I’d be interested to know how people think long-range forecasting is likely to differ from short-range forecasting, and to what degree we can apply findings from short-range forecasting to long-range forecasting. Could it be possible to, for example, ask forecasters to forecast at a variety of short-range timescales, fit a curve to their accuracy as a function of time (or otherwise try to mathematically model the “half-life” of the knowledge powering the forecast—I don’t know what methodologies could be useful here, maybe survival analysis?) and extrapolate this model to long-range timescales?
I’m also curious why there isn’t more interest in presenting people with historical scenarios and asking them to forecast what will happen next in the historical scenario. Obviously if they already know about that period of history this won’t work, but that seems possible to overcome.
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?
I’d be interested to know how people think long-range forecasting is likely to differ from short-range forecasting, and to what degree we can apply findings from short-range forecasting to long-range forecasting. Could it be possible to, for example, ask forecasters to forecast at a variety of short-range timescales, fit a curve to their accuracy as a function of time (or otherwise try to mathematically model the “half-life” of the knowledge powering the forecast—I don’t know what methodologies could be useful here, maybe survival analysis?) and extrapolate this model to long-range timescales?
I’m also curious why there isn’t more interest in presenting people with historical scenarios and asking them to forecast what will happen next in the historical scenario. Obviously if they already know about that period of history this won’t work, but that seems possible to overcome.
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?