As a computer science researcher working toward a PhD, I've been very interested in learning about the history of funding and also thinking about alternative funding models.<p>Speaking from the standpoint of computer science, I believe the golden age of scientific funding was between WWII through the early 1990s. Bell Labs and Xerox PARC invented a lot of the technologies that we rely on today, and there is still an untapped reservoir of research that has yet to be fully applied (I still believe that Smalltalk80 is quite futuristic in many ways despite its being 40 years old). DARPA and its predecessor ARPA was key for funding a lot of fundamental computer science work. Back in the 1960s ARPA had a funding model that emphasized "funding people, not projects." Alan Kay discusses this more at <a href="http://worrydream.com/2017-12-30-alan/" rel="nofollow">http://worrydream.com/2017-12-30-alan/</a>.<p>Unfortunately, since the 1990s (and possibly before then), both industry and government have shifted to the model of "funding projects, not people," and there's a greater emphasis on delivering short-term results instead of funding medium- and long-term research efforts. There is also the PhD production problem, where more PhDs are being produced than there are research jobs available for them, whether they are in academia, government, or industry. This results in plenty of computer science PhDs working as software engineers in non-research environments. For a few years I've been considering becoming a professor at a teaching-oriented university; computer science professors have been in high demand at teaching universities for the past few years, and there are fewer "publish-or-perish" and funding pressures for teaching-oriented professors than for professors at research universities. But now with the COVID-19 pandemic and the sudden shift to online education, I am concerned that this shift to online education, as well as the economic fallout of COVID-19, may dry up opportunities at teaching universities.<p>Because I still want to pursue a career in research even if traditional opportunities dry up, I am interested in alternative funding models that will ensure that researchers could still make a living in a world that demands instant results.
Funding is always limited, so if you are going to allocate funds with limited resources you might base it on a model nature provides: Diversity.<p>While there is no guaranteed way of choosing the right research to fund, an allocation of those funds among a set of common projects with similarities and dissimilarities would yield the most progress -- or at least, the highest probability of achieving progress. But how do you go about calculating the allocation?<p>There is a good set of papers that describes a framework for performing such an analysis.<p>M. Weitzman et al., 1992
M. Weitzman et al., 1998
H. Simianer et al., 2003<p>We built an implementation of the framework described in those papers, and though we are targeting the agricultural sector, we'd be curious to see it applied for other purposes such as measuring research progress, and allocating funds for it.<p>You can check it out here: <a href="https://mostdiverse.com" rel="nofollow">https://mostdiverse.com</a><p>Reach out if you want to run some of your own data, we are happy to see this applied to areas outside our expertise.<p>P.S. We are trying to get funding too! Ha!
After 10 years working as data-analyst I felt the urge to do PHD to improve something I was always struggling with in operational projects. Was backed up by a potential mentor a the uni. Was partly backed up by my Boss.<p>Most logical funding available for my case was denied by the authority "because that would have set a precedend... Look your idea is nice, useful and all, but we never actually activated this (fully defined by law) funding and we fear other might come and ask for it if we do it for you"...<p>Project is still in a corner of my mind but in standby (also for personals reasons beyond funding).<p>So yeah I can pretty much relate!
I find the article strangely void of content. If we are investigating funding of discoveries to find a formula to repeat, it seems to me the way to go is, to find the most relevant discoveries and see who funded them.<p>So, I would like to know: who funded Alexander Fleming work? who funded the Oxford lab that followed the work? Why they were underfunded?
> Progress doesn’t happen automatically when the scientific or technical prerequisites for it are in place. It only happens when people work on it, and that almost always requires funding.<p>This is among the reasons why, for all I love free software, it tends not to be a driver of innovation.