General Critical Properties of the Dynamics of Scientific Discovery

Scientific fields are difficult to define and compare, yet there is a general sense that they undergo similar stages of development. From this point of view it becomes important to determine if these superficial similarities can be translated into a general framework that would quantify the general advent and subsequent dynamics of scientific ideas. Such a framework would have important practical applications of allowing us to compare fields that superficially may appear different, in terms of their subject matter, research techniques, typical collaboration size, etc. Particularh' important in a field's history is the moment at which conceptual and technical unification allows widespread exchange of ideas and collaboration, at which point networks of collaboration show the analog of a percolation phenomenon, developing a giant connected component containing most authors. Here we investigate the generality of this topological transition in the collaboration structure of scientific fields as they grow and become denser. We develop a general theoretical framework in which each scientific field is an instantiation of the same large-scale topological critical phenomenon. We consider whether the evidence from a variety of specific fields is consistent with this picture, and estimate critical exponents associated with the transition. We then discuss the generality of the phenomenon and to what extent we may expect other scientific fields — including very large ones — to follow the same dynamics

General Critical Properties of the Dynamics of Scientific Discovery

Scientific fields are difficult to define and compare, yet there is a general sense that they undergo similar stages of development. From this point of view it becomes important to determine if these superficial similarities can be translated into a general framework that would quantify the general advent and subsequent dynamics of scientific ideas. Such a framework would have important practical applications of allowing us to compare fields that superficially may appear different, in terms of their subject matter, research techniques, typical collaboration size, etc. Particularh' important in a field's history is the moment at which conceptual and technical unification allows widespread exchange of ideas and collaboration, at which point networks of collaboration show the analog of a percolation phenomenon, developing a giant connected component containing most authors. Here we investigate the generality of this topological transition in the collaboration structure of scientific fields as they grow and become denser. We develop a general theoretical framework in which each scientific field is an instantiation of the same large-scale topological critical phenomenon. We consider whether the evidence from a variety of specific fields is consistent with this picture, and estimate critical exponents associated with the transition. We then discuss the generality of the phenomenon and to what extent we may expect other scientific fields — including very large ones — to follow the same dynamics

Report for the Office of Scientific and Technical Information: Population Modeling of the Emergence and Development of Scientific Fields

The accelerated development of digital libraries and archives, in tandem with efficient search engines and the computational ability to retrieve and parse massive amounts of information, are making it possible to quantify the time evolution of scientific literatures. These data are but one piece of the tangible recorded evidence of the processes whereby scientists create and exchange information in their journeys towards the generation of knowledge. As such, these tools provide a proxy with which to study our ability to innovate. Innovation has often been linked with prosperity and growth and, consequently, trying to understand what drives scientific innovation is of extreme interest. Identifying sets of population characteristics, factors, and mechanisms that enable scientific communities to remain at the cutting edge, accelerate their growth, or increase their ability to re-organize around new themes or research topics is therefore of special significance. Yet generating a quantitative understanding of the factors that make scientific fields arise and/or become more or less productive is still in its infancy. This is precisely the type of knowledge most needed for promoting and sustaining innovation. Ideally, the efficient and strategic allocation of resources on the part of funding agencies and corporations would be driven primarily by knowledge of this type. Early steps have been taken toward such a quantitative understanding of scientific innovation. Some have focused on characterizing the broad properties of relevant time series, such as numbers of publications and authors in a given field. Others have focused on the structure and evolution of networks of coauthorship and citation. Together these types of studies provide much needed statistical analyses of the structure and evolution of scientific communities. Despite these efforts, however, crucial elements of prediction have remained elusive. Building on many of these earlier insights, we provide here a coarse-grained approach to modeling the time-evolution of scientific fields mathematically, through adaptive models of contagion. That is, our models are inspired by epidemic contact processes, but take into account the social interactions and processes whereby scientific ideas spread - social interactions gleaned from close empirical study of historical cases. Variations in model parameters can increase or hamper the speed at which a field develops. In this way, models for the spread of 'infectious' ideas can be used to identify pressure points in the process of innovation that may allow for the evaluation of possible interventions by those responsible for promoting innovation, such as funding agencies. This report is organized as follows: Section 2 introduces and discusses the population model used here to describe the dynamics behind the establishment of scientific fields. The approach is based on a succinct (coarse) description of contact processes between scientists, and is a simplified version of a general class of models developed in the course of this work. We selected this model based primarily on its ability to treat a wide range of data patterns efficiently, across several different scientific fields. We also describe our methods for estimating parameter values, our optimization techniques used to match the model to data, and our method of generating error estimates. Section 3 presents brief accounts of six case studies of scientific evolution, measured by the growth in number of active authors over time, and shows the results of fitting our model to these data, including extrapolations to the near future. Section 4 discusses these results and provides some perspectives on the values and limitations of the models used. We also discuss topics for further research which should improve our ability to predict (and perhaps influence) the course of future scientific research. Section 5 provides more detail on the broad class of epidemic models developed as part of this project

The Power of a Good Idea: Quantitative Modeling of the Spread of Ideas from Epidemiological Models

The population dynamics underlying the diffusion of ideas hold many qualitative similarities to those involved in the spread of infections. In spite of much suggestive evidence this analogy is hardly ever quantified in useful ways. The standard benefit of modeling epidemics is the ability to estimate quantitatively population average parameters, such as interpersonal contact rates, incubation times, duration of infectious periods, etc. In most cases such quantities generalize naturally to the spread of ideas and provide a simple means of quantifying sociological and behavioral patterns. Here we apply several paradigmatic models of epidemics to empirical data on the advent and spread of Feynman diagrams through the theoretical physics communities of the USA, Japan, and the USSR in the period immediately after World War II. This test case has the advantage of having been studied historically in great detail, which allows validation of our results. We estimate the effectiveness of adoption of the idea in the three communities and find values for parameters reflecting both intentional social organization and long lifetimes for the idea. These features are probably general characteristics of the spread of ideas, but not of common epidemics

DALI team:

The Lunar Radio Array (LRA) is a concept for a telescope sited on the far side of the Moon with a prime mission of making precision cosmological measurements via observations of the highly-redshifted H I 21-cm line. Technology development in the 2010–2020 decade is required for a successful start to the LRA in the 2020–2030 decade. Many of these technologies are applicable to other NASA missions, space missions conducted by other Government agencies, or commercial interests. A key issue is that, while other interests are developing these technologies, focused investments will be necessary to make them applicable to the LRA and for astrophysics missions, in general. The table below presents a prioritized list of these investments. Hydrogen is the dominant component of the intergalactic medium (IGM), and the LRA potentially will provide precision cosmological measurements from observations of the state of the IGM prior to the formation of the first stars and unique information about the state of the IGM and large-scale structures after the first stars formed. Primary questions include: Does the standard cosmological model describe the Universe during the “Dark Ages, ” before the first stars? How does the IGM evolve during this important time? What were the properties of high-z galaxies? How did they affect the Universe? What is the nature of the field that drove inflation