Below, I briefly discuss some motivating reasons, as I see them, to foster more interdisciplinary thought in EA. This includes ways EA’s current set of research topics might have emerged for suboptimal reasons.
The ocean of knowledge is vast. But the knowledge commonly referenced within EA and longtermism represents only a tiny fraction of this ocean.
I argue that EA’s knowledge tradition is skewed for reasons including but not-limited-to the epistemic merit of those bodies of knowledge. There are good reasons for EA to focus in certain areas:
Direct relevance (e.g. if you’re trying to do good, it seems clearly relevant to look into philosophy a bunch; if you’re trying to do good effectively, it seems clearly relevant to look into economics (among others) a bunch; if you came to think that existential risks are a big deal, it is clearly relevant to look into bioengineering, international relations, etc. a bunch; etc.)
Evidence of epistemic merit (e.g. physics has more evidence for epistemic merit than psychology, which in return has more evidence for epistemic merit than astrology; in other words, beliefs gathered from different fields are are likely to pay more/less rent, or are likely to be more/less explanatory virtuous)
However, some of the reasons we’ve ended up with our current foci may not be as good:
The, in parts arbitrary, way academic disciplines have been carved up
Inferential distances between knowledge traditions that hamper the free diffusion of knowledge between disciplines and schools of thought
Having a skewed knowledge basis is problematic. There is a significant likelihood that we are missing out on insights or perspectives that might critically advance our undertaking. We don’t know what we don’t know. We have all the reasons to expect that we have blindspots.
I am interested in the potential value and challenges of interdisciplinary research.
(Academic) incentives make it harder for transdisciplinary thought to flourish, resulting in what I expect to be an undersupply thereof. One way of thinking about why we would see an undersupply of interdisciplinry thought is in terms of “market inefficiencies”. For one, individual actors are incentivised (because it’s less risky) to work on topics that are already recognised as interesting by the community (“exploitation”), as opposed to venturing into new bodies of knowledge that might or might not prove insightful (“exploration”). What is “already recognized as valuable by the community”, however, will only in part be determined by epistemic considerations, and in another part be shaped by path-dependencies. For two, “markets” are insufficiently liquid and thus tend to fail where we cannot easily specify what we want. I’d argue that this is the case for DS/ET work. This is generally true for intellectual work, but is likely even more true for DS/ET work due to the relatively siloed structure of academia that adds additional “transaction costs” to attempts of communicating across disciplinary boundaries.
One way to reduce these inefficiencies is by improving the interfaces between the disciplines. “Domain scanning” and “episetmic translation” are precisely about creating such interfaces. Their purpose is to identify knowledge that is concretely relevant to a given target domain and make that knolwege accessible to thinkers entrenched in the “vocabulary” of that target domain. A useful interface between political philosophy and computer science, for example, might require a mathematical formalization of central ideas such as justice.
At the same time, doing interdisciplinary well is callenging. For example, interdisciplinary research can only be as valuable as a researcher’s ability to identify knowledge relevant to their target domain; or as a research community’s quality assurance/error correction mechanisms. Phenomena like citogenesis or motivatiogensis are examples of manifestations of these difficulties. There have been various attempts at overcoming these incentive barriers, for example the Santa Fe Institute whose organizational structure completely disregards scientific disciplines; -ARPAs have a similar flavour; the field of cybernetics which proposed an inherently transdisciplinary view on regulatory systems; or the recent surge in the literature on “mental models” (e.g. here or here).
A closer inspection of such examples—in how far they were successful and how they went about it—might bear some interesting insights. I don’t have the capacity to properly puruse such case studies in the near future, but it’s definteily something on my list of potentially promising (side) projects.
If readers are aware of other examples of innovative approaches trying to solve this problem that might make for insightful case studies, I’d love to hear them.
I think RAND is a good case study for interdisciplinary approaches to problem solving, though I’m biased. The key there, as in industry and most places other than academia, but unlike Santa Fe and the ARPAs, is a focus on solving concrete specific problems regardless of the tools used.Also, big +1 to cybernetics, which is an interesting case study for 2 reasons, first because of what worked, and second because of how it was supplanted / coopted into narrow disciplines, and largely fizzled out as its own thing.