If forecasters are giving forecasts for similar things over different times, their resolution should very obviously decrease with time. A good example of this are time series forecasts, which grow in uncertainty over time projected into the future.
To site my other comment here, the tricky part, from what I could tell is calibration, but this is a more narrow problem. More work could definitely be done to test calibration over forecast time. My impression is that it doesn’t fall dramatically, probably not enough to make a very smooth curve. I feel like if it were the case that it reliably fell for some forecasters, and those forecasters learned that, they could adjust accordingly. Of course, if the only feedback cycles are 10-year forecasts, that could take a while.
I’m not sure what you mean by resolution. But if you mean accuracy, perhaps a counter example is the reversion of stock values to the long-term mean appreciation curve creating value forecasts that actually become more accurate five or 10 years out than in the near term?
If forecasters are giving forecasts for similar things over different times, their resolution should very obviously decrease with time. A good example of this are time series forecasts, which grow in uncertainty over time projected into the future.
To site my other comment here, the tricky part, from what I could tell is calibration, but this is a more narrow problem. More work could definitely be done to test calibration over forecast time. My impression is that it doesn’t fall dramatically, probably not enough to make a very smooth curve. I feel like if it were the case that it reliably fell for some forecasters, and those forecasters learned that, they could adjust accordingly. Of course, if the only feedback cycles are 10-year forecasts, that could take a while.
Image from the Bayesian Biologist: https://bayesianbiologist.com/2013/08/20/time-series-forecasting-bike-accidents/
I’m not sure what you mean by resolution. But if you mean accuracy, perhaps a counter example is the reversion of stock values to the long-term mean appreciation curve creating value forecasts that actually become more accurate five or 10 years out than in the near term?