Odd to see an article about forecasting without mentioning DeMesquita and Smith's predictioneer models and stakeholder salience models. Also without Charlie Mun

Odd to see an article about forecasting without mentioning DeMesquita and Smith's predictioneer models and stakeholder salience models. Also without Charlie Munger's comments on incentives, which have had a better track record than pretty much anyone. There was much point scoring done after the 2016 U.S. election about polling and predictions being wrong, particularly against Fivethirtyeight, but where I think critics got it wrong was, when someone says there's a %95 chance of an event going in one way, it's not valuable as a prediction, but it certainly is as an accurate assessment of the impact of the results. In 2016, if you told people that the %95 implied probability of winning translated to what felt like paying out a 19/1 loss, a lot of people would have agreed with you. You can reliably bet on the effect of the upset being inversely proportional to the predicted probabilities. When I see election predictions that are 60:40 I think the 2nd order impacts of the result will be slightly lopsided, but within the realms of expectations. In this sense, %54 chance of winning close races may be better for stability than perceived polarized %95 ones because of how people perceive the results of a loss.