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What previous work has been done on factors that affect the pace of technological development?

by Megan Kinniment1 min read27th Apr 20216 comments

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Speeding up developmentScientific progressDifferential progressForecasting
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Does anyone know of any previous work looking at different factors that might contribute to the pace at which some area or technology develops?

I have had a look at the differential technological progress tag and Michael Aird's very useful collection of resources on DTD. However, most of these seem more geared towards explaining the concept and its importance, rather than attempting to find levers that we may be able to pull to influence things, which I am more interested in. 

A couple of  examples of the flavour of work that I'm more interested in are:

  • A 2017 Open Phil report called Some Case Studies in Early Field Growth, which looks at times philanthropists tried to grow a young field.
  • Sections 2.2 and 2.3 of this paper by MIRI (the "speed bumps" and "accelerators" sections), which talks about a few relatively concrete factors that could affect AI development pace.

I'm particularly interested in any work on factors that might apply to the development of a wide range of technologies, and that apply for early field development.

Any suggestions would be really helpful to me, even if they are more loosely related to this area.

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One good resource is Innovation in Cultural Systems: Contributions from Evolutionary Anthropology. I think that is kind of what you're after? I wrote a little about this here:

Though innovation seems to be happening at breakneck speed, there is nothing abrupt about it. Changes are small & cumulative.[6] New ideas are based on old ideas, on recombinations of them & on extending them to new domains.[7] This does not make those ideas any less important. An illustrative example is the lightbulb, the history of which is one of incremental improvement. [...]

Diffusion of innovations have been shown to normally follow S-shaped cumulative distribution curves, with a very slow uptake followed by rapid spread followed by a slowing down as the innovation nears ubiquity.[8] Joseph Henrich has shown that these curves, which are drawn from real-life data, fit models where innovations are adopted based on their intrinsic attributes (as opposed to models in which individuals proceed by trial-&-error, for example).[9] In other words, in the real world, it seems, innovations spread in the main because people choose to adopt them based on their qualities. And which qualities are those? Everett Rogers, an innovation theorist who coined the term “early adopter”, identified five essential ones: an innovation must (1) have a relative advantage over previous ideas; (2) be compatible such that it can be used within existing systems; (3) be simple such that it is easy to understand & use; (4) be testable such that it can be experimented with; & (5) be observable such that its advantage is visible to others.[10]

[...]

The rate of cultural innovation generally is correlated with population size.[13] That makes sense: a country of a million will naturally produce more innovations than a country of one. Simulations indicate that innovation produces far more value in large population groups.[14] [...]

But there is also another quality that greatly affects the population-level rate of innovation. That quality is not necessity, which the adage calls the mother of invention; companies cut R&D costs when times are tough, not the other way around.[15] Neither is it a handful of geniuses making earth-shattering individual contributions.[16] No, what greatly affects a population’s rate of innovation is its interconnectedness, in other words how widely ideas, information & tools are shared.[17] In a culture that is deeply interconnected, where information is widely shared, innovations are observable & shared tools & standards mean that innovations are also more likely to be compatible. Most importantly, interconnectedness provides each individual with a large pool of ideas from which they can select the most attractive to modify, recombine, extend & spread in turn.

Here are some EA Forum things that might be relevant:

And here are some non-EA-Forum things that might be relevant, which I learned about from Max Daniel (I think) and which I haven't read myself:

(Also, glad to hear you found the differential progress tag and my related collection of sources useful!)

The roots of progress blog by Jason Crawford might be worth a look. It often discusses topics like technological stagnation or how quick technologies grow

https://en.wikipedia.org/wiki/Technological_transitions might be relevant.

The Geels book cited in the article (Geels, F.W., 2005. Technological transitions and system innovations. Cheltenham: Edward Elgar Publishing.) has a bunch of interesting case studies I read a while ago and a (I think popular) framework for technological change, but I am not sure the framework is sufficiently precise to be very predictive (and thus empirically validatable). 

I don't have any particular sources on this, but the economic literature on the effects of regulation might be quite relevant. In particular, I do remember attending a lecture arguing that limited liability played an important role for innovation during the industrial revolution.

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I would guess that there is a lot of relevant work in economics. In particular work that asks what kind of incentives (e.g. in terms of patents, intellectual property regulation, etc.) a social planner would need to set to achieve a "socially optimal" level of R&D expenses by private actors. This isn't exactly what you'd want to know, but I expect that sometimes it would be able to extract possible "levers" that could apply at the level of specific sectors and that could also be pulled on by actors other than government.

I recommend asking someone with an econ background, perhaps at GPI.