Thursday, February 27, 2014

Mapping Technology Chaos

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.

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