Prediction and resilience

In Radical Uncertainty, the authors make a common argument: Instead of trying to predict the future, you should prepare for a wide range of scenarios, including ones you can’t even fully articulate. You should become more robust or resilient:

The attempt to construct probabilities is a distraction from the more useful task of trying to produce a robust and resilient defence capability to deal with many contingincies, few of which can be described in any but the sketchiest of detail.

This might make sense as an argument against certain types of forecasting exercises that organizations undertake. But it seems to me to still clearly require accurate prediction.

They’re essentially shifting the necessary prediction from the likelihood of a specific future outcome (will there be a recession, for example) to a prediction of the causal effect of a certain action across a range of unspecified outcomes.

For example, one way to be more resilient as a company is to hold more cash. Cash keeps your options open, so instead of trying to predict exactly where the economy is going, hold more cash.

But does that get you away from predictions and forecasting?

Not as far as I can tell. You’re basically predicting that, all else equal, your firm will do better (by whatever metrics) if you increase your cash holdings than if you don’t. You’re making a prediction about a causal effect, rather than about a specific external scenario. Prediction, though, is still a key part of the enterprise.

In their book Prediction Machines, Agarwal, Gans, and Goldfarb define prediction as “using information you have to generate information you don’t have.” They’re using the word more broadly than just predicting the future, but that definition has value even if we add “about the future” to the end of it.

The art of decision making isn’t about trying to predict everything that might have an influence on you. It’s that you use the information you have to generate information you don’t have; you make the predictions that seem most useful, that have the highest payoff.

Sometimes that is straightforward forecasting of a scenario: Will there be a recession next year? Sometimes it’s predicting the effect of a choice you’re considering, ahead of time: Will we be more likely to succeed if we do X to improve our cash holdings?

The difference isn’t that one of these involves prediction and the other says prediction is impossible. Both are bets about the future. The difference is in their potential payoff. Does the information you have give you any purchase on the question? Maybe your information suggests that recession forecasting is borderline impossible but there’s good evidence on the causal effects of increasing cash holdings. You’re likely to make a better prediction in the latter case.

Use the information you have to generate information you don’t have. That takes time and effort — to collect information, to analyze it, to apply it to the decision at hand. The question when you’re considering the value of a forecast isn’t Can I avoid making predictions here? It’s Is the effort I’ll put into this prediction the best use of my time given what I’m trying to achieve?

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