If only Matt Hancock had read Noise. He might not be hanging onto his Cabinet post with stretchy fingernails, and the Covid strategy would have been less chaotic. If only Boris Johnson had read Noise. He might not have appointed Dominic Cummings in the first place. If only Dominic Cummings had read Noise. He might have opted for a modest lectureship at Essex University instead of aspiring to become The Mighty Mekon of Dan Dare Downing Street – or Barnard Castle, family commitments permitting.
On the other hand, Father Daniel Humphreys has obviously read Noise. So, he did not let the extraneous buzzing of two irritating previous non-Catholic marriages get in the way of his Westminster Cathedral service for Boris and Carrie. Father Humphreys is an exemplar of selective noise elimination as well as a Canon Law nit-picker.
To cut to the end of a thrilling 464-page chase of a book, Noise provides every one of us with a practical roadmap for making better decisions. Minimising bias, ignoring extraneous information clutter and accurately recognising patterns in a series of facts will make us less random.
The book deals more with correcting inconsistencies in the operation of institutions, the justice system, decision-making processes, medical diagnostics, human resource management, asset allocation and personnel selection, rather than everyday personal individual decisions.
But I did find myself applying its lessons to the breakfast menu yesterday, opting for yoghurt and abandoning the stacked pancakes with maple syrup. Damn Noise!
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The authors who may well change your life, or how you appraise the institutions that govern it day to day, are Daniel Kahneman, Olivier Sibony and Cass R. Sunstein. Amazon gets quickly bored with multi-authors and unkindly demotes Mr Sunstein to “et al” in its online shop blurb.
Nowhere in the book is it explained why the trio decided to co-operate on the project, nor is any claim made as to leadership. Kahneman may be a Nobel Laureate, but his precedence on the title page is a pure alphabetical accident. It also happens to be good for sales, as he is the best known of the three.
They may form a Trinity of equal rank, but the Nobel prize makes Kahneman the most famous. Nowhere in the book are individual contributions identified. Stylistically the writing is seamless. As the authors all identify as male, fictitious actors in the book are cunningly identified as female. There is no ostentation in achieving this wry, politically correct balance. No finger-wagging lecturing. It’s just amusing.
Kahneman is an Israeli psychologist and economist, acknowledged for his work on the psychology of judgment, decision-making and behavioural economics, for which he was awarded the 2002 Nobel Memorial Prize in Economic Sciences.
Sibony is a French professor focused on strategic thinking and the design of decision processes. Sunstein is an American legal scholar with experience in government, notably the Obama White House, and co-author of Nudge, (2008), which is about improving decisions on health, wealth and happiness.
Their qualifications meld well to address the issues identified in this book. All three have extensive experience in the private sector, so they bring a balance of academic rigour and real-world experience to their task. At times I wondered if the book should not have been retitled, Hire Us: We’re the Best in the Business. Maybe readers who want to reshape their organisations would be well advised to do so, but the book is much more than a puff for the authors’ consultancy services.
They have so much disarming self-confidence in their conclusions that early on, they recommend some readers easily bored with academic explanations might not want to read the whole thing. Skip to the conclusions and recommendations towards the end. In your interests, dear readers, I ignored that recommendation.
However, I do recommend that anyone averse to close-quarter academic jungle warfare might flip to the end first if only to assure themselves the view of the promised shining city on the sea of the conclusion is worth the effort of hand-to-hand machete-hacking through the thickets of tortuous logic, statistics, graphs and examples in the preceding 350 pages. The city does not disappoint. But, it is sometimes a difficult read to reach.
The book is divided into six parts. Part I: Finding Noise. Part II: Your Mind is a Measuring instrument. Part III: Noise in Predictive judgements. Part IV: How Noise Happens. Part V: Improving Judgements. Part VI: Optimal Noise. You can see the progression from theory to solution.
What is Noise? Noise is extraneous information or innate prejudice that prevents consistency in reaching decisions, especially critical when many actors are applying standards in justice, medicine, immigration rules and employee assessment. This is only an illustrative list.
The introduction figures a team of target shooters whose shots always fall to the right of the bull’s-eye – exhibiting a bias, as is a judge who always sentences black people more harshly than other ethnicities. That’s bad, but at least they are consistent, which means the biases can be identified and corrected.
But another team whose shots are scattered in different directions away from the target is shooting “noisily”, and that’s harder to correct. A third team whose shots all go to the left of the bull’s-eye but are scattered high and low is both biased and noisy. That’s “noise” for you. Think Cabinet.
Noise is rarely recognised, let alone counteracted. This is why the authors’ noise examples are so compelling and why gathering the examples in one place to demonstrate the cost of noise and then suggesting noise reduction techniques, or “decision hygiene,” makes the book so important. Noisy institutions inevitably undermine trust. Eradicating the noise that leads to random, unfair decisions will help us regain trust.
Noise seems certain to make a mark by calling attention to the problem and providing a tangible guide to reducing it. Despite the authors’ intimidating academic credentials, they take pains to explain, even with occasional redundancy, their various categories of noise, the experiments and formulae they deploy in their alchemic kit of noise elimination.
Why is it so important to address noise and bias? For starters, it can produce considerable inconsistencies in judicial sentencing policy. A study of 1.5 million cases in the US found that when judges pass down sentences on days when the local city’s football team lose, they are tougher than when their team wins. The study was consistent with a steady stream of anecdotal reports beginning in the 1970s that showed sentencing decisions for the same crime varied dramatically — indeed scandalously — for individual judges and depending on which judge drew a particular case.
It also matters in medicine. Wide variation in surgical outcomes across our own NHS emerged in the 1990s. The medical establishment rushed to their familiar redoubt of instant denial. A rational process of identifying and mitigating risk was required.
The book lights on a study at a US oncology centre that found that the diagnostic accuracy of melanomas was only 64 per cent, meaning that doctors misdiagnosed melanomas in one of every three lesions. There are similar instances to be found worldwide.
When two psychiatrists conducted independent reviews of 426 patients in US state hospitals, they came to the equivalent of a tossup: agreement 50 per cent of the time on what kind of mental illness was present.
In the commercial world, risk assessment is at the centre of many decision-making processes. When a large insurance company, concerned about quality control, asked its underwriters, who determine premium rates based on risk assessments, to come up with estimates for the same group of sample cases, their suggested premiums varied by an eye-popping median of 55 per cent. This means that one adjuster might have set a premium at $9,500 while a colleague set it at $16,700. The point is, perhaps both are wrong, but there is no established process for telling which is correct.
I do have some shortcoming quibbles. There is the occasional lapse in applied logic. Here is an example, rationalising the explanation of differing opinions:
“All the analysts believe that there is a correct view and that their own view is the one closest to it. As you listen, you may find several of the analysts equally impressive and their arguments equally convincing. You cannot know then which of them is correct (and you may not even know later, if their analyses are not formulated as clearly verifiable predictions). You know that at least some of the analysts are wrong, because they are in disagreement.”
The problem with that explanation is it ignores a possible logical conclusion that they may all be wrong. Elsewhere in the book, the point is made that everyone is fallible, but that’s a bit of a cop-out.
Banality can rear its ugly head. “If you must pick people to make judgments, picking those with the highest mental ability makes a lot of sense.” Wow! Never thought of that.
Of high relevance today is how the lessons in Noise can be successfully deployed to better handle current pandemics – Covid-19 – and those that will inevitably follow in its wake. The latter chapters provide an excellent handbook for those responsible for shaping response structures and ensuring they run smoothly.
There is a clarion call for better information, but this is by no means a “machines and artificial intelligence logarithms beat humans” rant. Rather, it argues for frameworks that make use of up-to-date technology while providing structures – set out in three appendices – that might just save ordinary mortals from their fallibility. It boasts 13 pages of comprehensive notes, which do not clutter the bottom of the pages as one goes along. An excellent index adds considerably to the work’s accessibility.
Read this book, become less “noisy” and, in the words of the Epilogue, you will be playing your part “in saving a great deal of money, improving public safety and health, increasing fairness and preventing many avoidable errors.” But, sadly, no more pancakes and maple syrup.