
Funding Models and Progress - jasoncrawford
https://rootsofprogress.org/funding-models-and-progress
======
linguae
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.

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
[http://worrydream.com/2017-12-30-alan/](http://worrydream.com/2017-12-30-alan/).

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.

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.

~~~
sytelus
Our biggest problem is that research is _expected_ to be in Academia. Not
withstanding few exceptions such as DeepMind and FAIR, vast majority of
industrial research is by definition short term. For example, the founding
document of Microsoft Research asserted that they only want to hire
researchers who are willing to work on relevant problems with 2 year horizon
with emphasis on product integrations. Similarly, even at Bell Labs research
was expected to help phone lines (a fun story being C/Unix were underground
projects, borderline "unapproved").

All these while, investment in science and research have had returns that
blows up any other investments you can possibly imagine. So why are companies
so relunctant at even putting in 1% of their revenues in research? Why we as
nations put less than 1% of GDP in research? Why true exploratory research is
only expected to happen in academia?

These are obvious and massive gaps in our economic story and progress. But
anytime you have gaps, you have opportunity! One of my favorite funding models
to play around is a slight variation of Y-Combinator model. Instead of giving
$120K to teens who can whip out websites that you can later offload to
trillion dollar stupid gorillas in 5 years, imagine you do this: You hire
researchers who are passionate and give them $0.5M grant to have 3 year
runway. They need not build companies, do pitches or generate revenues. They
just do their own curiocity driven exploration, they set their own agenda.
Bunch of these folks even perhaps work in same buildings so they can
collaborate and may be spend some money to hire students/post-docs/engineers.
After 3 years, VC looks at their ideas, prototypes, demos and funds a teen
with massive enterprenuerial spirit to build products off of those ideas. In
other words, we create _pool_ of innovations and _pool_ of people who can
build companies. We decouple short term determinisitc execution from long term
randomized innovations. It's like having brain with parts that think fast and
slow.

How will this work out? I would expect 99% of funded researchers producing
nothing useful in terms of business. From the rest 1%, possibly 9 out of 10
are minor things and may be 1 is significant breakthrough leading to $10B
company. But more importantly, there is 1 in 10,000 chance that breakthrough
leads to something of the order of $100B. This would result in expected return
of 20X. More importantly, these companies will be _cumulative_ , so your
expected return _grows_ over time with same continued investment! If done
right, I think this version of YC could become first $10 trillion company
because it will be sort of a meta-company whoes product is long lasting
companies built on big breakthroughts.

There are obviously several issues in this idea that I haven't worked out yet:
(1) why VC should fund academia like labs when government is already doing it?
(2) researchers want to open up their ideas so what moat do you really have?
(3) how does collaborations happen? (4) why any enterprenuer wants to execute
on someone else's ideas? etc.

PS: Loonshots is a must read anyone interested in this area.

~~~
why_only_15
The US puts 2.79% of its GDP towards research. It's arguable this should be
increased, but ~$550b is an impressive figure in any case. It would be
interesting to see what exactly is included in this figure and how much of
that is 'basic' etc.

~~~
sytelus
Exactly. A very little of it is basic and vast majority of it simply usual
development projects that you have to do anyway. This is similer to big tech
who oftne claims massive expenditure in "R&D" but R part typically gets 1% and
D part gets 99%. For reference NSF is less than $8B, a chump change for US GDP
of $18T.

------
mootzville
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.

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?

There is a good set of papers that describes a framework for performing such
an analysis.

M. Weitzman et al., 1992 M. Weitzman et al., 1998 H. Simianer et al., 2003

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.

You can check it out here: [https://mostdiverse.com](https://mostdiverse.com)

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.S. We are trying to get funding too! Ha!

~~~
btrettel
What are the complete citations for those papers?

In the decision analysis class I took 3 years ago they proposed the framework
in the following paper:

Matheson, James E., Michael M. Menke, and Stephen L. Derby. "Managing R&D
portfolios for improved profitability and productivity." The Journal of
Science Policy and Research Management 4, no. 4 (1989): 400-412.

[https://scholar.google.com/scholar?cluster=62055342011699436...](https://scholar.google.com/scholar?cluster=6205534201169943688&hl=en&as_sdt=5,33&sciodt=0,33)

~~~
mootzville
Nice, thanks...now I have some bedtime reading.

The papers are:

1\. On Diversity (1992) - M. Weitzman 2\. The Noah's Ark Problem (1998) - M.
Weitzman 3\. An approach to the optimal allocation of conservation funds
tominimize loss of genetic diversity between livestock breeds (2003) - H.
Simianer

~~~
btrettel
Here are the links to the articles moozville mentioned:

[https://academic.oup.com/qje/article-
abstract/107/2/363/1838...](https://academic.oup.com/qje/article-
abstract/107/2/363/1838289)

[https://www.jstor.org/stable/2999617](https://www.jstor.org/stable/2999617)

[https://www.sciencedirect.com/science/article/abs/pii/S09218...](https://www.sciencedirect.com/science/article/abs/pii/S0921800903000922)

------
Twisell
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.

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"...

Project is still in a corner of my mind but in standby (also for personals
reasons beyond funding).

So yeah I can pretty much relate!

------
RobertoG
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.

So, I would like to know: who funded Alexander Fleming work? who funded the
Oxford lab that followed the work? Why they were underfunded?

------
smitty1e
> 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.

This is among the reasons why, for all I love free software, it tends not to
be a driver of innovation.

~~~
zozbot234
Free software is actually a big driver of _cheap_ innovation, precisely
_because_ it's so funding-constrained in the first place. It's one of those
places where you would expect more funding to help quite a bit.

