Tag: Olivier Sibony

💎 Our tendency to underestimate the variance – or noise – in business

In a well-run insurance company, if you randomly selected two qualified underwriters or claims adjusters, how different would you expect their estimates for the same case to be? Specifically, what would be the difference between the two estimates, as a percentage of their average?

We asked numerous executives in the company for their answers, and in subsequent years, we have obtained estimates from a wide variety of people in different professions. Surprisingly, one answer is clearly more popular than all others. Most executives of the insur­ance company guessed 10% or less.  When we asked 828 CEOs and senior executives from a variety of industries how much variation they expected to find in similar expert judgments, 10% was also the median answer and the most frequent one (the second most popular was 15%). A 10% difference would mean, for instance, that one of the two underwriters set a premium of $9,500 while the other quoted $10,500. Not a negligible difference, but one that an organization can be expected to tolerate.

Our noise audit found much greater differences. By our measure. the median difference in underwriting was 55%, about five times as large as was expected by most people, including the company’s executives.

Excerpt from: Noise: A flaw in human judgement by Daniel Kahneman, Olivier Sibony and Cass R. Sunstein

💎 On how anchors influence us (even when they bear no relationship to the estimated value)

Surprisingly, anchors influence us even when they bear no relationship to the estimated value, and even when they’re patently absurd. Following the seminal experiments of Kahneman and Tversky in the 1970s, two German researchers named Thomas Mussweiler and Fritz Strack demonstrated this effect with remarkable creativity. In one of their experiments, they divided their subjects into two groups, asking one group whether Mahatma Gandhi was over or under 140 years old when he died, and the other whether he was over or under 9 years old when he died. Obviously, no one had trouble answering these questions. But when the respondents were then asked to estimate Gandhi’s age at death, these clearly ridiculous “anchors” made a difference: the group anchored on 140 thought, on average, that Gandhi had died at age 67, whereas the group anchored on 9 believed he had died at age 50. (Actually, Gandhi died at age 78.)

Excerpt from: You’re About to Make a Terrible Mistake!: How Biases Distort Decision-Making and What You Can Do to Fight Them by Olivier Sibony

💎 Kleiner Perkin’s tactic for avoiding their staff developing entrenched positions in meetings (flip-flop)

Another renowned venture capitalist, Kleiner Perkins’s Randy Komisar takes this idea one step further. He dissuades members of the investment committee from expressing firm opinions by stating right away that they are for or against an investment idea. Instead, Komisar asks participants for a “balance sheet” of points for and against the investment: “Tell me what is good about this opportunity; tell me what is bad about it. Do not tell me your judgment yet. I don’t want to know.” Conventional wisdom dictates that everyone should have an opinion and make it clear. Instead, Komisar asks his colleagues to flip-flop!

Excerpt from: You’re About to Make a Terrible Mistake!: How Biases Distort Decision-Making and What You Can Do to Fight Them by Olivier Sibony

💎 Analysing successful brands can be misleading (survivorship bias)

The models whose success we admire are, by definition, those who have succeeded. But out of all the people who were “crazy enough to think they can change the world,” the vast majority did not manage to do it. For this very reason, we’ve never heard of them. We forget this when we focus only on the winners. We look only at the survivors, not at all those who took the same risks, adopted the same behaviors, and failed. This logical error is survivorship bias. We shouldn’t draw any conclusions from a sample that is composed only of survivors. Yet we do, because they are the only ones we see.

Our quest for models may inspire us, but it can also lead us astray. We would benefit from restraining our aspirations and learning from people who are similar to us, from decision makers whose success is less flashy, instead of a few idols

Excerpt from: You’re About to Make a Terrible Mistake!: How Biases Distort Decision-Making and What You Can Do to Fight Them by Olivier Sibony