TUTORIAL ON 'SOCIAL SIMULATIONS'
International Symposium on Grid Computing (ISGC 2010)
7 March 2010
Academia Sinica, Taipei, Taiwan
, School of Computer Science, University of St Andrews, UK
, Manchester e-Research Centre, University of Manchester, UK
, School of Geography, University of Leeds, UK
If you have any questions about this tutorial please send an email to Alex Voss at
Agent-Based Social Simulation is a relatively new social science research method. It allows social phenomena to be investigated by studying dynamic processes in computational models of populations. Social simulation complements existing social science research methods and builds on empirical findings and theories of social phenomena. Applications of social simulation range from the study of basic demographic processes to uses in a range of interdisciplinary research areas such as public health, urban studies or criminology, to name a few examples.
Social scientists as well as other professionals such as policy makers have used various methods in the past to model social phenomena and generate predictions about them. At its simplest, one can extrapolate from empirical data by fitting functions that approximate the data gathered. In the past, a key limitation for such extrapolations was that the underlying model was a static one, defined in terms of mathematical formulas that could be manipulated manually. The necessary simplifying assumptions often meant that the applicability and the predictive power of these models was limited. The advent of computers and their increasing capacity has made it possible to build models that consist of representations of social agents and their environment and to simulate their behaviour and interactions.
Such agent based models (ABMs) are still limited by the capacity and processing speed of today's computer systems. It is now possible to simulate relatively large populations with a reasonable number of variables even on a desktop computer. However, to study social phenomena it is often necessary to run multiple simulations that vary specific variables in order to study their impact. Such parameter studies can be relatively easily distributed across a number of computers using grid technologies. Each instance of a simulation run with a specific set of parameters can be sent to a different machine in a grid, with all the machines working in parallel to produce an overall result.
This tutorial aims to provide an introduction to the principles behind social simulation and to demonstrate these principles by teaching participants how to develop a social simulation model and running a simulation using the
|11:00|| Practical I -|
|14:00||Infrastructures for Social Simulation (Rob Procter)|
|14:30||Introduction to Grids and Cloud Computing|
|15:30|| Practical II - running model ensembles on grids and clouds|
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