And I think this usually means some factors, in their units, like scale (e.g. number of individuals, years of life, DALYs amount of suffering) and probability of success (%), should be multiplied. And usually not weighted at all, except when you want to calculate a factor multiple ways and average them. Otherwise, you’ll typically get weird units.
And what is the unit conversion between DALYs and a % chance of success, say? This doesn’t make much sense, and probably neither will any weights, in a weighted sum. Adding factors with different units together doesn’t make much sense if you wanted to interpret the final results in a scope-sensitive way.
This all makes most sense if you only have one effect you’re estimating, e.g. one direct effect and no indirect effects. Different effects should be added. A more complete model could then be the sum of multiplicative models, one multiplicative model for each effect.
EDIT: But also BOTECs and multiplicative models may be more sensitive to their factors, and more sensitive to errors in factor values when ranking. So, it may be best to do sensitivity analysis, with a range of values for the factors. But that’s more work.
And I think this usually means some factors, in their units, like scale (e.g. number of individuals, years of life, DALYs amount of suffering) and probability of success (%), should be multiplied. And usually not weighted at all, except when you want to calculate a factor multiple ways and average them. Otherwise, you’ll typically get weird units.
And what is the unit conversion between DALYs and a % chance of success, say? This doesn’t make much sense, and probably neither will any weights, in a weighted sum. Adding factors with different units together doesn’t make much sense if you wanted to interpret the final results in a scope-sensitive way.
This all makes most sense if you only have one effect you’re estimating, e.g. one direct effect and no indirect effects. Different effects should be added. A more complete model could then be the sum of multiplicative models, one multiplicative model for each effect.
EDIT: But also BOTECs and multiplicative models may be more sensitive to their factors, and more sensitive to errors in factor values when ranking. So, it may be best to do sensitivity analysis, with a range of values for the factors. But that’s more work.