Nathan Rosenberg, Professor of Economics at Stanford University, gave a talk last Thursday at PARC entitled, “The Endogeneity of Technological Change in 20th Century America.” Endogeneity, as defined by Rosenberg, is the process of responding to changes in market forces.    (97)

According to Rosenberg, up until recently, economic historians viewed technological innovation largely as an exogenous process; that is, independent of market forces. Rosenberg argues that not only is much technological innovation endogenous, but that in the last century, scientific and technological innovation in the U.S. has been especially endogenous.    (98)

Rosenberg made an interesting point about how this endogenous view contrasted with the traditionally linear view of technological innovation. (Donald Stokes explores this topic extensively in his book, Pasteur’s Quadrant; see my previous reference to Stokes.) Engineering disciplines are often thought of as “applied sciences,” whereas in many cases, scientific research is actually an application of engineering disciplines. For example, solid state physics was rarely taught until after the transistor was invented in 1948. Following the transistor, investments in solid-state research increased dramatically, as did the number of physicists who specialized in that field. Similarly, basic research in polymer chemistry at Dupont in the 1920s (which eventually led to the invention of nylon, among other things) didn’t occur until after Dupont had made several advances in chemical engineering. The latter convinced the company that it had the capability to develop advances in polymer chemistry into marketable products.    (99)

Why We Need Models    (9A)

Rosenberg’s topic was stimulating, but something he said early in his talk caught my ear. He stated that he was skeptical that we could derive rigorous models of technological innovation, but that we could derive a great deal of valuable knowledge by considering these models.    (9B)

A group of us at Blue Oxen Associates are currently working on another community case study, and one of the topics we’re exploring is the effects of tools and processes on these communities. One of the researchers asked about how we could set up properly scientific experiments. I responded that I didn’t think it was possible. There are things you can do to make a study more “scientific,” but those things don’t guarantee rigorous results. That said, I don’t think a study has no value if you don’t have a control group. At the Hypertext 2002 Workshop on Facilitating with Hypertext, Jeff Conklin had this memorable line: “All abstractions are wrong, but they can still be very useful.”    (9C)