PDF Summary:Thinking, Fast and Slow, by Daniel Kahneman

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System 1 can be completely involuntary. You can’t stop your brain from completing 2 + 2 = ?, or from considering a cheesecake as delicious. You can’t unsee optical illusions, even if you rationally know what’s going on.

System 1 can arise from expert intuition, trained over many hours of learning. In this way a chess master can recognize a strong move within a second, where it would take a novice several minutes of System 2 thinking.

System 2 requires attention and is disrupted when attention is drawn away. More on this next.

System 1 automatically generates suggestions, feelings, and intuitions for System 2. If endorsed by System 2, intuitions turn into beliefs, and impulses turn into voluntary actions.

System 1 can detect errors and recruits System 2 for additional firepower.

• Kahneman tells a story of a veteran firefighter who entered a burning house with his crew, felt something was wrong, and called for them to get out. The house collapsed shortly after. He only later realized that his ears were unusually hot but the fire was unusually quiet, indicating the fire was in the basement.

Because System...

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Kahneman cites one particular task as the limit of what most people can do in the lab, dilating the pupil by 50% and increasing heart rate by 7bpm. The task is “Add-3”:

• Write several 4 digit numbers on separate index cards.
• To the beat of a metronome, read the four digits aloud.
• Wait for two beats, then report a string in which each of the original digits is incremented by 3 (eg 4829 goes to 7152).

If you make the task any harder than this, most people give up. Mentally speaking, this is sprinting as hard as you can, whereas casual conversation is a leisurely stroll.

Because System 2 has limited resources, stressful situations make it harder to think clearly. Stressful situations may be caused by:

• Physical exertion: In intense exercise, you need to apply mental resistance to the urge to slow down. Even a physical activity as relaxed as taking a stroll involves the use of mental resources. Therefore, the most complex arguments might be done while sitting.
• The presence of distractions.
• Exercising self-control or willpower in general.

Because of the fixed capacity, you cannot will yourself to think harder in the moment and surpass the...

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Ready?

You might have found that the second one felt better. Isn’t that odd? There is a very faint signal from the associative machine of System 1 that says “these three words seem to connect better than the other three.” This occurred long before you consciously found the word (which is sea).

Association with Context

For another example, consider the sentence “Ana approached the bank.”

You automatically pictured a lot of things. The bank as a financial institution, Ana walking toward it.

Now let’s add a sentence to the front: “The group canoed down the river. Ana approached the bank.”

This context changes your interpretation automatically. Now you can see how automatic your first reading of the sentence was, and how little you questioned the meaning of the word “bank.”

Associations Evaluate Surprise

The purpose of associations is to prepare you for events that have become more likely, and to evaluate how surprising the event is.

The more external inputs associate with each other, and the more they...

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2) Mental Shotgun

System 1 often carries out more computations than are needed. Kahneman calls this “mental shotgun.”

For example, consider whether each of the following three statements is literally true:

• Some roads are snakes.
• Some jobs are snakes.
• Some jobs are jails.

All three statements are literally false. The second statement likely registered more quickly as false to you, while the other two took more time to think about because they are metaphorically true. But even though finding metaphors was irrelevant to the task, you couldn’t help noticing them - and so the mental shotgun slowed you down. Your System-1 brain made more calculations than it had to.

Heuristics: Answering an Easier Question

Despite all the complexities of life, notice that you’re rarely stumped. You rarely face situations as mentally taxing as having to solve 9382 x 7491 in your head.

Isn’t it profound how we can make decisions at all without realizing it? You like or dislike people before you know much about them; you feel a company will succeed or fail without really analyzing it.

When faced with a difficult question, System 1 substitutes an easier question, or...

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In other words, your emotional response fills in the blanks for what’s cognitively missing from your understanding.

The Halo Effect forms a simpler, more coherent story by generalizing one attribute to the entire person. Inconsistencies about a person, if you like one thing about them but dislike another, are harder to understand. “Hitler loved dogs and little children” is troubling for many to comprehend.

Ordering Effect

First impressions matter. They form the “trunk of the tree” to which later impressions are attached like branches. It takes a lot of work to reorder the impressions to form a new trunk.

Consider two people who are described as follows:

• Amos: intelligent, hard-working, strategic, suspicious, selfish
• Barry: selfish, suspicious, strategic, hard-working, intelligent

Most likely you viewed Amos as the more likable person, even though the five words used are identical, just differently ordered. The initial traits change your interpretation of the traits that appear later.

This explains a number of effects:

• Pygmalion effect: A person’s expectation of a target person affects the target...

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If it’s not lifestyle, what’s the key factor here? Population size. The outliers in the high-cancer areas appeared merely because the populations were so small. By random chance, some rural counties would have a spike of cancer rates. Small numbers skew the results.

Case 2: Small Classrooms

The Gates Foundation studied educational outcomes in schools and found small schools were habitually at the top of the list. Inferring that something about small schools led to better outcomes, the foundation tried to apply small-school practices at large schools, including lowering the student-teacher ratio and decreasing class sizes.

These experiments failed to produce the dramatic gains they were hoping for.

Had they inverted the question - what are the characteristics of the worst schools? - they would have found these schools to be smaller than average as well.

When falling prey to the Law of Small Numbers, System 1 is finding spurious causal connections between events. It is too ready to jump to conclusions that make logical sense but are merely statistical flukes. **With a surprising result, we immediately skip to understanding causality rather than...

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Sometimes, the anchor works because you infer the number is given for a reason, and it’s a reasonable place to adjust from. But again even meaningless numbers, even dice rolls, can anchor you.

The anchoring index measures how effective the anchor is. The index is defined as: (the difference between the average guesses when exposed to two different anchors) / (the difference between the two anchors). Studies show this index can be over 50%! (A measure of 100% would mean the person in question is not only influenced by the anchor but uses the actual anchor number as their estimate; conversely, a measure of 0% would indicate the person has ignored the anchor entirely.)

• For example, one study asked Group A two questions: 1) Is the tallest redwood taller or shorter than 1,200 feet? and 2) What do you think the height of the tallest redwood is? They asked Group B the same two questions, except the anchor in the first question was 180 feet rather than 1,200 feet.
• Did the anchors in the first question affect the estimates given in answer to the second question? Yes. The Group A mean was 844 feet and the Group B mean was 282 feet. This produced an...

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• Items that are covered more in popular media take on a greater perceived importance than those that aren’t, even if the topics that aren’t covered have more practical importance.

As we’ll discuss later in the book, availability bias also tends to influence us to weigh small risks as too large. Parents who are anxiously waiting for their teenage child to come home at night are obsessing over the fears that are readily available to their minds, rather than the realistic, low chance that the child is actually in danger.

Availability Bias and the Media

Within the media, availability bias can cause a vicious cycle where something minor gets blown out of proportion:

• A minor curious event is reported. A group of people overreact to the news.
• News about the overreaction triggers more attention and coverage of the event. Since media companies make money from reporting worrying news, they hop on the bandwagon and make it an item of constant news coverage.
• This continues snowballing as increasingly more people see this as a crisis.
• Naysayers who say the event is not a big deal are rejected as participating in a coverup.
• Eventually, all of this can affect...

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Again, by pure number of people, there are far more people in the latter group than the former.

Why Do We Suffer from the Representativeness Heuristic?

Representativeness is used because System 1 desires coherence, and matching like to like forms a coherent story that is simply irresistible.

The representativeness heuristic works much of the time, so it’s hard to tell when it leads us astray. Say you’re shown an athlete who’s thin and tall, then asked which sport he plays. You’d likely guess basketball more than football, and you’d likely be correct.

(Shortform note: the representativeness heuristic causes problems when your System 1 forms a coherent story that is inaccurate. Common problems involve stereotypes that cause incorrect snap judgments:

• When hiring for a role, you might hire based on a stereotype of how that role should behave, rather than the work the person does. For example, if you expect engineers to be plain and soft-spoken, a candidate who’s fashionable and outgoing might strike you as suspicious.
• While in the Israeli army, Kahneman was tasked with evaluating which recruits would be best suited for officer positions. He confidently gave...

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Are Smart Predictions Always Good?

Kahneman notes that absence of bias is not always what matters most. Relying too much on statistics would avoid the prediction of rare or extreme events on shaky information.

For example, venture capitalists make their money by correctly predicting the few companies that will make it big. They lose only 1x their money on a bad investment but make 1000x their money on a Google, so it’s important not to miss out on the next Google. However, using the type of quantitative analysis above might paralyze some investors, if they start with the baseline failure rate of startups (which is very high) and have to adjust upward from that anchor. For some people prone to paralysis, having distorted overoptimistic evidence might be better.

Similarly, sometimes the evidence is against you but your choice feels right, like when you know the high divorce rate as you’re about to get married. In these cases, you might be happier deluding yourself with extreme predictions—”our marriage is going to defy the odds.” Listening to our intuitions is more pleasant and less hard work than acting consciously against them. **But at the least, be aware of what...

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*   The book _Built to Last _profiled companies at the top of their field; these companies did not outperform the market after the book was published.

• Management literature also tries to find patterns to management systems that predict success. Often, the results are disappointing and not enduring.
  • The correlation between a firm’s success and the quality of its CEO might be as high as .30, which is lower than what most people might guess. Practically, this correlation of .30 suggests that the stronger CEO would lead the stronger firm in 60% of pairs, just 10% better than chance.
• We readily trust our judgments in situations that are poor representatives of real performance (like in job interviews).

Funnily, in some situations, an identical explanation can be applied to both possible outcomes. Some examples:

• During a day of stock market trading, an event might happen, like the Federal Reserve System lowering interest rates. If the stock market goes up, people say investors are emboldened by the move. If the stock market goes down, people say investors were expecting greater movement, or that the market had already priced it in. That the same...

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• How do you predict whether newborns are unhealthy and need intervention? A while back, doctors used their (poor) judgment. Instead, in 1952 doctor Virginia Apgar invented the Apgar score, a simple algorithm that takes into account 5 factors, such as skin color and pulse rate. This is still in use today.
• How do you predict wine prices for a bottle of wine? Traditionally, wine enthusiasts tasted the bottle, then assigned a hypothetical price. Instead, an economist used only two variables—the summer temperature and rainfall of the vintage. This was more accurate than humans, but wine experts were aghast—“how can you price the wine without tasting it?!” The formula was in fact better because it did not factor in human taste.

There is still some stigma about life being pervaded by robotic algorithms, removing some of the romance of life.

• Professionals who use intuition to predict things feel outrage when algorithms encroach on their profession. They feel many of their predictions do turn out correct and that they have skill, but their downfall is they don’t know the boundaries of their skill.
• It seems more heart-wrenching to lose a child due to an algorithm’s...

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When estimating for a project, you tend to give “best case scenario” estimates, rather than confidence ranges. You don’t know what you don’t know about what will happen—the emergencies, loss of motivation, and obstacles that will pop up—and you don’t factor in buffer time for this.

Kahneman gives an example of a curriculum committee meeting to plan a book. They happily estimate 2 years for completion of the book. Kahneman then asks the editor how long other teams have taken. The answer is 7-10 years, with 40% of teams failing to finish at all. Kahneman then asks how their team skill compares to the other teams. The answer is Kahneman’s team is below average.

This was an astounding example of how a person may have relevant statistics in her head, but then completely fails to recall this data as relevant for the situation. (The book did indeed take 8 years.)

Furthermore, before Kahneman asked his questions, the team didn’t even feel they needed information about other teams to make their guess! They looked only at their own data situation.

Government projects have a funny pattern of being universally under budget and delayed. (Though there may be an underlying...

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Bernoulli then argued that utility and wealth had a logarithmic relationship. The difference in happiness between someone with $1,000 and someone with $100 was the same as $100 vs $10. On a linear scale, money has diminishing marginal utility.

This concept of logarithmic utility neatly explained a number of phenomena:

• This meant that $10 was worth more to someone with $20 than to someone with $200. This aligns with our intuition - people with more money are less excited than poorer people about the same amount of money.
• This explained the value of certainty in gamble problems, like the 80% chance question above. On a logarithmic scale for utility, having 100% of $80 was better than having 80% of $100.
• This also explained insurance - people with less wealth were willing to sell risk to the wealthier, who would suffer less relative utility loss in the insured loss.

Despite its strengths, this model presented problems in other cases. Here’s an extended example.

Say Anthony has $1 million and Beth has $4 million. Anthony gains $1 million and Beth loses $2 million, so they each now have $2 million. Are Anthony and Beth equally happy?

Obviously not - Beth...

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We Feel Better About Absolute Certainty

We’ve covered how people feel about small chances. Now consider how you feel about these options on the opposite end of probability:

• In a surgical procedure, going from 90% success rate to 95% success rate.
• In a surgical procedure, going from 95% success rate to 100% success rate.

Most likely, you felt better about the second than the first. Outcomes that are almost certain are given less weight than their probability justifies. 95% success rate is actually fantastic! But it doesn’t feel this way, because it’s not 100%.

As a practical example, people fighting lawsuits tend to take settlements even if they have a strong case. They overweight the small chance of a loss.

(Shortform note: how we feel about 0% and 100% are similar and are inversions of each other. A 100% gain can be converted into 0% loss—we feel strongly about both. For example, say a company has a 100% chance of failure, but a new project reduces that to 99%. It feels as though the chance of failure is reduced much more than 1%. Inversely, a project that increases the rate of success from 0% to 1% seems much more likely to work than 1%...

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*   Notably, a third group (Choosers) could either receive a mug or a sum of money, and they indicated that receiving $3.12 was as desirable an option as receiving the mug. It’s important to note that the mug owners had been given an essentially identical choice: they were either going to leave with the mug or with the money someone paid for it. But because they already possessed the mug, they vastly embellished their ask.

• Owners who buy a house at higher prices spend longer trying to sell their home and set a higher listing price - even though the current market value is rationally all that matters.

The endowment effect doesn’t occur in all cases - people are willing to exchange $5 for five $1 bills, and furniture vendors are happy to exchange a table for money. When the asset under question is held for exchange, the endowment effect doesn’t apply.

You only feel endowed with items that are planned for consumption or use, like a bottle of wine or vacation days.

As with prospect theory and loss aversion, experienced financial traders show less attachment to the endowment effect.

Goals are Reference Points

**We are driven more to avoid failing a goal...

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• In a company, individual project leaders can be risk averse when leading their own project, since their compensation is heavily tied to project success. Yet the CEO overlooking all projects may wish that all project leaders take on the maximum appropriate risk, since this maximizes the expected value of the total portfolio.
• The opposite scenario may also happen: in a company, leaders of individual projects that are failing may be tempted to run an expensive hail-mary, to seek the small chance of a rescue (because of overweighting probabilities at the edges). In the broad framing, the CEO may prefer to shut down projects and redirect resources to the winning projects.
• A risk-averse defendant who gets peppered with frivolous lawsuits may be tempted to settle each one individually, but in the broad framing, this may be costly compared to the baseline rate at which it would win lawsuits (let alone settling lawsuits invites more lawsuits).
• Appliance buyers may buy individual appliance insurance, when the broad framing of all historical appliances may show this is clearly unprofitable for individuals.

Antidote to Narrow Framing

To overcome narrow framing, adopt...

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But the remembering self evaluates differently from the experiencing self in two critical ways:

• Peak-end rule: The overall rating is determined by the peak intensity of the experience and the end of the experience. It does not care much about the averages throughout the experience.
• Duration neglect: The duration of the experience has little effect on the memory of the event.

Both effects operate in classic System 1 style: by averages and norms, not by sums.

This leads to preferences that the experiencing self would find odd, and show that we cannot trust our preferences to reflect our interests.

In the ice water experiment, participants were asked to stick their hand in cold water, then to evaluate their experience. Participants stuck their hand in cold water in two episodes: 1) a short episode: 60 seconds in 14°C water, and 2) a long episode: 60 seconds in 14°C, plus an additional 30 seconds, during which the temperature increased to 15°C. They were then asked which they would repeat for a third trial.

The experiencing self would clearly consider the long episode worse—you’re suffering for more time. But the longer episode had a more...

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*   Things that affect mood: coworker relations, loud noise, time pressure, a boss hovering around you. 
*   Things that do not affect mood: benefits, status, pay.

• Some activities generally seen as positive (like having a romantic partner) don’t improve experienced well-being. This might be partially because of tradeoffs—women in relationships spend less time alone, but they also have less time with friends. They spend more time having sex, but they also spend more time doing housework and caring for children.

Suggestions for Improving Experienced Well-being

How can you improve your moment-to-moment happiness?

• Focus your time on what you enjoy. Commute less.
• To get pleasure from an activity, you must notice that you’re doing it. Avoid passive leisure time in places like TV, and spend more time in active leisure time, like socializing and exercise.

Reducing the U-index should be seen as a worthwhile societal goal. Reducing the U-index by 1% across society would be a huge achievement, with millions of hours of avoided suffering.

Experienced Well-Being vs Life Evaluations

Where well-being is measured by methods like the Day Reconstruction...

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*   If you’re given one extreme number to adjust from, repeat your reasoning with an extreme value from the other direction. Adjust from there, then average your final two results.

• Availability bias
  • Force yourself to lower the weight on more available things. Especially be wary of things you see repeated often in news or advertising, and of things that strongly trigger extreme emotions.
  • Be aware of “common sense” intuitions that seem true merely because it’s repeated often, like “searing meat seals in the juices.”
• Representativeness
  • To avoid the “Tom W. librarian” problem, use Bayesian statistics. Start by predicting the base rates, using whatever factual data you have. Then consider how the new data should influence the base rates.
• Conjunction fallacy
  • When hearing a complicated explanation that has too many convenient assumptions or vivid details (like an investment pitch for a business or an explanation for what caused a phenomenon), be aware that each additional assumption lowers the likelihood of it being true.
• Hindsight/Outcome Bias
  • Keep a journal of your current beliefs and what you estimate the outcomes to be. In...