From: "Eric Armstrong" <email@example.com>
The workshop has made it abundantly clear that mankind is
faced with deep, significant problems. It is equally clear
that the "Dynamic Knowledge Repository" is the only significant
place where technology can be leveraged to improve mankind's
*ability* to solve complex problems.
We have already greatly expanded our communication and publication
technologies, with the result that we have an explosion of
information. What is needed is a better way to harness, interact
with, and utilize that knowledge base. Hence the importance of
a "Dynamic Knowledge Repository".
But what does such a thing look like? How does it work? How will
it make a difference?
I suspect that the system will look like a combination of email
and a hypertext archive (OHS, anyone?) but with some unusual
aspects. To understand what they have to be, and what they are
needed, I think it would help to start with a few observations
about weakness in our current information systems, which are:
Much of what gets published in the world is superflous, excess
noise. A lot of what counts towards the "information explosion"
is episodes of Jerry Springer, copies of People magazine, etc.
Needless to say, it is important to be able to remove superfluous
information from a knowledge repository.
Where real information exists, it is frequently presented a
dozen different ways. A journal article on the subject of
nutrition, an exploratory article in Newsweek, and a one-page
digest in Prevention magazine may all have the same fundamental
information as their foundation. Each is presented differently,
but a single information-model underlies them all. Ideally, that
model would be the foundation for a knowldege repository.
Email lists provide a great opportunity for discussion and
exploration of a subject area. Archives provide a rich mine
of background and information pertaining to the subject. But
such systems are totally "additive". Every contribution,
including summaries of the conversation up till now, becomes
an addition to the system.
A knowledge system, though, will need to be "reducible" in
the sense that a digest of previous posts should subsume those
posts. Those posts should still be available for investigating
the accuracy of the reduction, or to get more background
information, but they should no longer have a life of their
own -- they should be tucked "under" the reduction, so as to
simplify the view presented by the archives.
[Note: There are three ways for that reduction to happen,
depending on how the repository functions. More on that later.]
The important thing about these observations is that the goal is
to create a dynamic *knowledge* system, not merely another
*information* system. To do that, at least two other characteristics
need to be considered:
Like reducibility, abstractability is a desirable characteristic
of the system. It should function in a similar way, in the sense
that an abstract representation subsumes specific instances. The
difference is that the specific items don't disappear from the view,
as they do after a reduction. Instead, the specifics remain in
view, allowing investigators to operate at either more general or
more specific levels of detail.
An important aspect of any knowledge system is that, frequently,
what we *think* to be true turns out to be dead wrong. A good
knowledge system must have the capability to reduce the knowledge
base, not by deletion, but by marking a theory or fact as invalid,
and attaching the data to support that conclusion. After all, the
conclusion may itself be wrong. It must be possible to hide negated
precepts so we can focus on what we "know", but it must at the same
time be possible to revisit and possibly revive them.
Those are general characterisics for a Dynamic Knowledge Repository.
Given those characteristics, it seems likely that the ideal solution
would make use of some kind of "abstract knowledge mathematics". Like
symbolic logic, a knowledge mathematics would make possible a concise
expression of information and simplify the process of abstraction,
reduction, and negation. However, while symbolic logic deals with only
a few very simple relationships (true, false, if..then) an "abstract
knowledge mathematics" requires a seemingly infinite variety of
relationships. For example, attempting to model the human nutrition
system requires the ability to state relationships like these: improves,
requires, enables, is required for, is enabled by, causes, hinders,
prevents, manifests as, etc. Such a system would need to be extensible,
as well, since it is unlikely that everything which needs to be expressed
could possibly be anticipated.
If an "abstract knowledge mathmatics" were created, it would seem likely
that it would be very helpful for creating a knowledge repository. One
advantage of such a system would be the relative ease with which people
who speak different languages could interact with it.
On the other hand, the attempt to create a knowledge mathematics should
probably operate in parallel with the building of the first knowledge
repository. The problem is too complex, and the problems we face too
urgent, to make the solution wait. In the ideal scenario, we would solve
some of the most urgent problems in a natural-language system, as the
abstract knowledge system is developed. The natural-language system would
serve as the model for the abstract system, and provide something to check
it's operation against. Subsequent problems could then be attacked in the
The way in which reduction and abstraction works in such a system
would depend on whether the system uses an abstract knowledge
mathematics. If so, the system might be capable of automatic
reductions or possibly human-directed reductions where the computer
takes care of the details. At the very least, it should be possible
to do automated verification of reductions and abstractions that are
offered to the system. In a natural language system, on the other
hand, such operations must necessarily be manual -- which raises the
the necessity for competing reductions and abstractions, with arguments
for and against tallied with each, until at last some resolution is
The preceeding thoughts discuss general requirements for an ideal knowledge
repository. Each is a guideline. After all, even email archives are giving
us tremendous new tools to augment our thinking -- as are email lists like
this one, which put us in touch with concepts and ideas from thinkers
around the globe. So anything we put together is likely to be of *some*
help. Let's get a version 1.0 out, and start a list of things to do in
To get started, it seems helpful to attack a specific problem. Having
a real problem to solve provides a concrete referent when building the
system, provides a real-world validity test, and hopefully helps to
solve a real problem. Even if the system doesn't make much of an impact
with it's initial problem (because it's users are system designers
rather than domain expertes) *attempting* to solve the problem motivates
the system extensions that are really needed.
I'll propose the energy problem as a starting point because, (a) that
seems to be the most pressing problem we have seen to date and (b)if
we can't run the computers there's not a hell of a lot else that we
are going to be able to do with our technology.
In looking at a complex problem like the energy crisis, a number of
subsystems suggest themselves:
* problem definition
* data (time-stamped)
* tactical possibilities
* strategic alternatives
* proposed models (model construction kit)
* design decisions
* feedback (data)
The remainder of this post takes a short look at each subsystem.
This is a summary view of the problem, outlining it's fundamental
characteristics and egregious consequences. The problem statement
subumes, and is built on, the fundamental data that identifies the
problem. As the data changes, the summary needs to be periodically
updated as well. The change in the problem statement over time
therefore shows whether or not we are making progress.
For the energy problem, the data concerns who is using how much,
and how much is being generated. The data changes over time, so
accumulated data must be time-stamped.
If we drove less, telecomuted more, lived closer to work, used
public transportation more, built better solar panels -- each of
these is a tactic that may serve to ameliorate the problem. A
tactic, almost by definition, tends to be a one-dimensional
"solution" to a problem. The knowledge repository must be able
to track such approaches, providing whatever data is available on
the potential gain.
A strategy combines multiple tactical approaches in a unified way.
Or at least it pretends to be unified. A strategy might also
consist of a single intervention which indirectly motivates the
tactical attacks. For example, "doubling gasoline taxes" is a
possible strategy which increases telecommuting, motivates people
to live closer to work, reduce driving, and take public transporation.
Strategic obstacles and methods for circumventing them must also
be identified. For example, a political system in which policy-making
is dominated by profit-seeking corporations may have a serious
problem in summoning the political will to implement the only strategy
which stands to make a difference. If so, that obstacle must be
identified as the fundamental, irremedial difficulty that it is, so
that discussion can focus on how to modify the political system so that
change *becomes* possible. (Or possibly an alterative can be found?
We should be so lucky.)
Proposed Models (model construction kit)
Strategic visions are extremely difficult to quantify. Most political
discussions therefore resolve around spurious logic and disfunctional
rhetoric. A true "knowledge repository" needs something better, however.
One possibility is the capability of supporting strategic alternatives
with one or more "proposed models" that shows how it would work.
The system should make model-building easier by providing something in
the nature of a "Model Construction Kit". In such a system, you would
drag and drop sources, sinks, processes in a 3-dimensional framework
that you could rotate and view. You would construct flow lines and
feedback loops by dragging lines between the objects.
A system similar to the one used by the Education Objects Economy might
be useful here. A Java application might be used to run the models, or
perhaps the model would itself be a Java applet that could be run by
Like the EOE system, "attribution" is liable to be the most
profound motivator for contributions to the knowledge repository.
That makes autmomatic, accurate attribution a vital part of the
Some models would be conceptual. Others would be quantitative. It should
also be possible for a model to be totally conceptual and migrate to a
quantitative version in stages, being "operational" in some respect every
step of the way.
With such a system, arguments would be more oriented towards the validity
of the model, or of the data it is based on. Competing models could be
presented, and decisions made on something more than fundamental instinct
about the "rightness" of a given strategy.
The best argument for or against a given model will be based on real
feedback. If the anticipated result of an intervention is as predicted by
the model, then the model will have at least some degree of validity.
Although different models may predict different long term effects, at
those which are incorrect in the long term can be negated.
As in any project, the construction of a model entails a number of design
decisions. The ability to track those decisions would also provide a lot
benefit. Most models, for example, need to simplified in order to be
understandable at all. Ideally, the simplifications that were chosen
be recorded in a design journal, along with the reasoing. When the model
is questioned at a later date, the original assumptions can be
If they still seem to be valid, the model can justifiably remain
If it turns out that the assumption was in error, however, the need for
change is clear.
[Note: I've had both kinds of experiences. In some cases the design
journal justifies the decision. In others, it points out the long-
forgotten reasons, making it possible to easily consent to changes that
would otherwise be regarded with deep suspicion.]
A "Dynamic Knowledge Repository" of some kind is critical to augment
mankind's ability to solve urgent, complex problems. It is the one
area where technology can be brought to bear on that issue, itself
an urgent, complex problem, as Doug has made us vivdly aware. An
ideal system will be reducible, abstractable, and negatable. It will
also track and attribute contributions in the areas of problem
definition, data tracking, tactical possibilities, strategic alternatives,
model building, feedback, and tracking design decisions.
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