1. The distinction between “wrong” vs. “early” has less to do with analytics than the social ability to prevent listeners from giving up on you. [...]
2. Credibility is not impartial: Your willingness to believe a prediction is influenced by how much you need that prediction to be true. [...]
It’s crazy to think you can impartially judge a prediction if the outcome of that prediction will impact your wellbeing. This is especially true if you need, rather than merely want, a specific outcome. [...]
3. History is the study of surprising events. Prediction is using historical data to forecast what events will happen next. [...]
Historical data is a good guide to the future. But the most important events in historical data are the big outliers, the record-breaking events. They are what move the needle. We use those outliers to guide our views of things like worst-case scenarios. But those record-setting events, when they occurred, had no precedent. So the forecaster who assumes the worst (and best) events of the past will match the worst (and best) events of the future is not following history; they’re accidentally assuming the history of unprecedented events happening doesn’t apply to the future. [...]
4. Predictions are easiest to make when patterns are strong and have been around for a long time – which is often when those patterns are about to expire. [...]
9. Predicting the behavior of other people relies on understanding their motivations, incentives, social norms and how all those things change. That can be difficult if you are not a member of that group and have a different set of life experiences.
No comments:
Post a Comment