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1-Page Summary of Thinking, Fast and Slow
Daniel Kahneman starts by describing the two cognitive systems that make up our brains. System 1 is automatic, intuitive and involuntary. It’s used for simple math problems, reading sentences and recognizing objects as belonging to a category. System 2 requires effort and attention, but we tend to be lazy and rely on System 1 instead of using it properly, which leads us to making errors because of the biases in System 1.
Daniel Kahneman discusses System 1’s biases. The most obvious one is that people tend to believe things they hear because it’s easier for them to process. However, this doesn’t mean the information is accurate or true; another bias of System 1 is that it can cause people to like (or dislike) everything about a person, place or thing without giving any thought as to why they feel that way. Another example is substituting an easy question for a difficult one in order to avoid thinking too much about something and therefore coming up with the wrong answer.
The second section of the book focuses on bias in calculations. Our brains are not good at statistics, so we don’t realize that small samples can be more extreme than large ones. This leads us to make decisions based on insufficient data. We also tend to construct stories about statistical information even if there is no true cause for it.
We tend to overestimate the frequency of things that come to mind easily. For example, if you ask someone how many people die from cancer in a year, and then ask them how old Gandhi was when he died (which is an easy question), they will probably overestimate his age at death because it’s easier for them to think about him dying than other people dying. It also works with divorce rates: If you want to estimate the rate of divorced people over 60 years old, but can only remember one person who is divorced and over 60 years old, your estimation will be inaccurate because you’re basing your answer on just one person.
We underestimate statistics. For example, if we are told that a person is a computer science student and he fits the stereotype of one (Tom W), then we will think there’s a high probability that he actually belongs to this group. On the other hand, if someone fits the stereotype of being feminist (Linda) but she is not necessarily one, people will say that she’s more likely to be a bank teller than just any old bank teller.
When we try to predict the future, we often overestimate talent, stupidity and intention and underestimate luck. For example, if a golfer has a good first day in a tournament he is statistically more likely to have worse second day. We also tend to think that our predictions about the past are better than they actually were.
Kahneman then goes on to talk about how people are overconfident in their abilities and ideas. For example, he says that when he and a peer were observing soldiers for officer training, they would often make bad predictions based on gut feelings instead of using statistics or numerical records. He also talks about how we should rely more on checklists, statistics and numerical records than subjective feelings because it can lead to great progress such as the Apgar tests which have greatly reduced infant mortality rates.
Daniel Kahneman debunks the idea that financial analysts and newscasters are good at predicting future events. He works with Gary Klein to figure out when intuition can be trusted, and they find that these people have a lot of experience in their specific field. To become an expert, you need to have enough experience in your field so that you understand the patterns behind what is happening. Firefighters and chess masters are examples of those who fit this description well.