Simulating the Social Processes of Science

The science system is continuously growing and since long recognized as a special complex social system. Not surprisingly, for an understanding of this system a wide range of methods have been used including mathematical models. Recently, a special issue appeared in the Journal of Artificial Societies and Social Simulation, a highly recognized international journal devoted to the use of computer simulations in the exploration of social processes. As the guest editors write: “This Special Section of JASSS presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as “science”. It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction. ” Interestingly, this issue comes also together with a blog, inviting authors and readers to further discourse (see ).

The journal is open access, the bibliographic reference is Edmonds, Bruce, Nigel Gilbert, Petra Ahrweiler, and Andrea Scharnhorst. 2011. “Simulating the Social Processes of Science.” Journal of Artificial Societies and Social Simulation 14 (4): 1-6.

Related publications on this topic are: Börner, Katy, Wolfgang Glänzel, Andrea Scharnhorst, Peter van den Besselaar, eds. 2011. Modeling Science: Studying the Structure and Dynamics of Science. Editorial of a special issue. Scientometrics 89(1): 347-348.

Scharnhorst, A., K. Börner, and P. Van den Besselaar (eds) (2012) Models of Science Dynamics – Encounters Between Complexity Theory and Information Sciences. Springer, Understanding Complexity Series, 300 pages (see ).

Mathematical models and simulations of science rely on different kind of data: from anecdotical evidence to large data samples extracted from bibliographic databases or collaborative web-environments. In the current practice of modeling science sharing data sets is still in its infancy. Moreover, copyright issues – when using datasets from publishers – often make data exchange not even possible. Both to establish data sharing practices and to work at the mathematical mapping of different models are current challenges and should be on the agenda for science modelers.