First, when you\u2019re having fun, you\u2019re more likely to examine things more closely\u2014people who ask unusual questions are often the ones who are enjoying themselves. Second, you\u2019re more likely to spend time on something if you\u2019re enjoying it, and research shows that the key to success is<\/a> not raw talent but practice\u2014how many hours you put into a pursuit. Third, by using fun, you can encourage other<\/em> people to behave in certain ways. For example, a teacher might encourage their students to exercise by offering prizes to the class that does the most jumping jacks. <\/p>\n\n\n\n Levitt and Dubner write that to solve a problem that involves other people\u2014be it a personal, professional, or societal problem\u2014you must understand how and why people respond to incentives. Incentives are at the root of all human behavior and drive everyone\u2019s decision-making<\/a>. <\/p>\n\n\n\n Levitt and Dubner note that it can be difficult to identify a person\u2019s true incentives. People don\u2019t always clearly say\u2014or admit\u2014what really drives them.<\/strong> Instead they often say what they think the other person wants to hear, but later, in private, they behave as they truly want to. Economists call these declared preferences<\/em> and revealed preferences<\/em>. The key to successfully crafting solutions to problems is figuring out how to bridge the gap between these two types of preferences so that your incentives appeal to what people will<\/em> do rather than what they think they should<\/em> do. If you don\u2019t, the solutions you come up with may not inspire people to act as you hope they will, and thus may be ineffective. <\/p>\n\n\n\n Next, we\u2019ll review Levitt and Dubner\u2019s discussions of the power of herd mentality, the fact that people generally make selfish decisions, how incentives encourage people to reveal their true selves, and how using incentives can backfire. <\/p>\n\n\n\n Levitt and Dubner note that one of the most powerful incentives is the desire to fit into the group<\/strong>, often referred to as the herd mentality<\/em>. They write that this desire can rival the desire for money, and they cite a study that demonstrates just how strong the herd mentality can be. The study also highlights the discrepancy between declared and revealed preferences.<\/p>\n\n\n\n Through a phone survey, researchers asked Californian residents what would motivate them to cut back on their energy use. Most people cited environmental concerns, followed by societal benefits and then financial concerns. They ranked \u201cother people also conserving energy\u201d last. <\/p>\n\n\n\n However, the next phase of the experiment showed that what people actually<\/em> cared about was very different from what they claimed. The researchers hung information tags on people\u2019s doorknobs encouraging them to save energy. Each tag cited various reasons and included a related statistic, such as the fact that residents can reduce pollution by a certain amount every month, would save a certain amount of money, or that 77% of their neighbors use fans instead of air conditioning when possible. <\/p>\n\n\n\n Then, the researchers measured the energy use of each home. They discovered that the homes receiving tags that said their neighbors were also conserving energy lowered their energy usage significantly more than any other homes. Levitt and Dubner write that when you successfully identify people\u2019s true incentives like this, you can spur them to do something <\/strong>right<\/em><\/strong> even if it\u2019s for the <\/strong>wrong<\/em><\/strong> reasons<\/strong>\u2014like getting them to save energy just to fit in with the neighbors. <\/p>\n\n\n\n Levitt and Dubner write that when you evaluate incentives and base solutions on them, you must keep in mind that people usually act selfishly,<\/strong> putting their personal interests ahead of the concerns of a larger group. This human tendency poses challenges for policymakers\u2014organizations tasked with improving society<\/a> have to figure out how to counter the human instinct to work against the greater good in favor of personal benefit. <\/p>\n\n\n\n To illustrate this instinct, Levitt and Dubner examine the thought process that soccer players go through during a penalty kick\u2014when a kicker has a chance to shoot the ball into the goal with only the goalie to block it. The kicker can choose to kick left, right, or center, and will do so as randomly as possible to prevent the goalie from detecting a pattern. The goalie must choose to jump left, jump right, or stay in the center before the kick happens, because it takes just a fraction of a second for the ball to travel from the kicker to the goal. <\/p>\n\n\n\n Rationally, you\u2019d expect the kicker to choose left, right, or center approximately one-third of the time each, and same for the goalie. However, data shows that kickers kick to the center only 17 percent of the time\u2014and goalies stay in the center only two<\/em> percent of the time. Shots aimed at the center of the goal are significantly more likely to succeed, and both kickers and goalies know this. So why do kickers so seldom shoot there, and why do goalies so infrequently defend it?<\/p>\n\n\n\n Levitt and Dubner attribute this tendency to the desire on both sides to avoid embarrassment. Kickers don\u2019t want to look like they kicked directly to a waiting goalie, should the goalie stay in that spot. And if the goalie stays in the center while the ball is kicked to either side, it will look like they didn\u2019t bother to try. Either way, the crowd will react negatively. <\/p>\n\n\n\n This example illustrates the power of incentives to make decisions selfishly\u2014in this case, the players are motivated by the personal incentive<\/a> not to look silly rather than the teamwide incentive to win the game. Levitt and Dubner argue that acting selfishly in this way doesn\u2019t make anyone a bad person\u2014it simply means they\u2019re human,<\/strong> and when you\u2019re constructing incentive systems, you must assume that people are selfish and design your incentives accordingly. <\/p>\n\n\n\n Levitt and Dubner write that one way you can leverage incentives to benefit your project or organization is by designing incentive schemes that encourage people to reveal their true inner motivations, even if those motivations are something they\u2019d rather keep hidden. If you set up incentives to attract certain people in certain ways, you can get them to self-select into categories,<\/strong> allowing you to know what kind of people they are and how to interact with them. <\/p>\n\n\n\n For example, if you\u2019re hiring an employee, you can design your job application process to discourage the wrong people from applying. This is the reason some companies make their application process difficult even for entry-level jobs: It prevents people from applying who might quickly quit if they do get the job. A difficult application process is self-selecting, since it only attracts determined, diligent applicants. <\/p>\n\n\n\n Levitt and Dubner warn that incentives sometimes have the opposite effect of what you intend: They can encourage <\/strong>more<\/em><\/strong> of the bad behavior you\u2019re trying to prevent.<\/strong> One manifestation of this is if you warn people that a problem is common\u2014instead of feeling moral outrage, they\u2019ll often think, if everyone else is doing this, I can too<\/em>. This is an undesirable consequence of the herd mentality, and was illustrated when park rangers at Arizona\u2019s Petrified National Park posted a sign stating that 14 tons of petrified wood was stolen every year. Their intention was to deter visitors from stealing more, but instead, visitors developed a fear of missing out, prompting many to grab a piece of the forest before it all disappeared. <\/p>\n\n\n\n Levitt and Dubner say incentives can also backfire if you offer to pay people to destroy something undesirable<\/strong>\u2014they\u2019ll often create more<\/em> of it to destroy so they can get paid. This tendency is sometimes called the \u201ccobra effect,\u201d named after an incident in colonial India when a British overlord, concerned about the burgeoning cobra population, offered a cash reward for cobra skins. Instead of leading to fewer cobras, it gave rise to a cobra-farming industry designed around raising and slaughtering the snakes. <\/p>\n\n\n\n A more recent example of this phenomenon was when the United Nations offered payments to manufacturers to destroy their pollutants. Manufacturers began creating more of them so they could destroy them, leading to a sharp increase in pollution. <\/p>\n\n\n\nOverview of Think Like a Freak<\/em><\/strong><\/h2>\n\n\n\n
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Admit What You Don\u2019t Know<\/strong><\/h3>\n\n\n\n
We Confuse Facts With Beliefs <\/strong><\/h4>\n\n\n\n
We Overrate Our Skills<\/strong><\/h4>\n\n\n\n
Consequences for Guessing Wrong Are Small<\/strong><\/h4>\n\n\n\n
How to Experiment Effectively<\/strong><\/h4>\n\n\n\n
Identify the Root Problem<\/strong><\/h3>\n\n\n\n
Think Like a Kid<\/strong><\/h3>\n\n\n\n
Ask About the Obvious<\/strong><\/h4>\n\n\n\n
Generate Lots of Ideas and Edit Them Later<\/strong><\/h4>\n\n\n\n
Have Fun<\/strong><\/h4>\n\n\n\n
Use Incentives<\/strong><\/h3>\n\n\n\n
The Herd Mentality<\/strong><\/h4>\n\n\n\n
People Make Selfish Choices<\/strong><\/h4>\n\n\n\n
Self-Selecting Incentives <\/strong><\/h4>\n\n\n\n
Be Careful of Incentives Backfiring<\/strong><\/h4>\n\n\n\n