Jun 05, 2021

37 Quotes from Noise book by Daniel Kahneman, Olivier Sibony, et al

Noise book by Daniel Kahneman, Olivier Sibony, et al.jpg

Namaste friends. This post is a collection of quotes from the book - Noise by Daniel Kahneman, Olivier Sibony, et al. Noise is a revolutionary exploration of why people make bad judgments and how to make better ones.

Quotes

Wherever there is judgment, there is noise - and more of it than you think.

In noisy systems, errors do not cancel out. They add up.

Whether you make a decision only once or a hundred times, your goal should be to make it in a way that reduces both bias and noise.

Like a measuring instrument, the human mind is imperfect - it is both biased and noisy.

System noise is inconsistency, and inconsistency damages the credibility of the system.

Good decision making must be based on objective and accurate predictive judgments that are completely unaffected by hopes and fears, or by preferences and values.

In a perfect world, defendants would face justice; in our world, they face a noisy system.

You are not the same person at all times. As your mood varies, some features of your cognitive machinery vary with it. If you are shown a complex judgment problem, your mood in the moment may influence your approach to the problem and the conclusions you reach, even when you believe that your mood has no such influence and even when you can confidently justify the answer you found. In short, you are noisy.

You are not always the same person, and you are less consistent over time than you think. But somewhat reassuringly, you are more similar to yourself yesterday than you are to another person today.

Judgment is like a free throw: however hard we try to repeat it precisely, it is never exactly identical.

Noise in individual judgment is bad enough. But group decision making adds another layer to the problem. Groups can go in all sorts of directions, depending in part on factors that should be irrelevant. Who speaks first, who speaks last, who speaks with confidence, who is wearing black, who is seated next to whom, who smiles or frowns or gestures at the right moment - all these factors, and many more, affect outcomes.

In politics, as in music, a great deal depends on social influences and, in particular, on whether people see that other people are attracted or repelled.

Independence is a prerequisite for the wisdom of crowds. If people are not making their own judgments and are relying instead on what other people think, crowds might not be so wise after all.

We are able to see history only as it was actually run, but for many groups and group decisions, there are clouds of possibilities, only one of which is realized.

Ideas about politics and economics are a lot like movie stars. If people think that other people like them, such ideas can go far.

There is so much noise in judgment that a noise-free model of a judge achieves more accurate predictions than the actual judge does.

Many types of mechanical approaches, from almost laughably simple rules to the most sophisticated and impenetrable machine algorithms, can outperform human judgment. And one key reason for this outperformance - albeit not the only one - is that all mechanical approaches are noise-free.

What if we could use many more predictors, gather much more data about each of them, spot relationship patterns that no human could detect, and model these patterns to achieve better prediction? This, in essence, is the promise of AI.

What AI does involves no magic and no understanding; it is mere pattern finding.

Detailed long-term predictions about specific events are simply impossible. The world is a messy place, where minor events can have large consequences.

Asserting that the future is unpredictable is hardly a conceptual breakthrough. However, the obviousness of this fact is matched only by the regularity with which it is ignored.

As long as algorithms are not nearly perfect [...] human judgment will not be replaced. That is why it must be improved.

We think we understand what is going on here, but could we have predicted it?

Life is often more complex than the stories we like to tell about it.

Even the most enthusiastic proponents of AI agree that algorithms are not, and will not soon be, a universal substitute for human judgment.

Judgments are both less noisy and less biased when those who make them are well trained, are more intelligent, and have the right cognitive style. In other words: good judgments depend on what you know, how well you think, and how you think.

Noise can be an invisible problem, even to people whose job is to see the invisible.

In any judgment, some information is relevant, and some is not. More information is not always better, especially if it has the potential to bias judgments by leading the judge to form a premature intuition.

Confirmation bias can lead you to form an overall impression too early and to ignore contradictory information. The titles of two Hitchcock movies sum it up: a good decision maker should aim to keep a “shadow of a doubt,” not to be “the man who knew too much.”

The second opinion is not independent if the person giving it knows what the first opinion was.

Among doctors, the level of noise is far higher than we might have suspected. In diagnosing cancer and heart disease - even in reading X-rays - specialists sometimes disagree. That means that the treatment a patient gets might be a product of a lottery.

Most ratings of performance have much less to do with the performance of the person being rated than we would wish.

We spend a lot of time on our performance ratings, and yet the results are one-quarter performance and three-quarters system noise.

If algorithms make fewer mistakes than human experts do and yet we have an intuitive preference for people, then our intuitive preferences should be carefully examined.

It might be costly to remove noise - but the cost is often worth incurring. Noise can be horribly unfair. And if one effort to reduce noise is too crude [...] we shouldn’t just give up. We have to try again.

A rule-bound system might eliminate noise, which is good, but it might also freeze existing norms and values, which is not so good.

Creative people need space. People aren’t robots. Whatever your job, you deserve some room to maneuver. If you’re hemmed in, you might not be noisy, but you won’t have much fun and you won’t be able to bring your original ideas to bear.

Variability as such is unproblematic in some judgments, even welcome. Diversity of opinions is essential for generating ideas and options. Contrarian thinking is essential to innovation. A plurality of opinions among movie critics is a feature, not a bug. Disagreements among traders make markets. Strategy differences among competing start-ups enable markets to select the fittest. In what we call matters of judgment, however, system noise is always a problem. If two doctors give you different diagnoses, at least one of them is wrong.

Some noise may be inevitable in practice, a necessary side effect of a system of due process that gives each case individualized consideration, that does not treat people like cogs in a machine, and that grants decision makers a sense of agency.

Many concerns about algorithms are overblown, but some are legitimate. Algorithms may produce stupid mistakes that a human would never make, and therefore lose credibility even if they also succeed in preventing many errors that humans do make. They may be biased by poor design or by training on inadequate data. Their facelessness may inspire distrust.