To me, the odds of pandemic and wars and regulation feel decently independent, but perhaps I haven’t thought deeply enough about pandemics causing depressions causing wars, or wars leading to engineered pandemics being released, etc.
Looking at the past 100ish years of history, the worst wars (world war I & II), the worst pandemics (various flus, COVID19), and the worst recessions (great depression, great recession) all seem fairly independent.
In any case, we tried our best to come up with probabilities of each, conditional on the others not occurring.
What probabilities would you assign?
As we asked in our post:
If you disagree with our admittedly imperfect guesses, we kindly ask that you supply your own preferred probabilities (or framework modifications). It’s easier to tear down than build up, and we’d love to hear how you think this analysis can be improved.
Yeah I think I would just bin all of delay into one bucket such that they are not independent. For instance, the causal chain of WWI, Great Depression, and WWII seem quite contingent upon one another. I’ll chew on how the binning works but nonetheless really appreciate this piece of work and it’s really easy to read and understand—as well as internally well reasoned. Didn’t mean to come off too harsh.
Not harsh at all; I genuinely appreciate the discussion. If there are good criticisms of our approach, I hope that we absorb them into an improved model rather than entrenching ourselves against them.
The issue I see with grouping these factors is then how do we figure what forecast to make for the collective group? The intuitive approach I’d take is to look at the rates of pandemics, world wars, etc. So it feels like we’d still be basing the estimate on mostly independent considerations even if we smush the final product together at the end.
Seems like a tricky forecasting problem in general. You don’t want a model with too many finicky specific scenarios, but you also don’t want amorphous uninterpretable blobs that arise from irreversibly blending many ingredients together.
A model with 1,000 parameters isn’t going to convince anyone and neither will a model with just 1. We tried to keep to a manageable range of 10 overall factors, backed by a few dozen subfactors. But definitely room to move in either direction.
To me, the odds of pandemic and wars and regulation feel decently independent, but perhaps I haven’t thought deeply enough about pandemics causing depressions causing wars, or wars leading to engineered pandemics being released, etc.
Looking at the past 100ish years of history, the worst wars (world war I & II), the worst pandemics (various flus, COVID19), and the worst recessions (great depression, great recession) all seem fairly independent.
In any case, we tried our best to come up with probabilities of each, conditional on the others not occurring.
What probabilities would you assign?
As we asked in our post:
Yeah I think I would just bin all of delay into one bucket such that they are not independent. For instance, the causal chain of WWI, Great Depression, and WWII seem quite contingent upon one another. I’ll chew on how the binning works but nonetheless really appreciate this piece of work and it’s really easy to read and understand—as well as internally well reasoned. Didn’t mean to come off too harsh.
Not harsh at all; I genuinely appreciate the discussion. If there are good criticisms of our approach, I hope that we absorb them into an improved model rather than entrenching ourselves against them.
The issue I see with grouping these factors is then how do we figure what forecast to make for the collective group? The intuitive approach I’d take is to look at the rates of pandemics, world wars, etc. So it feels like we’d still be basing the estimate on mostly independent considerations even if we smush the final product together at the end.
Seems like a tricky forecasting problem in general. You don’t want a model with too many finicky specific scenarios, but you also don’t want amorphous uninterpretable blobs that arise from irreversibly blending many ingredients together.
A model with 1,000 parameters isn’t going to convince anyone and neither will a model with just 1. We tried to keep to a manageable range of 10 overall factors, backed by a few dozen subfactors. But definitely room to move in either direction.