Also, I think there has always been a great deal of tension, at least in policy making circles, which is borne out in part in the 1960s with the HINDSIGHT and the TRACES study about fundamental (basic) versus applied research. Professor Stokes has mounted the richest challenge to the linear model to date and I think his new model has moved away from a lot of the ideological positions that people have staked out. Professor Kash is absolutely right, these relationships always have had strong ideological components. I don't see a way around that either.

The final point is that I was not saying that the exercise is not worthwhile. I'm saying that the danger with GPRA in particular is that when you implement monitoring systems, either ex ante or ex post, you have to recognize that you have instituted a very dangerous activity. You have to recognize human behavior. You are going to get people "cooking" the numbers to present them in the most favorable light.

Robert A. Lichter: With respect to the Government Performance and Results Act (GPRA), I fully agree. I'm trying to look at this from a broader perspective. GPRA is just one particular constraint.

David A. Hounshell: My point is: It's symptomatic, even within the private sector.

Don E. Kash: I would agree with that wholeheartedly, and I would go a step further and say the enterprise that you're involved in not only is useful, it's absolutely necessary, but it is not necessary because it's going to demonstrate any good way to quantitatively measure this relationship. It is essential, because the political system requires us to go through this about on the same cycle as DuPont's 14-to 16-year cycle. That is real. The key point is that in going through what is a politically, socially, and perhaps financially mandated requirement, don't buy into something where the numbers get "cooked" and then come back and kill you.

Andrew Kaldor: Professor Kash, you lumped the world of R&D into your four categories. I guess my take-away message is that complex/complex is growing, and the Japanese are doing it better than we are. But in terms of our mission today, I'm not sure how we can handle this in terms of getting a measure for the effectiveness of research. It seems like a more productive approach would be to work backwards from a well-known or valuable product or development of some kind and trace the innovative process through the development stages back to the research phases and then back to "eureka." Gathering a database like this might help us better understand this process and come up with an effective measure.

Don E. Kash: We've done that with seven cases. One of them is a chemical case where the innovation clearly came out of the laboratory, the central laboratory. Now, we also have done it for a blade on a high pressure turbine on a jet engine. Here the ball game gets very mixed. It's a complex world out there—it goes back to the water wheel—and at least some of the engineers you talk to refer to the "black art" in casting.

One of the things that's really been terribly important is that much of the "black art" has been converted from tacit knowledge into explicit knowledge because of a whole new technology: computer-aided design. It is absolutely fascinating when you get these old engineers who know how to do things without understanding why, and put them at the front end of the process, inputting their knowledge into the computer. My point in this connection is that a lot of technology seems to take place without any understanding. It is surely not overwhelmingly based on explicit scientific research. In fact, an awful lot of scientific research is explaining what technology has done in advance, and so it has been for at least 400 years.

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement