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Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. 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.

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File URL: http://jasss.soc.surrey.ac.uk/14/4/14/14.pdf
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Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 14 (2011)
Issue (Month): 4 ()
Pages: 14

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Handle: RePEc:jas:jasssj:2011-67-1
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  1. André C. R. Martins, 2010. "Modeling Scientific Agents For A Better Science," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 519-533.
  2. Andreas Pyka & Nigel Gilbert & Petra Ahrweiler, 2006. "Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks," Discussion Paper Series 287, Universitaet Augsburg, Institute for Economics.
  3. Ismael Rafols & Alan Porter & Loet Leydesdorff, 2009. "Overlay Maps of Science: a New Tool for Research Policy," SPRU Working Paper Series 179, SPRU - Science and Technology Policy Research, University of Sussex.
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