Models of Science Dynamics

Encounters Between Complexity Theory and Information Sciences

  • Andrea Scharnhorst
  • Katy Börner
  • Peter van den Besselaar

Part of the Understanding Complex Systems book series (UCS)

Table of contents

  1. Front Matter
    Pages i-xxx
  2. Foundations

  3. Exemplary Model Types

    1. Front Matter
      Pages 67-67
    2. Nicolas Payette
      Pages 127-157
  4. Exemplary Model Applications

    1. Front Matter
      Pages 193-193
    2. Franc Mali, Luka Kronegger, Patrick Doreian, Anuška Ferligoj
      Pages 195-232
    3. Filippo Radicchi, Santo Fortunato, Alessandro Vespignani
      Pages 233-257
  5. Outlook

    1. Front Matter
      Pages 259-259
    2. Peter van den Besselaar, Katy Börner, Andrea Scharnhorst
      Pages 261-266
  6. Back Matter
    Pages 267-269

About this book


Models of science dynamics aim to capture the structure and evolution of science. They are developed in an emerging research area in which scholars, scientific institutions and scientific communications become themselves basic objects of research. In order to understand phenomena as diverse as the structure of evolving co-authorship networks or citation diffusion patterns, different models have been developed. They include conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, and computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive research agenda.

This book aims to fill this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented here cover stochastic and statistical models, game-theoretic approaches, agent-based simulations, population-dynamics models, and complex network models. The book starts with a foundational chapter that defines and operationalizes terminology used in the study of science, and a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of future challenges for science modeling and discusses their relevance for science policy and science policy studies.


citation diffusion and networks co-authorship networks idea diffusion processes modeling science science dynamics scientometrics

Editors and affiliations

  • Andrea Scharnhorst
    • 1
  • Katy Börner
    • 2
  • Peter van den Besselaar
    • 3
  1. 1.Humanities and Social Sciences, Royal Neth. Academy of Arts a. SciencesThe Virtual Knowledge Studio for theAmsterdamNetherlands
  2. 2.Center, School of Library and Information Sc.Cyberinfrastructure for Network ScienceBloomingtonUSA
  3. 3.The Rathenau InstituteScience System Assessment CenterThe HagueNetherlands

Bibliographic information