I’ve started reading Piketty’s Capital. His introduction begins with an overview of the history of economic theory, starting with Ricardo and Marx.
Both these early theorists predicted the concentration of wealth into the hands of the owners of factors of production that are not labor. For Ricardo, land owners extract rents and dominate the economy. For Marx, capitalists–owners of private capital–accumulate capital and dominate the economy.
Since those of us with an eye on the tech sector are aware of a concentration of wealth in the hands of the owners of intellectual property, it’s a good question what kind of economic theory ought to apply to those cases.
One one sense, intellectual property is a kind of capital. It is a factor of production that is made through human labor.
On the other hand, we talk about ideas being ‘discovered’ like land is discovered, and we imagine that intellectual property can in principle be ‘shared’ like a ‘commons’. If we see intellectual property as a position in a space of ideas, it is not hard to think of it like land.
Like land, a piece of intellectual property is unique and gains in value due to further improvements–applications or innovations–built upon it. In a world where intellectual property ownership never expires and isn’t shared, you can imagine that whoever hold some critical early work in some field could extract rents for perpetuity. Owning a patent would be like owning a land estate.
Like capital, intellectual property is produced by workers and often owned by those investing in the workers with pre-existing capital. The produced capital is then owned by the initiating capitalist, and accumulates.
Open source software is an important exception to this pattern. This kind of intellectual property is unalienated from those that produce it.
I’ve been depressed lately about the oligopolistic control of telecommunications for a while now. There’s the Web We’ve Lost; there’s Snowden leaks; there’s the end of net neutrality. I’ll admit a lot of my moodiness about this has been just that–moodiness. But it was moodiness tied to a particular narrative.
In this narrative, power is transmitted via flows of information. Media is, if not determinative of public opinion, determinative of how that opinion is acted up. Surveillance is also an information flow. Broadly, mid-20th century telecommunications enabled mass culture due to the uniformity of media. The Internet’s protocols allowed it to support a different kind of culture–a more participatory one. But monetization and consolidation of the infrastructure has resulted in a society that’s fragmented but more tightly controlled.
There is still hope of counteracting that trend at the software/application layer, which is part of the reason why I’m doing research on open source software production. One of my colleagues, Nick Doty, studies the governance of Internet Standards, which is another piece of the puzzle.
But if the networking infrastructure itself is centrally controlled, then all bets are off. Democracy, in the sense of decentralized power with checks and balances, would be undermined.
Yesterday I learned something new from Ashwin Mathew, another colleague who studies Internet governance at the level of network administration. The man is deep in the process of finishing up his dissertation, but he looked up from his laptop for long enough to tell me that the network backbone market is in fact highly competitive at the moment. Apparently, there was a lot of dark fiberoptic cable (“dark fiber“–meaning, no light’s going through it) laid during the first dot-com boom, which has been laying fallow and getting bought up by many different companies. Since there are many routes from A to B and excess capacity, this market is highly competitive.
Phew! So why the perception of oligopolistic control of networks? Because the consumer-facing telecom end-points ARE an oligopoly. Here there’s the last-mile problem. When wire has to be laid to every house, the economies of scale are such that it’s hard to have competitive markets. Enter Comcast etc.
I can rest easier now, because I think that this means there’s various engineering solutions to this (like AirJaldi networks? though I think those still aren’t last mile…; mesh networks?) as well as political solutions (like a local government running its last mile network as a public utility).
I’m studying software development and not social media for my dissertation.
That’s a bit of a false dichotomy. Much software development happens through social media.
Which is really the point–that software development is a computer mediated social process.
What’s neat is that it’s a computer mediated social process that, at its best, creates the conditions for it to continue as a social process. c.f. Kelty’s “recursive public”
What’s also neat is that this is a significant kind of labor that is not easy to think about given the tools of neoclassical economics or anything else really.
In particular I’m focusing on the development of scientific software, i.e. software that’s made and used to improve our scientific understanding of the natural world and each other.
The data I’m looking at is communications data between developers and their users. I’m including the code, under version control, as this. In addition to being communication between developers, you might think of source code as a communication between developers and machines. The process of writing code as a collaboration or conversation between people and machines.
There is a lot of this data so I get to use computational techniques to examine it. “Data science,” if you like.
But it’s also legible, readable data with readily accessible human narrative behind it. As I debug my code, I am reading the messages sent ten years ago on a mailing list. Characters begin to emerge serendipitously because their email signatures break my archive parser. I find myself Googling them. “Who is that person?”
One email I found while debugging stood out because it was written, evidently, by a woman. Given the current press on diversity in tech, I thought it was an interesting example from 2001:
From sag at hydrosphere.com Thu Nov 29 15:21:04 2001
From: sag at hydrosphere.com (Sue Giller)
Date: Thu Nov 29 15:21:04 2001
Subject: [Numpy-discussion] Re: Using Reduce with Multi-dimensional Masked array
In-Reply-To: <000201c17917$ac5efec0$3d01a8c0@plstn1.sfba.home.com>
References: <20011129174809062.AAA210@mail.climatedata.com@SUEW2000>
Message-ID: <20011129232011546.AAA269@mail.climatedata.com@SUEW2000>Paul,
Well, you’re right. I did misunderstand your reply, as well as what
the various functions were supposed to do. I was mis-using the
sum, minimum, maximum as tho they were MA..reduce, and
my test case didn’t point out the difference. I should always have
been doing the .reduce version.I apologize for this!
I found a section on page 45 of the Numerical Python text (PDF
form, July 13, 2001) that defines sum as
‘The sum function is a synonym for the reduce method of the add
ufunc. It returns the sum of all the elements in the sequence given
along the specified axis (first axis by default).’This is where I would expect to see a caveat about it not retaining
any mask-edness.I was misussing the MA.minimum and MA.maximum as tho they
were .reduce version. My bad.The MA.average does produce a masked array, but it has changed
the ‘missing value’ to fill_value=[ 1.00000002e+020,]). I do find this
a bit odd, since the other reductions didn’t change the fill value.Anyway, I can now get the stats I want in a format I want, and I
understand better the various functions for array/masked array.Thanks for the comments/input.
sue
I am trying to approach this project as a quantitative scientist. But the process of developing the software for analysis is putting me in conversation not just with the laptop I run the software on, but also the data. The data is a quantified representation–I count the number of lines, even the number of characters in a line as I construct the regular expression needed to parse the headers properly–but it represents a conversation in the past. As I write the software, I consult documentation written through a process not unlike the one I am examining, as well as Stack Overflow posts written by others who have tried to perform similar tasks. And now I am writing a blog post about this work. I will tweet a link of this out to my followers; I know some people from the Scientific Python community that I am studying follow me on Twitter. Will one of them catch wind of this post? What will they think of it?