Scalable Social Simulation Investigating population-scale phenomena using commodity computing Alex Voss School of Computer Science University of St Andrews St Andrews, United Kingdom avoss@cs.st-andrews.ac.uk Andy Turner School of Geography University of Leeds Leeds, United Kingdom Jing-Ya You, Eric Yen, Hsin-Yen Chen, Simon Lin Academia Sinica Grid Computing Acadmia Sinica Taipei, Taiwan Ji-Ping Lin Center for Survey Research Academia Sinica Taipei, Taiwan Abstract—Agent-based simulationmodels provide a way to investigate social phenomena that complement existing social science methods. Recent advances in computing hardware such as the wide availability of multi-core CPUs and increased main memory capacities make it possible to investigate population- scale phenomena using commodity computeresources. This paper describes experiences made in the development of an example model that utilises multiple CPU cores and an investigation of the scalability of the resulting code. We argue that commodity compute resources and commoditised simulation frameworks can now be used to simulate real-world populations and to use these simulations to investigate social phenomena such as migration. Keywords—social simulation; scalability; multi-threading. I. I NTRODUCTION Social Simulation is a relatively new social science research method. It allows social phenomena to be investigated by studying dynamic processes in computational modelsof populations. It complements rather than replaces existing social science research methods as it 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 [1] [2], the study of criminal behaviour [3] or the study of financial markets [4] to name a few examples. Social scientists and other professionals such as policy makers have used various methods in the past to model social phenomena and generate predictions about them. The simplest methods extrapolate from empirical data by fitting functions that approximate the data gathered. A key limitation for such extrapolations was that the underlying model was static, defined in terms of mathematical formulas that had to b manipulated manually [6]. The necessary simplifying assumptions often meantthat the applicability and the predictive power of these models were limited. The advent of computers and their increasing capacity has made it possible build models that exceedby ordersof magnitude the complexity of models that could be built and analysed before. A number of different modeling and simulation approaches have been developed over time that have different characteristics in terms of their ability to scale in variou dimensions, their ability to model dynamic processes with populations, the cost associated with the creation of a m and the computational costs of running a simulation. Choosin the right modeling and simulation approach is a crucial step in the development of a simulation model that needs to b taken with the research question and the phenomena of inter in mind (cf. [6]). Some simulation models represent a system as a whole in terms of a set of equations modeling particular variables interest and their dynamics (cf. [6], esp. chapter 3). These kin of models are used quite extensively in a number of resea areas, often constructed using generic packages such as Matlab™ or Gauss™. The principles of such models are now being popularised by online tools such as Forio Simulate™ [7 The advantage of such models is that they are deterministic a that the relationships between different variables are eas displayed so the model composition and behaviour are easy to understand. However, the associated weakness is that entitie the model are treated as homogeneous and relationships static. Static microsimulation approaches (cf. [6] chapter 4) were first attempt to model societies at the level of individuals and thereby to overcome the limitations of previous approaches in dealing with heterogeneity in populations. However, they not suitable to study changing populations over time. Dynami microsimulation approaches have therefore beendeveloped The work reported in the paper was carried out as part of the EUAsiaGrid project and made use of compute resources within the EUAsia VO. The EUAsiaGrid project was funded by the European Commission as part of its 7 th Framework Programme under contract number FP7-INFRA-223791. Contributions by Alex Voss were funded by the Scottish Funding Council. 2010 Sixth IEEE International Conference on e–Science 978-0-7695-4290-4/10 $26.00 © 2010 IEEE DOI 10.1109/eScience.2010.46 1