Diffusion of Knowledge

Science Depends on the Diffusion of Knowledge:

According to the National Science Foundation, there are over 2.5 million research workers worldwide, with more than 1.2 million in the U.S. alone.1 If we look at all the articles, reports, emails and conversations that pass between them, we could count billions of knowledge transactions every year. This incredible diffusion of knowledge is the very fabric of science.

Given that the diffusion of knowledge is central to science, it behooves us to see if we can accelerate it. We note that diffusion takes time. Sometimes it takes a long time. Every diffusion process has a speed. Our thesis is that speeding up diffusion will accelerate the advancement of science.

The millions of researchers are grouped into thousands of communities. A community may be defined as a group of researchers working on a single scientific problem.

The Web of Science indexes about 8,700 journals2, representing many different research communities. That's a lot of science to keep up with. Currently it is difficult for researchers, who primarily track journals within their specific discipline, to hear about discoveries made in distant scientific communities.

In fact, diffusion across distant communities can take years. In contrast, within an individual scientific community, internal communication systems are normally quicker. These include journals, conferences, email groups, and other outlets that ease communication.

Many communities use related methods and concepts: mathematics, instrumentation, and computer applications. Thus there is significant potential for diffusion ACROSS communities, including very distant communities. We see this as an opportunity.

Sequential Diffusion is Too Slow!

Diffusion to distant communities takes a long time because it often proceeds sequentially, typically spreading from the community of origin (A) to a neighbor (B), then to community (C), a neighbor of B, and so on. This happens because neighboring communities are in fairly close contact.

Science will progress faster if this diffusion lag time is diminished. The concept of global discovery is to transform this sequential diffusion process into a parallel process. This means that new knowledge flows directly to distant communities. The goal is to reduce the lag time from years to months and from months to days.

Modeling Knowledge Diffusion Suggests How to Accelerate It

In thinking about how to speed up diffusion across distant communities, we have looked at diffusion research, including computer modeling. We are particularly interested in recent work that applies models of disease dynamics to the spread of scientific ideas. The spread of new ideas in science is mathematically similar to the spread of disease, even though one produces positive results, the other negative. Our goal is to foster epidemics of new knowledge.

You might ask "Why is the math of disease related to the math of knowledge diffusion?" It is because neither involves considerations of conservation of mass. This makes disease and knowledge diffusion unlike many other kinds of diffusion that obey laws of conservation of mass. Consider, for example, diffusion of pollution. If pollution diffuses from point A to point B, point A now has less of it. But if knowledge diffuses from person X to person Y, person X still has what he started with.

We have been working with a group of modelers led by Luis Bettencourt of Los Alamos National Laboratory. They have written an important new paper, currently in press in Physica A: Statistical Mechanics and Its Applications, entitled: "The power of a good idea: quantitative modeling of the spread of ideas from epidemiological models."3 This paper applies a disease model to the spread of Feynman diagrams just after World War II. Feynman diagrams are a central method of analysis in particle physics.4