The Promise of Polymaths

Academia neglects interdisciplinary work that combines insights from multiple areas. Each field has expert versions which experts use among themselves, and public versions seen by outsiders, and people in field B won’t accept your using the expert version of A if that differs from the non-expert version of A that B folks have in mind. Also, if you hold an academic event on the topic of A intersect B, you’ll usually invite the most prestigious people you can get in A, and in B, but you won’t usually invite people who have specialized in A intersect B, as they will tend to be less prestigious. Thus humanity’s beliefs on many important topics have long been just inconsistent and incoherent across disparate fields of inquiry. Creating a huge opportunity to learn lots of big stuff fast: search for more contradictions between fields, and resolve them. (More)

Here’s a pattern that I think I see: when intellectuals and academics use the same set of standard abstractions, and analysis tools built on those abstractions, then the top folks who have most mastered these tend to agree on their application. That is, they agree on what those concepts and tools, as applied well, suggest on particular topics.

Yes, conclusions often don’t rely entirely on such tools, as analysts add in other considerations, including strong priors. And yes, less than top people disagree more often on how to apply standard tools, especially when such tools are weak. Thus it seems to me that our deepest disagreements are due to a great degree to our using different sets of abstractions and tools, sets which often suggest conflicting conclusions in simple first pass analyses.

This seems confirmed by the fact that the rare polymaths who have mastered the same multiple sets of relevant abstractions also tend to agree more, even though they agree less than those who master only one standard set of abstractions (and related tools). More disagreement here is plausible, as, to apply multiple sets of abstractions, you’ll need to think about how to integrate them into a unified analytical framework, and if there is no standard way to do that, different polymaths may do that differently.

Thus most of our deep disagreements might be resolved if we could find ways to induce more people to learn multiple relevant sets of abstractions, to listen more to such learners, and to get them to work more on how to best integrate such sets. We don’t do this now due to the low status described above on interdisciplinary work.

Now, yes, it is easier for big disciplines to agree on who is best, and thus to reward people for being good. It seems at first glance harder to do this for a world of polymaths working on many different if overlapping sets of abstractions. But I can think of some ideas without much effort, and this seems a problem well worth working on.

Yes, even if we gave polymaths proper respect, and could evaluate them well, there’d still be lots of lower-quality analysis, and disagreement due to differing priors. But when top people came to focus on deep questions, we’d agree and be right a lot more.