Perhaps you could see trying to think of analogies as sampling randomly in conceptspace from a reference class that the concept you are interested in belongs to.
Imagine a big book of short computer programs that simulate real-life phenomena. I’m working on a new program for a particular phenomenon I’m trying to model. I don’t have much data about my phenomenon, and I’m trying to figure out if a recursive function (say) would accurately model the phenomenon. By looking through my book of programs, I can look at the frequency with which recursive functions seem to pop up when modeling reality and adjust my credence that the phenomenon can be modeled with a recursive function accordingly.
Choosing only to look at pages for phenomena that have some kind of isomorphism with the one I’m trying to model amounts to sampling from a smaller set of data points from a tighter reference class.
This suggests an obvious way to improve on reasoning by analogy: try to come up with a bunch of analogies, in a way that involves minimal motivated cognition (to ensure a representative sample), and then look at the fraction of the analogies for which a particular proposition holds (perhaps weighting more isomorphic analogies more heavily).
I wouldn’t trust myself to sample randomly, so I prefer an adversarial approach: try to generate analogies that support each conclusion, then use them to figure out what evidence to look for.
+1 for adversarial approaches in general. I find that I’m more creative thinking of arguments if I’m trying to think of arguments to support a particular conclusion. So in order to gain maximum insight, I should try to think of all possible conclusions and then brainstorm arguments in favor of each.
Perhaps you could see trying to think of analogies as sampling randomly in conceptspace from a reference class that the concept you are interested in belongs to.
Imagine a big book of short computer programs that simulate real-life phenomena. I’m working on a new program for a particular phenomenon I’m trying to model. I don’t have much data about my phenomenon, and I’m trying to figure out if a recursive function (say) would accurately model the phenomenon. By looking through my book of programs, I can look at the frequency with which recursive functions seem to pop up when modeling reality and adjust my credence that the phenomenon can be modeled with a recursive function accordingly.
Choosing only to look at pages for phenomena that have some kind of isomorphism with the one I’m trying to model amounts to sampling from a smaller set of data points from a tighter reference class.
This suggests an obvious way to improve on reasoning by analogy: try to come up with a bunch of analogies, in a way that involves minimal motivated cognition (to ensure a representative sample), and then look at the fraction of the analogies for which a particular proposition holds (perhaps weighting more isomorphic analogies more heavily).
I wouldn’t trust myself to sample randomly, so I prefer an adversarial approach: try to generate analogies that support each conclusion, then use them to figure out what evidence to look for.
+1 for adversarial approaches in general. I find that I’m more creative thinking of arguments if I’m trying to think of arguments to support a particular conclusion. So in order to gain maximum insight, I should try to think of all possible conclusions and then brainstorm arguments in favor of each.
I like the idea of coming up with lots of analogies and averaging them or seeing if they predict things in common.