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Quantifying patterns of research-interest evolution

Nature Human Behaviour volume 1, Article number: 0078 (2017) Cite this article

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Abstract

To understand quantitatively how scientists choose and shift their research focus over time is of high importance, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded1–9. Despite extensive investigation into various factors that influence a scientist’s choice of research topics8–21, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research-interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, and we show that changes in research interests follow a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research-interest evolution5,22. We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations. This work uncovers and quantitatively analyses macroscopic patterns that govern changes in research interests, thereby showing that there is a high degree of regularity underlying scientific research and individual careers.

‘The essential tension’ hypothesis as described in ref. 5 has highlighted the conflicting demands of scientific careers that require both exploration and exploitation4,8,22. Indeed, career advancement, from promotion to obtaining grants, demands a steady stream of publications, which is often achieved through uninterrupted, yet incremental contributions to the existing, established research agenda. By contrast, frequent changes in research topics invite risk of failure and decreased productivity. The disciplinary boundaries, arising from factors such as implicit culture, tacit and accumulated knowledge23,24 and peer recognition3,25, together with intensifying specialization in science and engineering disciplines26, make radical shifts, such as moving from chemical biology to high energy physics, unlikely, if at all possible. On the other hand, although a steady and focused research portfolio helps scientists stay productive, it potentially undermines chances for originality8. Indeed, innovative and novel insights often emerge from encountering new challenges and opportunities associated with venturing into new topics and/or incorporating them into the existing research agenda4,15,20,27,28.

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Figure 1: An example demonstrating the procedure to compose a topic tuple and topic vector.
Figure 2: Patterns in the research-interest evolution.
Figure 3: An illustration of the ‘seashore walk’.
Figure 4: Results of the ’seashore walk’.

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Acknowledgements

We thank A.-L. Barabasi for providing the initial dataset, A.-L. Barabási and G. Korniss for discussions. This work was supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053. T.J. is supported by the Natural Science Foundation of China (61603309) and CCF-Tencent RAGR (20160107). D.W. is supported by the Air Force Office of Scientific Research under award number FA9550-15-1-0162 and FA9550-17-1-0089. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Tao Jia

  2. Laboratory for Software and Knowledge Engineering, Southwest University, Chongqing, 400715, China

    Tao Jia

  3. Kellogg School of Management, Northwestern University, Evanston, 60208, Illinois, USA

    Dashun Wang

  4. Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, 60208, Illinois, USA

    Dashun Wang

  5. McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, 60208, Illinios, USA

    Dashun Wang

  6. Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute, Troy, 12180, New York, USA

    Boleslaw K. Szymanski

  7. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, 12180, New York, USA

    Boleslaw K. Szymanski

  8. Społeczna Akademia Nauk, Łódź, 90-113, Poland

    Boleslaw K. Szymanski

Authors
  1. Tao Jia
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  2. Dashun Wang
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Contributions

T.J., D.W. and B.K.S. designed the research. T.J. performed numerical simulations and analysed the empirical data. T.J., D.W. and B.K.S. prepared the paper.

Corresponding authors

Correspondence to Tao Jia, Dashun Wang or Boleslaw K. Szymanski.

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Jia, T., Wang, D. & Szymanski, B. Quantifying patterns of research-interest evolution. Nat Hum Behav 1, 0078 (2017). https://doi.org/10.1038/s41562-017-0078

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