The path to 100% Using probabilities the right way

A few years ago, I wrote the following article, Probabilities that aren't

In it, I discussed the corrupting effect of the probability guesses that we all work with every day. I was reminded of it when I read David Spiegelhalter’s Does probability exist? Probably not — but it is useful to act as if it does. (Nature | Vol 636 | 19/26 December 2024) - in it, Spiegelhalter argues for one of my principles of asymmetric learning - obliquity - the intentionally unintentional consequences of process. That is, if we ask the right questions, in the right way, we will create more interesting observations, but they may not be the ones we expected.

Here’s my original piece:

The Probability of Technical Success, I am going to suggest, is a distracting, dangerous methodology.

1. We do the numbers, but no-one really believes them unless they're extreme (5%, 95%...), because they're useful in planning (as Philip Tetlock recounts the tale: 'future Nobel laureate Kenneth Arrow, when he was a young statistician during the Second World War, discovered that month-long weather forecasts used by the army were worthless, and warned his superiors against using them. He was rebuffed. “The Commanding General is well aware the forecasts are no good,” he was told. “However, he needs them for planning purposes.”'). That is, we know they're wrong, but the variance is wrong in both directions, so we use them because what else would you use?
2. We do the numbers so that it looks like we're putting governance into a subjective system. Better than telling the analysts about the HIPPO.
3. We do the numbers because we always did the numbers, and no-one has yet decided to try something different.

(There may well be other reasons you can tell me, but I haven't yet heard a defence of the idea of a no-feedback-loop system.) Most conversations suggest that there is an awful lot of gaming, or body English, used to make sure the numbers that we get are the numbers that are wanted: 'you don't need a weatherman to know which way the wind blows...'

• Q: Could you improve the numbers to be more 'true', even if they're not as good-looking, by using constructive feedback loops? (A: yes, of course.)
• Q: Could we imagine a better predictive system? (A: yes.)
• Q: Could we run a controlled study to see whether a better predictive system would produce better results over two years? (A: yes.)
• Q: Why would we not do all three of these? (A: ?)

This may well seem a specious argument. 'Well, with what would you replace them? McKinsey designed our process 10 years ago, and we know it is cr**, but just imagine the effort to uproot it and start again...' Well, as they say, 'your lack of imagination is not an argument.' An industry that believes (or says it believes) in precision, in diagnostics, and statistical analysis, should be in a healthier place.

In Spiegelhalter’s piece, he writes:

The latter has something in common with frequentist definition of objective probability, just with the class of repeated similar observations replaced by a class of repeated similar subjective judgements. In this view, if the probability of rain is judged to be 70%, this places it in the set of occasions in which the forecaster assigns a 70% probability. The event itself is expected to occur in 70% of such occasions.

Key to our processes is to understand what we are doing the calculations for, and to rethink our processes. Belief in any number would bring the awkward realisation of how rarely our teams all agree on the accuracy of any of the numbers they’ve produced. The solution, as we argue with our path to market approach, is that we do not rely on any one composite, but create multiple potential paths: they may well each contain their own assumptions, but we’re establishing the process in order to create a path to learn, rather than to confirm or deny.