By David E. Wojick. Ph.D.
March 2014
(This paper was accepted and distributed at the March 6, 2014 US Library of Congress conference on "Analytical Methods for Technology Forecasting.")
Today’s engineers are under pressure to develop and deploy
new technologies at an ever-quickening pace. Thus the world of engineering might best be described as one of technology chaos. In the midst of all this
uproar, engineers are supposed to know what is going on and coming on, but the
projects and possibilities are legion. Clearly, engineers have a lot of
homework to do for their own projects. They also have to avoid being blindsided
in a meeting by the latest whiz-bang Wall Street Journal article.
Technology assessment in the face of this kind of chaos may seem impossible,
but it is also mandatory. Why is it all so complicated?
Diffusion confusion
How does an engineer find out what’s happening that’s
related to his or her field or project? Why is it so hard? The basic technology
assessment problem can be put in one word—diffusion. The flow of knowledge in science
and technology is an extremely complex diffusion process. Much of the
complexity is due to two simple processes that overlay one another—convergence
and divergence.
Shown above is a simplified map of science and technology
diffusion. The sheer complexity of the interrelationships is what makes the
concept so hard to grasp, even though the individual relationships may be
clear. Not only is the pattern complex, but it represents many possible
combinations of divergent and convergent flow over time in the future. The
possibilities are not endless, but they are many. They may even be well
defined, but the array is structurally complex and virtually impossible to
visualize mentally. As with many engineering problems, mapping the complexity
is the answer.
Science and technology diffusion
The
flow of knowledge is like a complex diffusion process. Each lettered box
represents a project. Links indicate the potential flow of results from one
project to another at a later stage of development, or from left to right.
Each
lettered box represents a project currently being developed. Projects span the
spectrum of development, from basic, cutting-edge research to fielding
established technologies. The projects included depend on the scale of
interest. A project’s focus might be narrow, like corrosion on turbine blades,
or very broad, like clean coal technology. On a broader scale, the projects
might represent entire research communities.
Links
indicate the potential flow of results from one project to another at a later
stage of development, or from left to right. For simplicity’s sake, each
project is shown feeding just three downstream projects. Not shown is that fact
that new projects may come into being, and existing ones may disappear, as time
goes by.
There
are a great many link-by-link paths between distant projects. That’s the
consequence of divergence and convergence. Results from A may diverge, working
their way to C or D, or both. Yet C and D may be very different
technologically. Likewise, results from A or B, or both, may converge on C. It
is the combination of paths that creates the complexity and confusion, not the
individual paths themselves.
People
working on operational-level technologies, on the right side of the map, tend
to look for convergence. But for those seeking to understand how a new basic
technology will change the status quo, divergence is mostly what they look at.
Trying to look at both at the same time is already very hard, and it’s getting harder.
Bounding the Problem
In
fact, comprehending everything in any technology forecasting case is a daunting
challenge. A few Google searches no longer suffice. But hope is not lost.
Understanding the diffusion of the science and technology related to a specific
engineering issue is like any other engineering problem. You have to scale it
to your resources and use the proper tools. In other words, you have to bound
the problem or it cannot be solved.
A
science and technology assessment problem might include any of the following.
The point is that these are very different problems.
1.
The convergence of diffusion pathways to a given technology, problem, or
application.
2.
The divergence of a particular breakthrough.
3.
The neighborhood or cluster of activities related to a specific technology at a
specific stage of development.
4. A
single transition pathway from a specific project to a specific application.
Other
combinations of projects and pathways also are possible. In every case, it is
critical to limit the search to just those projects and links that can be
feasibly assessed. The feasibility of understanding is the key concept here.
One cannot examine everything.
Diffusion Distance
It is
particularly important to distinguish basic research, pilot tests, and the like
from real-world applications. This involves what I call the "diffusion
distance" or the number of projects and links between projects at
different stages of development. Speculation about new science and technology
in the general press often misunderstands and understates the great diffusion
distance from basic research to actual application. Every stage of development
normally takes several years to work through. In particular, basic research
breakthroughs often take 10 to 30 years to become useful. Even proven pilot
technologies may be a long way from actual application. A feasible assessment
may have to simply ignore distant diffusion.
On
the other hand, if one has a very specific technical problem, the solution may
already exist in a distant research community. Other things being equal, the
narrower the technical problem, the more distant the search can be. Once again
it is a matter of knowing what can and cannot be done.
Case study: Naval R&D
Some
time ago I tested this diffusion mapping concept for the US Chief of Naval
Research. The Navy clusters its R&D projects into an ascending series of
seven categories of so-called “budget activities.” Category 1 is basic
scientific research, while category 7 is operational systems development. We
began with categories 2 and 3, respectively called “applied research” and “advanced
technology development,” as these are the earliest technology development
categories. Our primary data were project descriptions, especially what are
called Research and Development Descriptive Summaries (RDDS).
In
every case we were able to identify clear transition paths from projects in
categories 2 and 3 to category 7, via projects in the intermediate categories.
Interestingly this typically involved something we call semantic overlap or
linking. This means that the language that occurs in a given project typically
overlaps that used in projects that are adjacent along a given transition path.
Thus we were able to find the transition paths using semantic search tools.
