Cyan, this wouldn’t be much harder to test that most social science signaling hypotheses. But since you don’t seem to know much social science, I don’t see the point in outlining to you how I’d go about that if I had the funding and time to do so.

I continue to get this sort of flak when I post on social science; most commenters here seem to consider friendly AI theory more well established than social science.

…every model is “oversimplified” in the sense of neglecting relevant details.

The question is whether it’s too simplified to be of any practical use.

I don’t know how I gave you the impression that I have “blind reliance” on my model as an “accurate representation.”

You feel it’s accurate enough to support the point you’re making in the penultimate paragraph. I’m not so sure.

As for economics modeling in general, I am more skeptical of it than I used to be. It’s a lot of fun to do in the armchair, but can be pretty dangerous in the real world, where it’s easy to miss a relevant detail with serious consequences, or have one’s model misapplied by others in inappropriate settings. Generic disclaimer of course don’t do much good (although given human nature I think they’re still better than nothing). More useful would be specific disclaimers about what the model assumes, guidelines on when it is likely or not likely to give sensible results, and rationales behind any technical modeling choices such as the specific form of utility function and whether variables are continuous or discrete (e.g. whether there is reason to believe that the model is insensitive to these choices).

I think people are complaining that there is too little, not too much, math. Robin’s post gave the impression of blind reliance on an oversimplified model, which people are understandably wary of, given the recent news stories about how oversimplified risk modeling was a major cause of the current economic crisis.

Robin wrote: Wei, I did not mean a zero chance of large noise.

Robin, in that case I’m not sure what you mean. You can clear this up by giving us the model you have in mind, with noise and repetition, and its equilibrium solutions.

But the complaints are not yet articulate enough to determine their source.

Can this math-esque model be tested experimentally? How could one measure e or p in the wild? How can reducing inherently massively multidimensional quantities to two scalars be justified? Is there any reason to expect that such a reduction would yield an accurate description of what goes on in human brains to even first order?

I’m not quite as skeptical as Brian Macker, but I can’t uncritically adopt this math as an accurate representation for what actually goes on in the real world. Suggestive, sure — conclusive, no. (Grant thinks it’s not intended to be realistic, but I don’t see anything in the post that would prompt that inference.)