1. SICSA student induction day, 2009Slide 1 Social Simulation Tutorial International Symposium on Grid Computing Taipei, Taiwan, 7 th March 2010

2. SICSA student induction day, 2009Slide 2 Agenda TimeSessionSpeaker 09:00Registration 09:30Welcome, What is social simulation? What is Agent-Based Modelling (ABM)? Alex Voss 10:00The RePast Simphony Toolkit, an example modelAlex Voss 10:30Coffee break 11:00Practical I: installing RePast and running modelAlex Voss 12:00Population reconstructionAndy Turner 12:30Lunch 14:00Infrastructures for social simulationRob Procter 14:30Introduction to grids and clouds 15:00Coffee break 15:30Practical II: running model ensembles on grids and cloudsAlex Voss 16:45Closing Remarks and feebackAlex Voss 17:00End

3. SICSA student induction day, 2009Slide 3 The Team Alex Voss School of Computer Science University of St Andrews Andy Turner School of Geography University of Leeds Rob Procter Manchester e-Research Centre University of Manchester

4. SICSA student induction day, 2009Slide 4 Aims of the Tutorial To provide a brief introduction to agent-based simulation, population reconstruction and RePast Simphony. To motivate the use of grids and clouds for running social simulation ensembles To demonstrate use of grids and clouds and provide practical instructions for running your own models Not to replace RePast tutorial!

5. SICSA student induction day, 2009Slide 5 Practicalities Using your own laptop Participant sheet with login URL for instructions: https://e-research.cs.st-andrews.ac.uk/SimISGC2010 Download software and certificates from local server (see URL above) – also USB sticks to pass around Using GILDA training infrastructure

6. SICSA student induction day, 2009Slide 6 PART I: SOCIAL SIMULATION ETC. IN A NUTSHELL

7. SICSA student induction day, 2009Slide 7 Background Much social science does not use advanced ICT but emergence of new analytical methods is driven by: –Increased availability of data about social phenomena –But data is ‘messy’, anonymised, aggregated, incomplete –Challenges to analyse social phenomena at scale –Challenges to inform practical policy and decision making (e.g., evidence-based policy making)

8. SICSA student induction day, 2009Slide 8 What is Social Simulation? A new approach to modeling social phenomena Based on empirical data Based on existing theories A new way to explore them, complementing other forms of modelling and prediction Used to understand and predict Not just one form of simulation: systems dynamics, microsimulation, queueing models, etc.

9. SICSA student induction day, 2009Slide 9 What is Social Simulation? (II) Models necessarily incomplete There can always be more detail –Higher spatial and temporal resolution –More and more detailed attributes –Geography and social science is no different to any other type of science in this respect Need to assess impact of decision about how to model

10. SICSA student induction day, 2009Slide 10 Simulation as a Method Model Simulated Data Target Collected Data Adapted from Gilbert & Troitzsch, p. 17 Abstraction Simulation Data Gathering or Re-Use Validation Population Reconstruction Model Implementation and Verification

11. SICSA student induction day, 2009Slide 11 What is Agent-Based Modelling? Simulating interactions between dynamic populations in changing environment Heterogeneous populations – each individual has specific attributes such as age, gender, socio-economic status, health, etc. Stochastic process – each run can differ from previous Notion of emergence – larger-scale phenomena produced through many small interactions / events Sets of simple rules produce complex behaviour – sets can be large… Can help model and analyse phenomena too complex for closed form, can be used in absence of knowledge about causality

12. SICSA student induction day, 2009Slide 12 ABM Frameworks Rapid growth over last 10 years Many implementations General frameworks (open source): –Swarm –MASON –NetLogo –Repast –Repast Simphony Aim to separate concerns clearly to maximise modeling capability

13. SICSA student induction day, 2009Slide 13 ABM Conceptual Components PopulationFemalesPregnantMales MortalityFertility

14. SICSA student induction day, 2009Slide 14 Building Simulation Models Model Design – about the choices made, cf. research design Model implementation – cf. research method (‘the logistics’, ‘plumbing’) Verification – checking the implementation matches the design Validation – checking the design represents the aspects of the world modeled

15. SICSA student induction day, 2009Slide 15 Building Simulation Models (II) Simple models can be built using graphical editors (cf. RePast tutorial) More complex models and behaviours inevitably require programming Data management etc. become important as size grows Calls for interdisciplinary collaboration, division of labour see above

16. SICSA student induction day, 2009Slide 16 Trust in Models Scientific codes often many years old and carefully maintained Social simulation is in its infancy – relatively speaking Need to build community development approaches that produce robust, reliable code Yet need for flexibility and adaptability – research is about doing things not done before…

17. SICSA student induction day, 2009Slide 17 Social Simulation: Reading Nigel Gilbert and Klaus G. Troitzsch: Simulation for the Social Scientist (cress.soc.surrey.ac.uk/s4ss/) Joshua M. Epstein Generative Social Science: Studies in Agent-Based Computational Modeling (http://press.princeton.edu/titles/8277.html)