Good questions! It’s a shame I don’t have good answers. I remember finding Spencer Greenberg’s framing helpful too but I’m not familiar with other useful practical framings, I’m afraid.

I suggested the Bayes’ factor because it seems like a natural choice of the strength/weight of an argument but I don’t find it super easy to reason about usually.

The final suggestion I made will often be easier to do intuitively. You can just to state your prior at the start and then intuitively update it after each argument/consideration, without any maths. I think this is something that you get a bit of a feel for with practice. I would guess that this would usually be better than trying to formally apply Bayes’ rule. (You could then work out your Bayes’ factor as it’s just a function of your prior and posterior but that doesn’t seem especially useful at this point/it seems like too much effort for informal discussions.)

Sorry for the slow reply. I don’t have a link to any examples I’m afraid but I just mean something like this:

This is just an example I wrote down quickly, not actual views. But the idea is to state explicit probabilities so that we can see how they change with each consideration.

To see you can find the Bayes’ factors, note that if P(W) is our prior probability that we should give weights, P(¬W)=1−P(W) is our prior that we shouldn’t, and P(W|A1) and P(¬W|A1)=1−P(W|A1) are the posteriors after argument 1, then the Bayes’ factor is

P(A1|W)P(A1|¬W)=P(W|A1)P(¬W|A1)P(¬W)P(W)=P(W|A1)1−P(W|A1)1−P(W)P(W)=0.650.350.40.6≈1.24Similarly, the Bayes’ factor for the second pro is 0.750.250.350.65≈1.62.