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


But first, a correction.  In my comments on Enron earlier today,
I said that marketplaces are public goods.  That's usually not true,
and having fouled up the point, let me take a moment to explain
it.  According to economics, a public good is a good that has two
properties: it is nonexcludable, meaning that once it exists it's
impossible to keep people from using it, and nonrival, meaning that
many people can use it at once.  Pure public goods are rare, but
many things come close.  National defense is a common example: once
the country has been protected, everyone gets the benefit of the
protection.  Ideas are nearly public goods: you can keep them secret,
but once they're out there everyone can use them.  The best book
about public goods for noneconomists is Inge Kaul, Isabelle Grunberg,
Marc Stern, eds, Global Public Goods: International Cooperation in
the 21st Century, Oxford: Oxford University Press, 1999.  The concept
of a public good has several failings, but I'll ignore them for now.

Public goods are important because markets can't be expected to
provide them.  Markets only provide things because someone can make a
profit doing so, and if you can't exclude people from using something
then you have no way of making a profit from it.  For this reason, it
is often proposed that public goods be provided from tax money.  In
fact many people prejudge the issue by conflating the economic concept
with the idea of something provided by the government.  This doesn't
automatically follow, however, for all kinds of reasons.  Some public
goods are actually bad things -- we're talking about "goods" in the
economic sense, not the moral sense, so that ethnic stereotypes are
public goods that the government shouldn't be providing.  Also, the
government should only provide things that the government is likely
to do a better job than the market.  Many public goods are provided
through indirect market mechanisms, e.g., through advertising, as
side-effects of the production of non-public goods, and so on.  So
even though public provision of public goods is often a good idea,
each case needs to be analyzed on its own terms.

Marketplaces are generally not public goods, for the simple reason
that if you can build a fence around something then it's not a public
good.  So, for example, the flea market might charge each vendor a fee
to set up a stand, or it might charge each shopper a fee to enter the
fairgrounds where the market is being held.  When I used the phrase
"public good", I did have some real issues in mind, just not the ones
that correspond to that phrase.  I was thinking, first, about various
laws and customs that require kinds of market-makers to deal with
everyone on equal terms.  I was also thinking about the problematic
nature of many, if not most, decisions about how marketplaces should
be paid for.  Marketplaces, as I mentioned, tend to exhibit network
effects, meaning that the more people participate in them, the more
valuable they become.  Large marketplaces thus tend to crowd out
smaller ones, other things being equal, so that competition among
marketplaces is often unstable.  A monopoly marketplace can extract
rents (i.e., above-competitive prices) from its participants.  This
can happen through high prices imposed on vendors or sellers, but it
can also happen through the marketplace's attempts to interfere with
the goods being sold, the terms of sale, the nature of advertising,
and so on.  When a marketplace does impose such restrictions, though,
it can be hard to prove that they are anticompetitive.  On the other
hand, even if a marketplace is a monopoly, it doesn't automatically
follow that the market in marketplaces has failed.  It's complicated,
is the point, and it's often very unclear what the right answer is.

What's worse, the mechanisms for charging people to participate
in a marketplace are often determined more by the practicalities of
extracting money from them than by the economically optimal answer.
Thus, for example, the flea market should ideally charge a "tax" on
every transaction rather than charging all participants a flat fee.
The flat fee distributes the burden unfairly and thus causes some
potential participants to stay home.  But it's not practical for a
flea market to keep track of everyone's transactions precisely enough
to collect taxes.  So if we come across a marketplace that pays for
itself through a peculiar mechanism that nobody can explain, it need
not follow that anything is wrong, though it may well follow that
an opportunity exists for someone who can invent a better mousetrap.

The flea market makes a good example because it's harmless; we can
follow the economic concepts without getting mad.  When we move along
to the California electricity market, however, things get bad really
fast.  The complexities of electric power trading are quite amazing.
To start with, you've got the power grid, which is a vast, delicate,
and extremely dangerous physical object.  Even on a good say the
power grid only operates because of intense, highly-evolved real-time
cooperation among parties spread across half the continent, any one
of whom is capable of turning out the lights in Los Angeles on a few
seconds' notice.  The problem is hard enough when those parties all
work for the same organization, but when they work for competing firms
then life gets very hard.

Furthermore, the economics that you learned in economics classes
-- you know, the economics where they assume that the commodity is
uniform in every way except maybe the one little way that a particular
model is concerned with -- doesn't have much to do with real markets,
even in the case of electricity, where you would think that the
commodity is just about as uniform as it could possibly be.  In fact,
electricity markets have a diversity of different kinds of producers,
each with its own attributes and thus its own agenda, and every last
detail of the very complicated market mechanisms has huge implications
for the strategic positions of each of these producers.  That's why
it helps to buy politicians, and why you want to have the resources
to set the agenda and work the negotiations from day one, and get your
favorite economists into the loop, and so on.  It is far from clear
that we know how to organize that kind of process so that it produces
a rational outcome.  In economics dreamworld it's easy.  In this
world it's not.  We read again last week about how energy trading
is "inevitable", and I'm sure it seems inevitable to people who live
in the dreamworld of economics.  It probably also seems inevitable
to people who live in the world where privatization happens whether
it makes sense or not.  That's a world much closer to our own.  But
in the real world, "inevitable" is nothing but a refusal to think or

I don't know what's going to happen, and I don't even have much of
an opinion about it.  But I do know that the Enron debacle exposes
some predictable systemic difficulties with modern real-time markets.
It's partly about the accounting industry, whose absolute perfidy
appears to be the one area of indisputable consensus at the moment.
But that's just the start.  For one thing, I haven't even heard
the first breath of the right answer to the problem with accounting:
establish shareholder cooperatives to organize audits.  We know a
great deal about how to govern cooperatives, and even badly governed
shareholder cooperatives will do a better job of choosing accounting
firms than (for heaven's sake!) the firms that are being audited.
And even when we solve that problem, we still haven't begun to think
about the institutional demands of the world we're walking into.
We can expect to hear a lot of fog in the next few months, because
many of the same people who celebrate markets would be terrified
to confront the rigors of real ones.  It's much better to maintain
a bunch of dark corners where they can rip people off.


The rise of object world.

One of those revolutions that keeps not quite happening is called
"distributed objects".  Although object-oriented programming was
invented with the intention of simulating real objects in the world,
in practice objects are bundles of data that interact by sending
"messages" to one another.  Objects are said to be "distributed" when
they can reside on any Internet host, keeping track of one another and
exchanging messages without any regard to their location.  Distributed
object standards do exist, but they are not a big part of the average
user's life.  There are some good reasons for this, but never mind
about that.  Instead, I want to imagine what the world will be
like once standards for distributed objects are widely accepted and
implemented, and once the creation and use of distributed objects
becomes a routine part of life.

Let's say we're having a conversation, and that I recommend a book to
you.  You'd like to follow up, so I pick up an object that represents
the book (technically, a pointer to an instance of the "book" object),
and I hand it to you.  These actions of "picking up" and "handing"
could be physically realized in several ways.  For example, I could
pick up a small token, wave it over a copy of the book, and click on
it with my finger to lock in the book's identifier.  (A Xerox PARC
concept video shows something like this.)  Working out an interface
for this idea is a lot harder than telling a story about it, but at
least it's one idea about what "picking up" and "handing" might mean.

Another possibility is that we could use software that monitors our
conversation for objects it can recognize, either interpreting the
fluent speech or waiting for us to address it with commands.  The
software could show each of us a steady stream of candidate objects
that it thinks we're referring to, and we could stop occasionally to
poke one of those objects, indicating that we want to save it or share
it.  It would be fascinating to watch people develop conversational
conventions for interacting with this device.  It would stop being
a novelty in short order, and people would conjure objects frequently
without making a big deal of it.

Software people think by classifying the world into object classes,
some of them more abstract than others.  This way of thinking is
hidden from normal people, but in object world we would have to
democratize it.  People would become aware of object classes, and
communities would have tools to come up with new object classes of
their own.  (Paul Dourish is doing something like this.)  It would
be useful to have an object class for any sort of "thing", abstract
or concrete, that you want to maintain any kind of relationship with.
When you make an airplane reservation, for example, an object could
materialize in front of you, and you could drop it into your wallet.
That object would include several attributes, all of them tracking
the current state of the flight: whether it has been cancelled, when
it is scheduled to leave, your seat assignment and meals, and so on.

Airplane reservations are an obvious example, already famous from the
early-1990s vaporware of "agents", but object world really starts when
people start noticing "hey, that's a sort of 'thing' that I would like
to track".  Some of the "things" would be physical objects, and others
would be more abstract.  For example, if you are traveling to Omaha
on April 22nd and 23rd, you can go to a weather site on the Web and
pick up on object for the weather in Omaha on those days.  Of course,
there will be gnashing of teeth about whether you are going to pick
up one object, or two objects, or two objects contained within an
abstract "visit" object, or what, but that is the normal gnashing of
teeth that programmers engage in all the time.  We're just going to
democratize the problem.  As the date approaches, the weather objects
will continually update themselves to the best available forecast.
Once you reach Omaha, the objects will start tracking the weather in
real time.  When your visit is done, you'll have objects that record
the weather statistics for Omaha in fine detail.  If you care about
these objects then you can include them in your diary or trip report.
If not then you can toss them.

Objects can be complicatedly interlinked.  When you buy a car, you
would also get a complex, interlinked pile of objects, one for every
component of the car.  The components that include electronics would
be connected in real time to their object representations, so that
the objects reflect the current state of the components and some of
the components can be controlled remotely by manipulating the objects.
When a component is replaced at the repair shop, its object would
be discarded and a new object would be linked in with the others.
In fact, every auto part in the world would have a unique ID embedded
in its corresponding object; when the part is manufactured, the object
is manufactured along with it, and the object will then shadow the
part forever.  When the part is discarded or destroyed, the object
will hang around as a record of it.  This may sound complicated, but
once the necessary standards and infrastructure are in place it will
be perfectly routine.  Besides, it's already happening on a large
scale, and the resulting numbers are data-mined for quality-control
and insurance purposes.  Once this system goes public, children will
grow up assuming that everything has a digital shadow.

Objects can be designed to provide different kinds of access to
different sorts of people.  You could have a "home page" or other
public space (perhaps by then the whole idea of a home page will
be quaint) where you post some objects for anyone to take, such as
your latest publication or the upcoming performance of your theater
company.  You could also distribute objects that make a wider range
of attributes available to some people and a narrower range available
to others.  Decent graphical tools will allow regular non-programmers
to design such things.

We are a long way from object world.  We could build demonstration
versions of it today, and someone surely has.  (Do send URL's for
papers about any demonstrations.)  To make the idea scale, though,
real problems need to be solved on several levels.  One problem
is middleware.  Lots of work has been done on distributed object
databases, but now we're talking about making billions of objects
available in billions of locations, exchanging them over wireless
links, and all sorts of hard things.  If I have a link to my car's
objects in my wallet, where are the objects really stored?  How widely
are they replicated, and how is that replication managed?  Are they
really going to be kept constantly up to date while the car is being
driven?  Will they be updated ten times a second?  That's a lot of
data.  Or will the updates wait until I use the object?  How does
the object know it is being used?  And what if I associate policies
with the object that it should take such-and-such an action if
such-and-such predicate on the attributes of the car starts holding,
for example to detect theft?  Once we ask these questions, we come
face-to-face with the problem of middleware, the intermediate layers
of software that are capable of supporting the full range of answers
to these questions that might be reasonable in one context or another.
To design middleware you need some idea of the range of applications
and the practical problems that arise in them, and that in turn
requires a fair amount of painful iteration between the applications
level (which wants the middleware to stand still) and the middleware
level (which wants to keep issuing new releases forever).

Other issues arise at the interface level.  The idea is to insinuate
objects into the finest corners of everyday life.  If we interact
with objects only by mousing them on desktops then we have failed.
The voice-recognition interface that I sketched above is half an idea
of what it would be like to weave object-manipulation into the most
ordinary activities.  No doubt other interface models will work best
in particular settings, and these interfaces will have to work for
a wide variety of people.  Interface design is relatively easy if
you have the user's complete attention, which has been the case for
nearly all interfaces so far, but interfaces for people who are mainly
focused on something else are harder.  Likewise, interfaces are easy
to design if you have immobilized the user's body, but they are harder
to design if three-quarters of the user's bodily orientation is driven
by a practical task in the real world, such as fixing a car.

The hardest problems, perhaps, are semantic.  It is already a serious
problem that people in different communities use words in different
ways and thus recognize different categories of things.  An object
like "the day's weather" can probably mean very different things to
athletes, meteorologists, hikers, event planners, and disaster relief
workers.  The negotiation of those semantic differences has heretofore
happened behind the scenes, but in object world it will become more
public.  The world is full of standards organizations dead-set on what
they call "semantic interoperability", meaning that language gets its
meaning standardized for the convenience of computers.  The whole idea
makes my flesh crawl, but in object world we'll definitely have to
decide if we want it.


Logarithmic change.

One of the many unexamined assumptions of the cyberfuturists is that
exponential growth in the power of computers (which nobody doubts)
automatically implies exponential growth in the impacts on society.
If you really believe this, and it's implicit in a great deal of
public prognostication, for example in the work of Ray Kurzweil,
then society is headed for a revolution of inconceivable proportions.
But what if it's not true?  I have already suggested one reason why
social impacts might not be proportional to computing power: there
may simply be a limit to the amount of computing that society has any
use for.  But that argument is no more convincing than the opposite;
its purpose is really to balance one implausibility with another.

Let me try another counterargument -- or really a counterintuition
-- that might work better.  The counterintuition is that computing
has its "impacts" on society logarithmically.  The technological
changes might be exponential, but the consequences might be linear.
If you list the potential applications of computing, they fall along
a spectrum, and that spectrum has a logarithmic feeling to it.  One
category of applications requires 100 units of computational zorch,
the next category requires 1,000 units, the next category requires
10,000 units, and so on, and each category of applications is equally
easy for society to absorb.  Think, for example, of the computational
differences between audio and video.  We can now do quite general
audio processing in cheap, ubiquitous embedded processors, but
the same is not yet true with video.  The result is a natural kind
of pacing.  Society can digest the technological, entrepreneurial,
cultural, criminal, and aesthetic possibilities of digital audio
processing, and then it can move along to digest the possibilities of
video processing.  Society will be digesting new digital technologies
for a long time, no doubt about it, and we are always getting better
at living in a world of continual change.  But the technologies will
appear in manageable bunches, and we will learn to manage them.

It would be helpful to have a model of the space of forthcoming
applications of computing.  One approach is to think about computing
applications in terms of their input sources and output sinks.  As
to inputs: you can't compute without data, and the data has to come
from somewhere.  So the first question is how the data is produced.
Sometimes this isn't a problem.  Nature provides an infinite supply
of data, and natural sciences applications (the paradigm case is
environmental monitoring) will be able to absorb fresh supplies of
computational capacity forever.  But what are the output sinks for
natural sciences computing?  That is, who uses the results?  In order
to change the world, the outputs need to feed into the social system
someplace where they create new opportunities or destabilize old ones.
If scientists write research papers based on experiments that employ
petabytes of data rather than terabytes, does the world change at all?
The world does change somewhat because of the organization required
to capture petabytes of data; someone has to install and maintain the
sensor arrays.  But the institutions of science are already geared to
maintaining substantial distributed infrastructures.  The picture of
the lone scientist in the lab coat was old-fashioned a generation ago.

Other examples can be multiplied.  Video games that employ gigaflops
rather than megaflops are still video games.  They fit into the world
in the same way, they appeal to the same audience, and they require
the same amount of time and attention.  People likewise only have a
certain number of hours in a day that they can spend talking on the
telephone.  We can do a much better job of delivering print material
to people -- there's no reason why the average citizen can't skim
the equivalent of a several hundred books a year -- but again people's
capacity maxes out after a while.

In my view, the applications of computing that most clearly change the
world are the ones that involve the "object world" that I described
above.  (David Gelernter refers to something similar as the "mirror
world", but I've explained elsewhere the many problems with the
mirror metaphor.)  Consider again the case of the digital car object.
That case is perhaps a little frivolous, given that few people need
to track the deep workings of their cars.  Even so, there's a quiet,
profound revolution going on: everything in the world is growing a
digital shadow, that is, a data object that reflects its attributes
in real time, not to mention its entire history and its various
simulated futures.  It's a quiet revolution because it's hard to do.
It's not going to be an overnight success like the Web.  It requires
whole layers of existing practices to be ploughed up and replanted,
and that means trashing decades of tacit knowledge and innumerable
small power bases.  In the long run, however, it really does change
the world.  It allows various activities to unbundle themselves
geographically and organizationally and then reassemble themselves in
new combinations.  (It doesn't mean that the whole world disassembles
irreversibly into fragments, however, no matter what you've heard.)
It creates whole new power arrangements based on access to objects;
it cuts people loose from old social arrangements while binding them
into new ones.

The question is how significant the social effects of the object
world are as a function of the magnitude of the data they involve.
Clearly, it will be a long time before everything in the world acquires
a full-blown digital shadow.  The computing power required to make
real-time data on every device in the world available to every other
device in the world is several orders of magnitude greater than what's
available now.  It will happen, given exponential improvements in
computing, but it will happen slowly.  Assuming that organizational
change processes can keep up with technological improvements (perhaps
we'll have a lag of undigested technological capacity at some point),
we can imagine the object world being built and taken for granted
within a few decades.  The process has two kinds of terminus: when
we've exhausted the input side by mirroring every object in the world,
and when we've exhausted the output side by doing everything with the
mirrored data that we can possibly (I mean, profitably) imagine doing.

Be that as it may, it would still be helpful to have intuitions about
the magnitude of the social impact as a function of the magnitude
of the inputs.  If we track ten times as many parameters of the
world, does that cause ten times the consequences?  It seems unlikely.
For one thing, we can expect people to objectify the high-impact
data first -- meaning, the data that produces the highest payoff
relative to the effort of capturing it.  And some types of data are
harder to capture than others, relative to the infrastructure and
manufacturing techniques that are available at a given time.  It is
relatively easy to capture the operating parameters of an auto engine;
the engine is already full of computers, all of which will soon be on
a general-purpose network.  Embedding sensors and processors in every
bolt will take longer and result in orders-of-magnitude increases in
the amount of available data, but it's hard to imagine that digital
bolts will change the structure of the auto industry much more than
the first round of objectifying that's under way now.  The first round
really *will* change the industry, and not just the manufacturers but
the insurers, repairers, hobbyists, regulators, and cops.  But the
next round?  I suspect that much of the institutional framework to
deal with those data flows will already be in place.  We will see.


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

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.