Quantifying the rise and fall of scientific fields

Abstract

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.

What are patterns of rise and decline?

“…For example, following Singh et al . [14] novel scientific fields are hardly perceived as fields in their early phases of formation. This early phase, however, may be the most influential time for the future content and success of the field.…”

“…catching the onset of decline before catastrophic crashes) but also recognizing when a new entity of importance is in formation or when a recognized entity is losing integrity due to becoming something else. For example, following Singh et al [14] novel scientific fields are hardly perceived as fields in their early phases of formation. This early phase, however, may be the most influential time for the future content and success of the field.…”

“…in the case of prolonged extinction phase termed ‘dead clade walking’ [79]). Rapid rise and prolonged decline have been shown to be common in the histories of biological species [13] as well as in the evolution of scientific fields [14].…”

“…in microbial communities) or foresee a new entity, such as a new scientific field, forming even when it has not yet reached the integrity that would allow intuitively defining it. For example, novel scientific research fields tend to start without a name or rigid boundaries, as vaguely defined collaborations among people from very different backgrounds and proceed to become more coherent programmes with more specialized players and well-defined boundaries [14]. Paralleling this, Bettencourt et al [78] showed that the network of co-authorships unravels and re-structures in the onset of a new scientific field emerging.…”

“…Similarly, hat-shaped unimodal success curves that describe the birth, rise, peak, decline and disappearance of an entity across time have been identified in biological, cultural and economic systems [11,12]. Success curves of both biological species [13] and scientific fields [14] tend to show a similar right-tailed curve, a rapid rise followed by a gradual decline over time.…”