This is great. I take this opportunity to offer a feature request, and apologise I lack the technical facility to contribute to making it happen.
Insofar as I can tell the montecarlo is developing uniform or normal distributions for the data. A wider family of distributions would be extremely helpful, or (possibly even better) the ability to bootstrap a distribution via monte carlo for a limited set of original data. I suspect our brains are not too bad at guestimation in the cases of fairly well-behaved distributions, and the app will have even greater value if it allows us to navigate distributions which thank to significant skew or kurtosis have very counter-intuitive behaviours when combined together.
I agree. Glancing at the politics influence estimate, the final confidence interval seems much narrower than my intuitive judgement. One reason for that could be that the distributions should have wider tails. Or it might be mainly because there’s more uncertainty in the inputs than is being modeled here (if I read it correctly, the fraction of spending determined by policy and fraction of influence figures are taken as numbers rather than distributions).
It’s possible that it’s not just a matter of distributions types, but also that the model itself didn’t have enough uncertainty when written. Which would mean that the tool already shed light on something not obvious when in article form, if true.
This is great. I take this opportunity to offer a feature request, and apologise I lack the technical facility to contribute to making it happen.
Insofar as I can tell the montecarlo is developing uniform or normal distributions for the data. A wider family of distributions would be extremely helpful, or (possibly even better) the ability to bootstrap a distribution via monte carlo for a limited set of original data. I suspect our brains are not too bad at guestimation in the cases of fairly well-behaved distributions, and the app will have even greater value if it allows us to navigate distributions which thank to significant skew or kurtosis have very counter-intuitive behaviours when combined together.
Agreed that more distributions need to happen. It’s the #1 feature I’ll work on, after better insuring stability and so on.
I agree. Glancing at the politics influence estimate, the final confidence interval seems much narrower than my intuitive judgement. One reason for that could be that the distributions should have wider tails. Or it might be mainly because there’s more uncertainty in the inputs than is being modeled here (if I read it correctly, the fraction of spending determined by policy and fraction of influence figures are taken as numbers rather than distributions).
I would agree they seem quite narrow.
It’s possible that it’s not just a matter of distributions types, but also that the model itself didn’t have enough uncertainty when written. Which would mean that the tool already shed light on something not obvious when in article form, if true.