[Discuss] Economic value of OSH work
Javier.Serrano at cern.ch
Mon Sep 8 12:37:58 UTC 2014
On 09/06/2014 05:51 PM, Joshua Pearce wrote:
> Thank you to everyone with your good suggestions, references and thoughts.
> I have a much better understanding now and you brought up some
> interesting points (which I will attribute) I had not considered and
> now will include in the paper in order to make an economic case for
> government funding of FOSH development.
> The paper in the works - I will share with you when I have finished
> running all the numbers and it is ready.
Joshua, I think this is very important work and look forward to reading
the paper when it's published. Going quantitative is indeed very hard
but decision makers often face situations where they have to decide
on subjects they don't fully master. Quantification is perceived to add
objectivity, and I think funding agencies will be happy to get some
numbers and an explanation, at least as a basis for discussion.
I may be misguided, but I think one of the factors which may have pushed
many governments to ask their publicly-financed universities to patent
as many of their inventions as possible is that the patent world is easy
to quantify. So if you buy the argument that more patents means more
innovation and ultimately more welfare for your citizens, which some
governments seem to believe, then it's easy to assess your success by
counting patents and royalties from those patents. And there might be a
temptation to be a bit sloppy with logic and promote easy
quantifiability from an important feature that will allow evaluation of
the success of a given course of action -- which has independently been
deemed appropriate -- to an implicit reason supporting the
appropriateness of that course of action itself.
I don't have a clue about how to go quantitative with FOSH. At CERN, we
know most of the people and companies who manufacture, distribute and
support the hardware we design and publish, but the process of
evaluating economic impact by regularly polling them seems cumbersome,
error-prone and non-scalable. Maybe there are things to be learned from
Google and other such companies, who succeed in convincing their clients
to pay them because they are able to give a reasonably accurate
estimation of the impact their services have on the sales of their
clients. My understanding is that they do this through a combination of
technology, societal models which are continuously validated and very
powerful statistics artillery.
Many thanks for your work. Cheers,
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