Noise

Need a good level of statistics knowledge to understand the key points and follow the chapters

This book examines how we make judgments and why so many judgments are flawed – including those that the makers believe to be correct judgments. The authors identify two prevailing problems in human judgments decisions. The first is bias and the second is noise. Bias is a subject that is covered in great depth and detail in Daniel Kahneman’s (one of the three authors of this book under review) book, ‘Thinking, Fast and Slow’ (2013 Farrar, Straus & Giroux).‘Noise’ here, is about – well, noise, in the decision-making process and the decisions themselves. Noise is the diversity of decisions or conclusions on the same question. By way of a simple illustration, if we have a case in which a 40-year-old man, with a family of five, is convicted of stealing a loaf of bread, and two judges are asked to decide the sentence on him, one says jail for a day and the other says jail for a month. The different outcomes are the noise that conceals the correct judgment. One of them must be wrong. The authors show why understanding noise is important. In their discussions, they question the utility and differences between rules and standards and how they might be applied to reduce noise in decision-making. They pose the question: ‘Who counts as disabled, such that they should qualify for economic benefits reserved for those who are unable to work?’ The authors maintain that if the question is phrased in this way, ‘judges will make ad hoc decisions that will be noisy and unfair’. They show how standards and rules approach may often result in less noisy, and fairer judgments. ‘If doctors are given clear guidelines to decide whether patients have strep throat, their decisions might be fast and relatively straightforward.’ This book is a primer for all decision makers, not only in the professional fields, but also in administrative work, and even all of us making decisions in the domestic settings. But, if leaders of domestic organisations use algorithms either to replace human judgment or supplement it, would that be desirable? Are we – should we – be prepared to displace discretion for rules? That, perhaps is a matter of distinguishing two different situations. The first is one where the facts are the same. The second is where the facts are not uniform. Even if we were to disagree with some of the claims of the authors, this is a book that will stimulate the mind of every decision maker.

Great read

I really have no idea who the intended audience was for this book: the authors really, really dumb it down, to the point of explaining what variance is over several pages of prose. We did not all fail high school.At the same time, they bring into the discussion some serious tools you won’t even meet until you get to graduate school in statistics, like the “percentage concordant,” which is not some type of supersonic airplane, but a rank correlation type of measure, and even provide a mini-table to move you from percentage concordant (PC) to correlation. The table, by the way, is bogus in the absence of context, as percentage concordant is a construct that I’m willing to bet relies heavily on assumptions that go unmentioned here.The chapters end with summaries, which was OK for Thinking Fast and Slow, but a bit of an insult when the subject matter is so plain.The style is pompous and paternalistic.System A and System B are parachuted in, but (i) they’re barely explained (ii) that’s a theory to explain bias rather than noise (and invite a celebrity author to the proceedings)Most annoyingly, terribly little ground is covered in this weighty tome. Gun to my head, I could probably get it all down to one page. Let me try:1. Noise is just as bad as bias in terms of messing up your results2. A good way to measure how bad your results are is the mean square error3. Composition of Mean Square Error:• Mean square error is made up of Bias and Noise• Noise is made up of Level Noise and Pattern Noise• Pattern Noise is made up of Stable Pattern Noise and Occasion Noise• Level Noise is the kind of noise that comes from the fact that some judges are harsh and some are lenient, so two guys who did the same crime could get very different punishment.• Pattern Noise is the kind of noise that comes from the fact that a judge may have a daughter, making him less harsh on young women that remind him of his daughter. He could be a harsh judge who is less harsh on young women who remind him of his daughter; or he could be a lenient judge who is extra lenient on young women who remind him of his daughter.• Occasion Noise is the kind of noise that comes from the fact that judges are harsher right before lunch. Same judge, same crime, same perpetrator, different outcome, because it was a different occasion4. If you ask people to measure something independently from one another, the more the merrier; but if they talk to each other first, then they will amplify errors for a variety of reasons that lead to groupthink5. Machines beat people when it comes to cutting noise6. In the quest to limit noise, people can fight back by sticking to simple rules7. We humans like to build stories after the fact to explain what happened; they’re usually bogus: statistical explanations beat causal explanations8. Bias can be the source of noise: inconsistency in bias is noise9. Noise can arise when you’re told to rank things on a scale; to cut noise, it’s better to go ordinal than cardinal10. To improve judgements you need (i) better judges (ii) a decision process that aggregates in a way that maintains independence among the judges (iii) guidelines (iv) relative rather than absolute judgements11. There is a place for intuition: it’s got to be brought in at the very end, after all the mechanical work has finished12. There actually is a place for noise: when people are bound to game the systemRead something else!

Daniel Kahneman is a Nobel-prize-winning economist and this explanation of his findings builds on his previous book, "Thinking Fast and Slow". They don't cover the same ground and I would say that they are both books that everybody should read as they contain insights that can change the way you think.Essentially this book analyses why decision-making, even by experts, is often highly variable and therefore potentially wrong. The authors look at variability (which they call noise) between different individuals, different organisations and even the same individual at different times. Mostly the individuals concerned, and the organisations employing them, are completely unaware that such variability exists at all and yet it is responsible for inconsistencies in sentencing by judges, recruiting employees, assessing insurance claims and indeed anything where a judgement has to be made.It is fascinating, very clear and readable, and will, I think, help many people to make better decisions of all kinds.

Can't wait to start reading this as I am doing psychology and criminology at university and always buy from amazon as good value for money.