I do think many of your points are correct. The strongest argument to your post title I can think of is sparse evidence + multiple models and you’re uncertain between them → precise priors are unwarranted → use an interval instead, possibly quite wide like [10⁻⁶, 0.5] (just to make something up) → updating may not collapse this wide interval → so EV-maxxing becomes undefined → so switch to other decision criteria, e.g. maybe robustness to harms, which is less Bayesian as per your post title → choose robustly good actions, maintain option value, build capacity etc. Which is basically what most meta interventions are about, no?
This argument works with sharp probabilities too? If the distributions for the cost-effectiveness are very wide, the expected cost-effectiveness of decreasing uncertainty or building capacity would tend to be higher than the highest expected cost-effectiveness of the interventions under evaluation, even if these are all sharp values?
I do not want to give up completeness because it follows from 3 super intuitive premises.
Behaviour norms are considered for decision trees which allow both objective probabilities and uncertain states of the world with unknown probabilities. Terminal nodes have consequences in a given domain [premise 1; unrestricted domain]. Behaviour is required to be consistent in subtrees [premise 2; dynamic consistency]. Consequentialist behaviour, by definition, reveals a consequence choice function independent of the structure of the decision tree [premise 3; consequentialism]. It implies that behaviour reveals a revealed preference ordering [“a complete, transitive, binary relation”] satisfying both the independence axiom and a novel form of surething principle.
Hi Mo.
This argument works with sharp probabilities too? If the distributions for the cost-effectiveness are very wide, the expected cost-effectiveness of decreasing uncertainty or building capacity would tend to be higher than the highest expected cost-effectiveness of the interventions under evaluation, even if these are all sharp values?
I do not want to give up completeness because it follows from 3 super intuitive premises.