on some readings of your post, “forecasting” becomes very broad and just encompasses all of research.
To add another perspective: Reasoning helps aggregating forecasts. Just consider one of the motivating examples for extremising, where, IIRC, some US president is handed the several (well-calibrated, say) estimates around ≈70% for P(head of some terrorist organisation is in location X)—if these estimates came from different sources, the aggregate ought to be bigger than 70%, whereas if it’s all based on the same few sources, 70% may be one’s best guess.
This is also something that a lot of forecasters may just do subconsciously when considering different points of view (which may be something as simple as different base rates or something as complicated as different AGI arrival models).
So from an engineering perspective there is a lot of value in providing rationales, even if they don’t show up in the final forecasts.
[Disclaimer: I’m working for FutureSearch]
To add another perspective: Reasoning helps aggregating forecasts. Just consider one of the motivating examples for extremising, where, IIRC, some US president is handed the several (well-calibrated, say) estimates around ≈70% for P(head of some terrorist organisation is in location X)—if these estimates came from different sources, the aggregate ought to be bigger than 70%, whereas if it’s all based on the same few sources, 70% may be one’s best guess.
This is also something that a lot of forecasters may just do subconsciously when considering different points of view (which may be something as simple as different base rates or something as complicated as different AGI arrival models).
So from an engineering perspective there is a lot of value in providing rationales, even if they don’t show up in the final forecasts.