[Infrastructures] sysadmin: "problem" or "network" field? [mostly from Agre/RRE]

[Will Partain]

Folks, a little weekend reading for the bath, below... By
Phil Agre, from his Red Rock Eater (RRE) mailing list.

The article may throw light on the question, "what kind of
field (of knowledge) is `system administration'"?  In
particular, is sysadmin a "problem" field, or a "network"
field (terms defined below)?

Conjecture: it's a "network" field.  This has biggish
implications, e.g.: (a) most of our can't-get-no-respect
difficulties follow from living/working cheek-by-jowl with a
"problem" field, namely computer science; (b) efforts to
have us grow up into a proper ("problem") research field
are, um, doomed; (d) we're more like library science than
computer science; (d) we probably need some "network"-y way
of structuring our body of knowledge; and (e) probably lots
more :-)

Comments, etc., welcome.

Will

=====================================
Date: Sun, 27 Jan 2002 22:54:17 -0800
Message-Id: <200201280654.WAA68962@alpha.oac.ucla.edu>
From: Phil Agre <pagre@alpha.oac.ucla.edu>
To: "Red Rock Eater News Service" <rre@lists.gseis.ucla.edu>
Subject: [RRE]notes and recommendations

Some notes about distributed objects, technology-driven change, and
the diversity of knowledge.

[snips -will]

**

Networks and problems.

Different fields produce different kinds of knowledge.  The idea of
a diversity of knowledge, however, intimidates many people; it sounds
to them like relativism, as if *anything* can count as knowledge
if someone simply says so.  That's silly; no such thing follows.
Even so, it *is* a hard problem to understand how knowledge functions
in society if knowledge is diverse, for example how to tell the
difference between quality-control and censorship.  The scholars who
have argued for the diversity of knowledge, despite the quality of
their research, have often been unconcerned with the public-relations
problem that their insights suffer.  They can win the argument
about relativism when they are arguing with people equally as erudite
as themselves, but they have historically not done a good job of
translating the arguments into a rhetoric that wins public debates.
That's partly because they are so concerned to defeat the mythology
of unitary knowledge that they emphasize heterogeneity more than
they emphasize the limits to heterogeneity.  That's too bad, because
the diversity of knowledge actually turns out to be related to the
Internet's place in society.

Let me suggest an intuitive way to think about the differences between
different kinds of knowledge.  To simplify, I'll stick with academic
fields.  Every academic field, I will suggest, has two dimensions:
problem and network.  By the "problem" dimension of knowledge I
mean the ways in which research topics are framed as discrete and
separable, so that researchers -- whether individuals or teams --
can dig into them and produce publishable results without enaging
in far-flung collaborations.  By the "network" dimension of knowledge
I mean the ways in which researchers organize themselves across
geographical and organizational boundaries to integrate experience
from many different sites.  Every field has its own complexity in
both of these dimensions, but often the emphasis is on one dimension
or another.  As a result, we can roughly and provisionally categorize
academic fields as "problem" fields and "network" fields.

The prototype of a "problem" field is mathematics.  Think of Andrew
Wiles, who disappeared into his study for several years to prove
Fermat's Last Theorem.  The hallmark of "problem" fields is that
a research topic has a great deal of internal depth and complexity.
The math in Wiles' proof may seem like vast overkill for something
so simple as the statement of Fermat's Last Theorem, but you can think
of it as an engineering project that finished building a bridge over
a conceptual canyon.  Publicity value aside, the mathematicians value
the bridge because they hope that it's going to carry heavier traffic
in the future.  Even so, it's not clear that Wiles' type of math
represents the future.  Math papers are more likely to be coauthored
than in the old days, as mathematicians work increasingly by bringing
different skills together.  This is partly a legacy of the major math
project of the 20th century, which aimed at the grand unification
of fields rather than producing heavier theorems in a single area.
That unification project opened up many seams of potential results
along the edges between different areas of math.  The increasing
practical applicability of even very abstruse areas of math (e.g.,
in cryptography) didn't hurt either.

Even so, math is still weighted toward the "problem" dimension.  Math
people do form professional networks like anyone else, but the purpose
of these networks is not so much to produce the knowledge as to ensure
a market for it.  The same thing is true in computer science, where
professional networks also help with funding.  And those are not
the only problem fields.  Cultural anthropology is a good example.
The anthropologist goes to a distant island, spends two years learning
the culture, and writes a book that uses it as raw material to explore
a particular theoretical problem in depth.  The "problem" nature
of cultural anthropology is partially an artefact of technology;
if long-distance communication is hard then it's easier to uphold
the myth that humanity comes sorted into discrete cultures, and a
fieldworker who travels great distances to study a culture has no
choice but to define a large, solitary research project.  But that
doesn't change the fact that the best anthropology (and there's a
lot of good anthropology being written) has intellectual depth to
rival anything being done in computer science, even if the conceptual
and methodological foundations of the research could hardly be more
different.

Contrast these fields to some others: medicine, business, and library
science.  Medicine, business, and library science may not seem similar
on the surface, but they have something important in common: they are
all network-oriented.  Because they study something that is complex
and diverse (illnesses, businesses, and information), they build their
knowledge largely by comparing and contrasting cases that arise in
professional practice.  Physicians don't make their careers by solving
deep problems or having profound ideas; they make their careers by
building networks that allow them to gather in one central location
the phenomenology of a syndrome that has not yet been systematically
described.  Medical knowledge is all about experience-based patterns.
It says, we've seen several hundred people with this problem, we've
tried such-and-such treatments on them, and this is what happens.
Business is the same way: we've investigated such-and-such an issue
in the context of several businesses, and this is the pattern we've
discerned.  Library science, likewise, is concerned to bring order
to the diversity of information as it turns up in the collections of
library institutions worldwide.

When mathematicians look at business or computer scientists look at
library science, they often scoff.  They have been taught to value
"problems", and they are looking for the particular kind of "depth"
that signifies "good work", "real results", and so on.  When they
don't find what they are looking for, they often become disdainful.
The problem is that they are looking in the wrong place.  The don't
realize that the "problems" that they are familiar with are largely
artificial constructions.  To fashion those kinds of problems, you
need to take several steps back from reality.  You're abstracting
and simplifying, or more accurately someone else is abstracting and
simplifying for you.  Many job categories are devoted to suppressing
the messy details that threaten to falsify the abstractions of
computer science, starting with the clerks whose computer terminals
demand that they classify things that refuse to be classified.
The dividing-line between computer science and the business-school
discipline of "MIS" is especially interesting from this point of view,
since the MIS managers are much closer to the intrinsic complexity
and diversity of day-to-day business.  Computer scientists, as a broad
generalization, have little feeling for the complexity and diversity
of the real world.  That's not to say that they are bad people or
defective intellects, only that the field of computer science frames
its knowledge in certain ways.  It takes all kinds to make a world,
and that goes for knowledge as well.  We should encourage the creative
tension between problem field and network fields, rather than arguing
over who is best.

Medicine is an interesting case for another reason.  Even though
problem fields are higher-status than network fields as a broad
generalization, medicine is an exception to the rule.  If my theory
is right, then, why doesn't medicine fall into the same undeservedly
low-status bin as business and library science?  The reasons are
obvious enough.  Medicine is a business unto itself -- at UCLA it's
half the university's budget -- and it brings money in through patient
fees, insurance reimbursements, and Medicare, as well as through
research grants and student tuition.  Money brings respect, all
things being equal, although the increasingly problematic finances
of teaching hospitals will test this dynamic in the near future.
Medicine is also very aggressive in the way it wields symbols --
it's hard to beat life and death for symbolic value.  What's more,
business and library schools have stronger competitors than medical
schools, so they have a greater incentive to speak in plain English.
Precisely because they rely so heavily on symbols, medical schools
have never had to explain how their knowledge works in ways that
normal people can understand.

Professional schools in general tend to produce knowledge that is
more network-like than problem-like, but historically they have very
often responded to the disdain of the more problem-oriented fields
by trying to become more problem-oriented themselves.  This strategy
is very old; in fact Merton described it perhaps fifty years ago.
Unfortunately, it doesn't always work.  You end up with professional
schools whose faculties are trained in research methods that are
disconnected from the needs of their students, or else you end up
with factionalized schools that are divided between the scientists
and the fieldworkers, or with people whose skills lie in network
methods trying to solve problems because that's what the university
wants.  I think this is all very unfortunate.  I'm not saying that
every field should be homogenous, and even if everyone does the
research they ought to be doing we'll still have the problem of how
scholars with incommensurable outlooks can get along.  Still, the
asymmetry of respect between network knowledge and problem knowledge
is most unfortunate.

I think the world would be better off if network knowledge were just
as venerated as problem knowledge.  Before this can happen, we need
better metaphors.  We are full of metaphors for talking about the
wonders if problem knowledge, as we ought to be.  When Andrew Wiles
can go off in his room and prove Fermat's Last Theorem, that's a good
thing, and there's nothing wrong with using the metaphor of "depth"
to describe it.  It's just that we need metaphors on the other side.

So here's a metaphor.  I propose that we view the university as the
beating heart of the knowledge society.  The heart, as we all know,
pulls in blue blood from all over the body, sends it over to the lungs
until it's nice and red with oxygen, and then pumps it back out into
the body.  The university does something similar, and the predominant
working method of business schools can serve as a good way to explain
it.  If you read business journals, especially journals such as
the Harvard Business Review that are largely aimed at a practitioner
audience, you will often see two-by-two matrices with words written in
them.  These sorts of simple conceptual frameworks (which I've talked
about before) are a form of knowledge, but it's not widely understood
what form of knowledge they are.  Once we understand it, we'll be able
to see how the university is like a heart.

So let's observe that there are at least two purposes that knowledge
can serve: call them abstraction and mediation.  Abstraction is the
type of knowledge that the West has always venerated from Plato's
day forward.  It is something that rises above concrete particulars;
in fact, it carries the implicit suggestion that concrete particulars
are contaminants -- "accidents" is the medieval word -- compared to
the fixed, permanent, perfect, essentially mathematical nature of the
abstractions.  Abstractions generalize; they extract the essence from
things.  They are an end in themselves.  In Plato's theory we were all
born literally knowing all possible knowledge already, since access
to the ideals (as he called them) was innate.  That made questions of
epistemology (i.e., the study of the conditions of knowledge) not so
urgent as they became subsequently, as the West began to recognize the
absurdity of a conception of knowledge that is so completely detached
from the material world.

But if knowledge can abstract, it can also mediate.  The purpose
of the two-by-two matrices in the business journals is not to embody
any great depth in themselves, the way a theorem or an ethnnography
might.  Instead, their purpose is to facilitate the creation of new
knowledge in situ.  Choose a simple conceptual framework (transaction
costs, core competencies, structural holes, portfolio effects), and
take it out into real cases -- two or more, preferably more.  Study
what each conceptual framework "picks out" in each case; that is, use
the conceptual framework to ask questions, and keep asking questions
until you can construct a story that makes sense within the logic of
that particular case.  That's important: each case has its details,
and each case is filled with smart people who have a great deal of
practical knowledge of how to make a particular enterprise more or
less work.  So work up a story that makes sense to them, that fits
with their understandings, yet that is framed in terms of the concepts
you've brought in.  Of course, that might not be possible; your new
concepts may not pick out anything real in a particular case, in which
you need to get new concepts.  But once you've found concepts that
let you make sense of several cases, now you can compare and contrast.

And that's where the real learning happens.  Even with the concepts
held constant, each case will tend to foreground some issues while
leaving others in the background.  Take the issues that are foreground
in case A, and translate those issues over to cases B, C, D, and E,
asking for each of them what's going on that might correspond to the
issue from case A.  It doesn't matter whether the other cases are all
directly analogous to case A; even if the issue sorts out differently
in those other cases, the simple fact that you've thought to ask the
question will provoke new thoughts that may never have occurred to
anybody before.  That's what I mean by the mediating role of knowledge:
it mediates the transfer of ideas back and forth between situations
in the real world that might not seem at all comparable on the surface.

And that's the beating heart: what the university does is fashion
concepts that allow ideas to be transferred from one setting to
another.  Each setting has its own language, so the university
invents a lingua franca that gets conversation started among them.
At first the ideas will pass through the doors of the university.
A researcher will go out to several different sites, gather ideas,
bring them home, think about them, and then scatter them in other
sites.  Eventually the concepts themselves will be exported, so that
students who graduate into companies or consulting firms will become
beating hearts on their own account.  (That's a place where the
analogy falters: maybe the university is more like a manufacturer of
hearts.)  We in modern society take for granted something remarkable:
that nearly every site of practice is on both the donating and the
receiving end of these mediated transfers of ideas.  Often we don't
realize it because the people who import ideas by mediation from
other fields will often present them full-blown, without bothering to
explain where they got them.  Other times, a kind of movement will get
going whereby researchers and practitioners unite across disciplinary
lines around a particular metaphor that they find useful for mediating
transfers among themselves: self-organization is one of the fashionable
metaphors of the moment.

Mediating concepts can be used in various ways, but in general what
you see is a mixture of two approaches: explicit comparing/contrasting
of particular cases and something that looks more like abstraction.
The resulting abstractions, however, usually have no great depth in
themselves; their purpose is simply to summarize all of the issues and
ideas and themes that have come up in the various cases, so that all
of them can be transferred to new situations en masse.  This is what
"best practices" research is.  It's also what physicians do when they
codify the knowledge in a particular area of medicine; the human body
is too complicated, variable, and inscrutable to really understand in
any great depth, and so codified medical knowledge seeks to overwhelm
it with a mass of experience loosely organized within some operational
concepts and boiled down into procedures that can be taught, and whose
results can be further monitored.  This is the important thing about
network knowledge: it really does operate in networks -- meaning both
social networks and infrastructures -- and networks are institutions
that have to be built and maintained.  In a sense, network knowledge
is about surveillance, and mediating concepts exist to render the
results of surveillance useful in other places.

The mediating role of concepts can help us to explain many things.
It is a useful exercise, for example, to deliberately stretch the
idea of mediation to situations where its relevance is not obvious.
Philosophy, for example, has long been understand as the ultimate
abstraction, something very distant from real practice.  This is
partly a side-effect of the unfortunate professionalization of
philosophy that led to the hegemony of analytical philosophy in
the English-speaking world perhaps a century ago, but really it
dates much further back into the ancient Greek mythologies of ancient
times.  The popular conception of philosophy as the discipline of
asking questions with profound personal meaning is almost completely
unrelated to the real practice of philosophy at any time or place
in history.  There are exceptions.  One of Heidegger's motivations,
especially in his earliest days, was to reconstruct philosophy around
the kinds of profound meanings that he knew from Catholic mysticism.
Some political philosophers have tried to make themselves useful to
actual concrete social movements.  But for the most part, philosophy
has been terribly abstract from any real practice.

Yet, if we take seriously the mediational role of concepts, then
maybe the situation is more complicated.  One role of the university
is precisely to create concepts that are so abstract that they
can mediate transfers of ideas between fields that are very distant
indeed.  Perhaps we could go back and write a history of the actual
sources of scholars' ideas, and maybe we would find that the very
abstract concepts that scholars learned in philosophy often helped
them to notice analogies that inspire new theories.  Analogies
have long been recognized as an important source of inspiration for
new discoveries, especially in science but in other fields as well,
and nothing facilitates the noticing of analogies so efficiently as
an abstract idea that can be used to describe many disparate things.

I would like to see the university take the mediating role of concepts
more seriously.  I would like every student to be taught a good-sized
repertoire of abstract concepts that have historically proven useful
for talking about things in several disparate fields -- examples
might include positive and negative feedback, hermeneutics, proof by
contradiction, dialectical relationships, equilibrium concepts from
physics, evolution by natural selection, and so on -- and teach them
not as knowledge from particular fields, but as schemata that help
in noticing analogies and mediating the transfer of ideas from one
topic to another.  The students would be drilled on the use of these
concepts to analyze diverse cases, and on comparing and contrasting
whatever the analyses turn up, and then they be sent off to take
classes in their chosen majors.  After a while we could do some
intellectual epidemiology to see which of the concepts actually prove
useful to the students, and we could gradually evolve the curriculum
until we've identified the most powerful concepts.  I do realize the
problem with this proposal: it is bound to set off power struggles
along political lines, and between the sciences and humanities, over
the best repertoire of concepts to teach.  But that's life.

The mediating role of concepts, and network knowledge generally, are
also a useful way to re-understand fields that we normally understand
mostly in terms of their problem knowledge.  (You'll recall that my
classification of fields as "network fields" and "problem fields" is
a heuristic simplification, and that every field has both dimensions.)
What is the network-knowledge dimension of math or computer science?
I've already described one role of professional networking in each
field, which is to provide an audience for one's work.  All research
depends on peer review, so it's in your interest to get out there and
explain the important of your research to everyone who might be asked
to evaluate it.  Likewise, if you need funding for your research then
you'll probably want to assemble a broad coalition of researchers who
explain the significance of their proposed research in similar ways,
so that you can approach NSF or the military with a proposition they
can understand.

But none of that speaks to the network nature of the knowledge itself.
What is network-like about knowledge in math and computing?  It's true
that neither field employs anything like the case method.  But they do
have something else, which is the effort to build foundations.  Much
of math during the 20th century, as I mentioned, was organized by the
attempt to unify different fields, and that means building networks of
people with deep knowledge in different areas.  Only then can proposed
foundations be tested for their ability to reconstruct the existing
knowledge in each area.  In computing, the search for foundations
takes the form of layering: designing generic computer functionality
that can support diverse applications.  In that kind of research,
it's necessary to work on applications and platforms simultaneously,
with the inevitable tensions that I also mentioned above.  So in that
sense math and computer science have a network dimension, and I think
that each field would profit by drawing out and formalizing its network
aspects more systematically.

Even though anthropology is built on deep case studies, the network
nature of its knowledge becomes clearer as you speak with the more
sophisticated of its practitioners.  Anyone who engages seriously
with the depths of real societies is aware that theoretical categories
apply differently to different societies, and that there's a limit to
how much you can accomplish by spinning theories in abstraction from
the particulars of an ethnographic case.  I am basically a theorist
myself, but I realize that my research -- that is, the theoretical
constructs I describe -- is only valuable for the sense it makes of
particular cases.  So I read case studies, and I try to apply my
half-formed concepts to those cases, or else I draw on concepts that
have emerged from particular cases, and then I try to do some useful
work with them.  My work is also influence by personal experience,
usually in ways that I don't write about.  But I can only go so
far before it's time to start testing the concepts against real
cases again, and that's why I often move from one topic to another,
contributing what I can until I feel like I'm out on a limb, beyond
what I can confidently say based on existing case studies and common
knowledge.  It *is* possible to useful things without being directly
engaged with cases, for example pointing out internal inconsistencies
in existing theories, sketching new areas of research that other
people haven't gotten around to inventing concepts for, noticing
patterns that have emerged in the cases so far, or comparing
and contrasting theoretical notions that have arisen in different
contexts.  But if you believe that theory can blast off into space
without any mooring in real cases then you're likely to do the sort
of pretentious big-T Theory that gives us all a bad name.

Anthropologists are thoroughly infused with that understanding, and so
the best ones really do refuse abstraction.  They see their theoretical
constructs very much as ways of mediating between different sites.
Their concern is not practical, so they are not interested in moving
ideas from one site to another on a material level.  They are usually
not trying to help the people they study.  Rather, they are interested
in describing the fullness of the social reality they find in a given
place, and like the business people they understand that the real
test is the extent to which their story about a particular case makes
internal sense.  Granted, they are less concerned than the business
people to be understandable to the people they are studying, although
that too is changing as the "natives" become more worldly themselves,
and as it becomes more acceptable by slow degrees to study "us"
as well as "them".  In any case, I think that the anthropologists'
relationship to theory is healthy, and I wish I could teach it to
people in other fields.  Anthropology is also becoming more network-
like as reality becomes more network-like, and as the myth of discrete
cultures becomes more and more of an anachronism, but that's a topic
for another time.

Knowledge is diverse because reality is diverse.  In fact, reality
is diverse on two levels.  A field like medicine, business, or
library science derives knowledge by working across the diversity
of illnesses, businesses, and information, gathering more or less
commensurable examples of each under relatively useful headings that
can be used as to codify and monitor practice.  And then the various
fields themselves are diverse: they are diverse in diverse ways.
Fields that pride themselves on abstraction operate by suppressing
and ignoring diversity.  That can be okay as a heuristic means of
producing one kind of knowledge -- knowledge that edits the world
in one particular way, and that can be useful when recombined with
knowledge that edits the world in other ways.  But it's harmful when
abstraction is mistaken for truth, and when fields that refuse to
abstract away crucial aspects of reality are disparaged as superficial
compared to the artificial depth at the other end of campus.  Let's
keep inventing metaphors that make network-oriented fields sound
just as prestigious and heroic as problem-oriented fields.  The
point, of course, is not just to mindlessly praise the work, since
bad research can be done anywhere.  The point, rather, is to render
intuitive the standards that can and should guide us in evaluating
research of diverse types.  If we don't, then we will disserve
ourselves by applying standards that don't fit, or else no standards
at all.

end