S. Phineas Upham

Emerging research fronts in science and technology: patterns of new knowledge development

Research fronts represent the most dynamic areas of science and technology and the areas that attract the most scientific interest. We construct a methodology to identify these fronts, and we use quantitative and qualitative methodology to analyze and describe them. Our methodology is able to identify these fronts as they form—with potential use by firms, venture capitalists, researchers, and governments looking to identify emerging high-impact technologies. We also examine how science and technology absorbs the knowledge developed in these fronts and find that fronts which maximize impact have very different characteristics than fronts which maximize growth, with consequences for the way science develops over time.

Citation structure of an emerging research area on the verge of application

A case study of an emerging research area is presented dealing with the creation of organic thin film transistors, a subtopic within the general area called “plastic electronics.” The purpose of this case study is to determine the structural properties of the citation network that may be characteristic of the emergence, development, and application or demise of a research area. Research on organic thin film transistors is highly interdisciplinary, involving journals and research groups from physics, chemistry, materials science, and engineering. There is a clear path to industrial applications if certain technical problems can be overcome. Despite the applied nature and potential for patentable inventions, scholarly publications from both academia and industry have continued at a rapid pace through 2007. The question is whether the bibliometric indicators point to a decline in this area due to imminent commercialization or to insurmountable technical problems with these materials.

Finding cohesive clusters for analyzing knowledge communities

Documents and authors can be clustered into “knowledge communities” based on the overlap in the papers they cite. We introduce a new clustering algorithm, Streemer, which finds cohesive foreground clusters embedded in a diffuse background, and use it to identify knowledge communities as foreground clusters of papers which share common citations. To analyze the evolution of these communities over time, we build predictive models with features based on the citation structure, the vocabulary of the papers, and the affiliations and prestige of the authors. Findings include that scientific knowledge communities tend to grow more rapidly if their publications build on diverse information and if they use a narrow vocabulary.

Analyzing knowledge communities using foreground and background clusters

Insight into the growth (or shrinkage) of “knowledge communities” of authors that build on each others work can be gained by studying the evolution over time of clusters of documents. We cluster documents based on the documents they cite in common using the Streemer clustering method, which finds cohesive foreground clusters (the knowledge communities) embedded in a diffuse background. We build predictive models with features based on the citation structure, the vocabulary of the papers, and the affiliations and prestige of the authors and use these models to study the drivers of community growth and the predictors of how widely a paper will be cited. We find that scientific knowledge communities tend to grow more rapidly if their publications build on diverse information and use narrow vocabulary and that papers that lie on the periphery of a community have the highest impact, while those not in any community have the lowest impact.

Innovating knowledge communities

A useful level of analysis for the study of innovation may be what we call “knowledge communities”—intellectually cohesive, organic inter-organizational forms. Formal organizations like firms are excellent at promoting cooperation, but knowledge communities are superior at fostering collaboration—the most important process in innovation. Rather than focusing on what encourages performance in formal organizations, we study what characteristics encourage aggregate superior performance in informal knowledge communities in computer science. Specifically, we explore the way knowledge communities both draw on past knowledge, as seen in citations, and use rhetoric, as found in writing, to seek a basis for differential success. We find that when using knowledge successful knowledge communities draw from a broad range of sources and are extremely flexible in changing and adapting. In marked contrast, when using rhetoric successful knowledge communities tend to use very similar vocabularies and language that does not move or adapt over time and is not unique or esoteric compared to the vocabulary of other communities. A better understanding of how inter-organizational collaborative network structures encourage innovation is important to understanding what drives innovation and how to promote it.

Positioning knowledge: schools of thought and new knowledge creation

Cohesive intellectual communities called “schools of thought” can provide powerful benefits to those developing new knowledge, but can also constrain them. We examine how developers of new knowledge position themselves within and between schools of thought, and how this affects their impact. Looking at the micro and macro fields of management publications from 1956 to 2002 with an extensive dataset of 113,000+ articles from 41 top journals, we explore the dynamics of knowledge positioning for management scholars. We find that it is significantly beneficial for new knowledge to be a part of a school of thought, and that within a school of thought new knowledge has more impact if it is in the intellectual semi-periphery of the school.

Is economics scientific? Is science scientific?

Abstract The usefulness of models that describe the world lies in their simplicity relative to what they model. But simplification entails inaccuracy, so models should be treated as provisional. Nancy Cartwrights account of science as a modeling exercise, in which fundamental laws hold true only in theory—not in reality, given the complexities of the real world—suggests that Rational Choice Theory (RCT) should not be rejected on the traditional basis of its lack of realism: that, after all, is to be expected of any simulacrum model. But when RCT has been extended to domains, such as politics, in which there is no necessary reason to expect systemic pressures against people who depart too far from the model, RCT is a simulacrum without any particular claim to expressing underlying causal laws. This cautions against the tendency to rest content with models and to treat their assumptions as if they were true.

FREEDOM FOR THE FUTURE: THE INDEPENDENT VALUE OF FREEDOM IN LIGHT OF UNCERTAINTY

ABSTRACT Both classical and modern liberals tend to treat freedom of choice as if it is intrinsically valuable—regardless of what is chosen. They fear that treating freedom as, instead, instrumental only to good choices might open the door to paternalism if a polity were to decide that people were making bad choices. A middle course would be to treat freedom as independently valuable. On the one hand, the independent value of freedom does not treat all choices as good as long as they are freely made. On the other hand, it does not reduce the value of freedom to the known, or predictable, good ends to which a free action may be conducive. Following from Hayek’s acknowledgement that we are often ignorant of what the future may hold, freedom may have value because it will allow us to make decisions whose positive consequences cannot now be predicted.