This is surprising. Now two things can happen that will change your interpretation of the event:. In both cases, System 1 assigns cause and effect without any conscious thought. This works much of the time, but it fails when you apply causal thinking to situations that require statistical thinking. Conversely, maintaining multiple incompatible explanations requires mental effort and System 2.
Tangent: note that you interpret your own actions differently from other people. When you act, you experience the action as a decision that a disembodied you made. The world of your mind is separate from the world of objects, which makes it possible to envision soulless bodies and bodiless souls, which then can give rise to religions. The implications of priming are profound — if we are surrounded everyday by deliberately constructed images, how can that not affect our behavior?
And likewise, if we are required to behave in a certain way eg in a workplace, social community, as citizens , does that not affect our cognition and beliefs? The effects may not be huge — but a few percentage points by swinging marginal voters can make a difference. Note the irony about being biased about biases. The field hungered for confirming evidence itself, becoming too ready to accept a neat story priming without employing its System 2 thinking to question whether the evidence was valid!
System 1 is humming along, and System 2 accepts what System 1 says. Again, cognitive ease is a summary feeling that takes in multiple inputs and squishes them together to form an impression. The basic assessments include language, facial recognition, social hierarchy, similarity, causality, associations, and exemplars. However, not every attribute of the situation is measured.
System 1 is much better at determining comparisons between things and the average of things, not the sum of things. These basic assessments are not impaired when the observer is cognitively busy eg with a memory task. You rarely face situations as mentally taxing as having to solve x in your head. You like or dislike people before you know much about them; you feel a company will succeed or fail without analyzing it.
When faced with a difficult question, System 1 substitutes an easier question , or the heuristic question. System 2 has the opportunity to reject this answer, but a lazy System 2 often endorses the heuristic without much scrutiny. When reversed, the correlation was very high. The first question prompted an emotional response, which was then used to answer the happiness question. This is acceptable if the conclusions are likely to be correct, the costs of a mistake are acceptable, and if the jump saves time and effort.
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When presented with evidence, especially those that confirm your mental model, you do not question what evidence might be missing. System 1 seeks to build the most coherent story it can — it does not stop to examine the quality and the quantity of information. In an experiment, groups were given background to a legal case. Those given only one side gave a more skewed judgment, and were more confident of their judgments than those given both sides, even though they were fully aware of the setup.
If you think positively about something, it extends to everything else you can think about that thing. Say you find someone visually attractive and you like this person. You more likely find her intelligent or capable, even if you have no evidence of this. This forms a simpler, more coherent story by generalizing one attribute to the entire person. First impressions matter. It takes a lot of work to reorder the impressions to form a new trunk. 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. Explained more in Part 2. People want to believe a story and will seek cause-and-effect explanations in times of uncertainty. This helps explain the following:.
Once a story is established, it becomes difficult to overwrite. This helps explain why frauds like Theranos and Enron were allowed to perpetuate — observers believed the story they wanted to hear. A good treatment of this here. Experiments show that when System 2 is taxed eg when holding digits in memory , you become more susceptible to false sentences. The general theme of these biases: we prefer certainty over doubt. We prefer coherent stories of the world, clear causes and effects. Sustaining incompatible viewpoints at once is harder work than sliding into certainty.
A message, if it is not immediately rejected as a lie, will affect our thinking, regardless of how reliable the message is. The general theme is that we pay more attention to the content of the story than to the reliability of the data. We prefer simpler and coherent views of the world and overlook why those views are not deserved.
We overestimate causal explanations and ignore base statistical rates. Often, these intuitive predictions are too extreme, and you will put too much faith in them. The smaller your sample size, the more likely you are to have extreme results. Do NOT fall for outliers.
Here System 1 is finding spurious causal connections between events, too ready to jump to conclusions that make logical sense. With a surprising result, we immediately skip to understanding causality rather than questioning the result itself. Even professional academics are bad at understanding this — they often trust the results of underpowered studies, especially when the conclusions fit their view of the world. You are not adequately sensitive to sample size. Obviously, if the figures are way off 6 seniors were asked, or million were asked System 1 detects a surprise and kicks it to System 2 to reject.
People tend to expect randomness to occur regularly. For coin flips following sequences all have equal probability:.
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However, sequence 3 looks far more random. Sequence 1 and 2 are more likely to trigger a desire for alternative explanations. Evolutionarily, this might have arisen out of a margin of safety for hazardous situations. Individual cases are often overweighted relative to mere statistics. The taxicab problem shows how causal base rates are overweighted relative to statistical base rates. This was shown to great effect when psychology students were taught about troubling experiments like the Milgram shocking experiment, where 26 of 40 participants delivered the highest voltage shock.
Students were then shown videos of two normal-seeming people, not the type to voluntarily shock a stranger. How likely were these individuals to have delivered the highest voltage shock? This is odd. They had exempted themselves from the conclusions of experiments. The antidote to this was to reverse the order — students were told about the experimental setup, shown the videos, and only then told the outcome of how the two ordinary people they saw had failed.
Their estimate of the failure rate was much more accurate. Over repeated sampling periods, outliers tend to revert to the mean. High performers show disappointing results, and strugglers show sudden improvement. This occurs when the correlation between two measures is imperfect. When we have a cute causal explanation for regression to the mean, we can come up with superstitions and misleading rules.
Anchoring: When you are exposed to a number, the number affects your estimate of an unknown quantity. This happens even when the number has no meaningful relevance to the quantity to be estimated. But again even meaningless numbers, even dice rolls, can anchor you. Insidiously, people take pride in their supposed immunity to anchored numbers. Instead, threaten to end the negotiation if that number is still on the table. More quantitatively, when trying to estimate the size of a category or the frequency of an event, you instead report the heuristic of the ease with which instances come to mind.
The conclusion is that System 1 uses ease of recall as a heuristic, while System 2 focuses on content. He likes soft music and wears glasses. Which profession is Tom W. If you picked librarian without thinking too hard, you used the representativeness heuristic — you matched the description to the stereotype, without thinking about the base rates. More generally, representativeness is when we estimate the likelihood of an event by comparing it to an existing prototype in our minds — matching like to like.
Ideally, you should have examined the base rate of both professions in the male population, then adjusted based on his description. Construction workers outnumber librarians by in the US — there are likely to be more shy construction workers than all librarians. And even when base rate data are given , the representativeness is still weighted more than the statistics. Representativeness is used because System 1 desires coherence, and matching like to like forms a coherent story that is simply irresistible.
Insidiously, the representativeness heuristic works much of the time. The taxicab problem is another fun example where subjects confuse the probability of events. Apply Bayesian statistics. A simple form: Start by predicting the base rates. Consider how the new data should adjust the base rates. Eg Tom W is 4 times more likely to be a librarian than construction worker. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. If you guessed 2, you fell for the conjunction fallacy.
However, 2 explicitly mentioned a coherent story and thus seemed more representative of Linda, even though being more statistically unlikely.
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The latter is more plausible because of the vividness of its detail, but it is certainly less probable. This is a problem for forecasters — adding details to scenarios makes them more persuasive, but less likely to come true. More examples here. But when viewed separately, people are willing to pay less for 2 than 1. This was also shown when mixing common cards with star cards lowered their price.
She read fluently when she was four years old. What is her grade point average GPA? Notice how misled this is! People feel confident to predict an outcome 2 decades later because of appearances of causality and intensity matching. This approach is generalizable. It avoids overly extreme results from intuition, uses base rates, and assesses the quality of information.
However, Kahneman notes that absence of bias is not always what matters most. A reliance in statistics would avoid the prediction of rare or extreme events on shaky information. But for those who are merely interested in Kahenman's takeaway on the Malcolm Gladwell question it is this: If you've had 10, hours of training in a predictable, rapid-feedback environment--chess, firefighting, anesthesiology--then blink. In all other cases, think. It now seems inevitable that Kahneman, who made his reputation by ignoring or defying conventional wisdom, is about to be anointed the intellectual guru of our economically irrational times.
Goldstein, The Chronicle of Higher Education. This is one of the greatest and most engaging collections of insights into the human mind I have read. To anyone with the slightest interest in the workings of his own mind, it is so rich and fascinating that any summary would seem absurd. What's most enjoyable and compelling about Thinking, Fast and Slow is that it's so utterly, refreshingly anti-Gladwellian. There is nothing pop about Kahneman's psychology, no formulaic story arc, no beating you over the head with an artificial, buzzword-encrusted Big Idea.
It's just the wisdom that comes from five decades of honest, rigorous scientific work, delivered humbly yet brilliantly, in a way that will forever change the way you think about thinking.
Two Brains Running
As Copernicus removed the Earth from the centre of the universe and Darwin knocked humans off their biological perch, Mr. Kahneman has shown that we are not the paragons of reason we assume ourselves to be. We like to see ourselves as a Promethean species, uniquely endowed with the gift of reason.
But Mr. Kahneman's simple experiments reveal a very different mind, stuffed full of habits that, in most situations, lead us astray. Kahneman's notable contributions, over five decades, to the study of human judgment, decision-making and choice. Thanks to the elegance and force of his ideas, and the robustness of the evidence he offers for them, he has helped us to a new understanding of our divided minds--and our whole selves.
Chabris, The Wall Street Journal. Call his field "psychonomics," the hidden reasoning behind our choices. Thinking, Fast and Slow is essential reading for anyone with a mind. The work of Kahneman and Tversky was a crucial pivot point in the way we see ourselves. Nobel-winning psychologist Kahneman Attention and Effort posits a brain governed by two clashing decision-making processes. The largely unconscious System 1, he contends, makes intuitive snap judgments based on emotion, memory, and hard-wired rules of thumb; the painfully conscious System 2 laboriously checks the facts and does the math, but is so "lazy" and distractible that it usually defers to System 1.
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Kahneman uses this scheme to frame a scintillating discussion of his findings in cognitive psychology and behavioral economics, and of the ingenious experiments that tease out the irrational, self-contradictory logics that underlie our choices. We learn why we mistake statistical noise for coherent patterns; why the stock-picking of well-paid investment advisers and the prognostications of pundits are worthless; why businessmen tend to be both absurdly overconfident and unwisely risk-averse; and why memory affects decision making in counterintuitive ways.
Kahneman's primer adds to recent challenges to economic orthodoxies about rational actors and efficient markets; more than that, it's a lucid, marvelously readable guide to spotting--and correcting--our biased misunderstandings of the world. Before Malcolm Gladwell and Freakonomics, there was Daniel Kahneman who invented the field of behavior economics, won a Nobel…and now explains how we think and make choices. Here's an easy choice: read this. Before computer networking got cheap and ubiquitous, the sheer inefficiency of communication dampened the effects of the quirks of human psychology on macro scale events.
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