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<nettime> First-World Myopia: The invisible context of computing

  First-World Myopia:
  The Invisible Context of Computing

  Philip E. Agre
  Department of Information Studies
  University of California, Los Angeles
  Los Angeles, California  90095-1520

  This is a draft.  Please do not quote from it.
  Version of 16 October 1999.
  1100 words.

Information is everywhere and nowhere, immaterial and abstract, cleanly
separable from the concrete world of cows and cars.  That is the common
sense we learn in school.  But the common sense cannot be quite right, or
else we would not need the Internet to move all of this information
around.  The Internet is strange in this way, an ontological hybrid, with
one foot in the world of atoms and the other foot in the world of bits. 
Metaphysics since Plato and political science since the Enlightenment make
it easy to imagine the world of universal information that the Internet
promises to bring -- so easy, in fact, that we can underestimate the work
that will be required to bring this world about. 

Here is the problem.  We all know that computers are complex beasts.  But
for all of their internal complexity, computers are just as complicated in
their embedding in the outside world.  Yet the complexity of this
embedding is largely invisible to the people who design computers, and to
the people who make a living promoting their use.  Call it first-world
myopia: taking for granted the sprawling background of infrastructure,
institutions, and information that make modern societies possible. 

It may seem implausible that infrastructure, institutions, and information
could be invisible, since in public discourse these days we hardly seem to
discuss anything else.  But it happens all the time, and here is why. 
Modern society exhibits a tremendous division of labor, and a division of
labor is only possible if every occupational community can focus on its
own speciality, letting everything else fade into the background.  In a
society with a thousand and one occupations, everyone will be socialized
into an occupational discourse and practice that simply presupposes the
products of a thousand others.  This is efficient, but it is also
dangerous.  The products of industrial society often do not travel well. 
Computers, for example, require electricity and an overnight delivery
system for spare parts -- both complex infrastructures that are not always
available.  These are familiar examples; let us consider three examples
that are less familiar. 

Thing first about data.  What is data?  In fact computer scientists have
thought about data in several ways (Agre 1997).  In the early days, they
spoke of computation as data processing, the idea being that data was an
industrial material like iron ore.  But this metaphor did not do justice
to the representational nature of data -- the fact that data makes claims
about something in the world.  So techniques like data modeling arose to
make clearer what sorts of entities in the world the machine was supposed
to represent.  But that hardly exhausts the topic.  Once a computer is
filled with well-defined numbers, consider what it means to add those
numbers (or subtract them, or compare them).  Those numbers originated
somewhere, for example a 3 from Europe and a 4 from Asia.  The sum of
those numbers, 7, is only meaningful if the 3 and the 4 are commensurable,
and that requires that they be measured in the same way.  A working
computer thus requires more than functioning circuits.  It also requires a
far-flung institutional arrangement that provides for the standardized
capture of data.  Bowker (1994) refers to this kind of realization as an
infrastructural inversion, and it is a good example of the first-world
myopia that takes such things for granted. 

Think next about the settings in which computers are used.  It is
extraordinarily common for organizations to invest large sums in complex
computers without any investment in training.  Schools often invest their
scarce resources in computers without any thought to the curriculum.  In
some cases the responsible authorities are duped by claims that the
systems are easy to use.  In other cases it is assumed that computers will
pay for themselves by displacing staff, and further investments in human
capital seem like the opposite of that intention.  In each case, what is
neglected is what Kling (1992) calls the web of relationships around the
computer.  Computers are easy to see, but webs are not. 

This effect is especially pronounced with the Internet.  The Internet's
design was motivated in large part by "end-to-end arguments" (Saltzer,
Reed, and Clark 1984), according to which it is more efficient to move
complex networking functions from the network itself to the computers that
use it.  This made perfect sense in places like MIT and UCLA, where the
researchers could depend on a web of skilled people and advanced technical
resources.  It makes less sense in the real world, and organizations that
adopt the Internet are often unprepared for the cost and managerial
overhead of hiring a system administrator to maintain it.  In the
telephone system, most of those administration skills are internalized by
the telephone company (Odlyzko 1998).  The Internet's distributed
architecture also distributes the social and technical complexity.  An
analogy can be found in Latour's (1988: 90 [?])  account of the spread of
Pasteurization in France.  Pasteur's process worked in his laboratory, and
in order for the process to spread around the country, the relevant
aspects of the laboratory had to be spread around as well.  The
consequences for Internet services are considerable.  For example, most
first-worlders are accustomed to the decades of cross-subsidies that
brought basic telephone service to rural areas.  They take this
universality for granted, and they too readily imagine the rural utopias
that universal Internet service will bring. 

Think, finally, about neoclassical economics, which continues to dominate
first-world economics departments despite its programmatic neglect of
institutions and information (e.g., Hodgson 1988).  Such a theory is only
plausible if a tremendous framework of both can be taken for granted.  Of
course, some neoclassically oriented economists have spent the 1990s
relaxing these extreme assumptions, so that institutions and information
have started to become visible in mainstream discourse (e.g., Stiglitz
1985).  Nonetheless, the neoclassical idealizations of perfectly
information and perfectly functioning economic institutions are deeply
ingrained in a vast economic rhetoric, and this rhetoric is still
routinely applied in contexts where the idealizations do not hold. 
Economic playing fields are thus made to seem much more level than they
really are. 

These few examples hardly exhaust the depths of first-world myopia.  But
they do get us started on an important project: understanding how
technologies and ideas can be perfectly valid in one context and
disastrously wrong-headed in another.  Once we acquire this new,
clearer-sighted variety of common sense, we will become less susceptible
to what Leigh Star and Gail Hornstein (1990) call "universality biases":
the uncritical assumption that discoveries in one context will necessarily
apply in another.  Instead, we will take the transfer of technology and
ideas as an opportunity to make visible the taken-for-granted background
of technical and economic work.  Bad advice will be replaced by dialogue,
and we will all be better for it. 


Philip E. Agre, Beyond the mirror world: Privacy and the representational
practices of computing, in Philip E. Agre and Marc Rotenberg, eds,
Technology and Privacy: The New Landscape, Cambridge:  MIT Press, 1997. 

Geoffrey Bowker, Information mythology: The world of/as information, in
Lisa Bud-Frierman, ed, Information Acumen: The Understanding and Use of
Knowledge in Modern Business, London: Routledge, 1994. 

Geoffrey M. Hodgson, Economics and Institutions: A Manifesto for a Modern
Institutional Economics, Cambridge, UK: Polity Press, 1988. 

Rob Kling, Behind the terminal: The critical role of computing
infrastructure in effective information systems' development and use, in
William Cotterman and James Senn, eds, Challenges and Strategies for
Research in Systems Development, London: Wiley, 1992. 

Bruno Latour, The Pasteurization of France, translated by Alan Sheridan
and John Law, Cambridge: Harvard University Press, 1988. 

Jerome W. Saltzer, David P. Reed, and David D. Clark, End-to-end arguments
in system design, ACM Transactions in Computer Systems 2(4), 1984, pages

Gail A. Hornstein and Susan Leigh Star, Universality biases: How theories
about human nature succeed, Philosophy of the Social Sciences 20(4), 1990,
pages 421-436. 

Joseph E. Stiglitz, Information and economic analysis: A perspective,
Economic Journal 95(supplement), 1985, pages 21-41. 

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