Description 
1 online resource (xxx, 269 pages) 
Series 
Springer complexity 

Understanding complex systems, 18600832 

Springer complexity


Understanding complex systems

Contents 
Part I Foundations  An Introduction to Modeling Science: Basic Model Types, Key Definitions, and a General Framework for the Comparison of Process Models  Mathematical Approaches to Modeling Science From an Algorithmichistoriography Perspectice  Part II Exemplary Model Type  Knowledge Epidemics and Population Dynamics Models for Describing Idea Diffusion  Agentbased Models of Science  Evolutionary Game Theory and Complex Networks of Scientific Information  Part III Exemplary Model Applications  Dynamic Scientific Coauthorship Networks  Citation Networks  Part IV Outlook  Science Policy and the Challenges for Modeling Science  Index 
Summary 
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 coauthorship 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, gametheoretic approaches, agentbased simulations, populationdynamics 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 algorithmichistoriography perspective. It concludes with a survey of future challenges for science modeling and discusses their relevance for science policy and science policy studies 
Analysis 
Physics 

Engineering 

Socio and Econophysics, Population and Evolutionary Models 

Information Systems Applications (incl. Internet) 

Complexity 
Bibliography 
Includes bibliographical references and index 
Subject 
Science  Mathematical models


Computational complexity.


Physique.


Astronomie.


Computational complexity


Science  Mathematical models

Form 
Electronic book

Author 
Scharnhorst, Andrea


Börner, Katy


Besselaar, Peter van den

ISBN 
9783642230684 

3642230687 

3642230679 

9783642230677 
