1 / 17

Social Simulation Tutorial

Social Simulation Tutorial. International Symposium on Grid Computing Taipei, Taiwan, 7 th March 2010. Agenda. The Team. Alex Voss School of Computer Science University of St Andrews Andy Turner School of Geography University of Leeds

reba
Download Presentation

Social Simulation Tutorial

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Social Simulation Tutorial International Symposium on Grid ComputingTaipei, Taiwan, 7th March 2010

  2. Agenda

  3. The Team • Alex VossSchool of Computer ScienceUniversity of St Andrews • Andy TurnerSchool of GeographyUniversity of Leeds • Rob ProcterManchester e-Research CentreUniversity of Manchester

  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. 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. PART I: SOCIAL SIMULATION ETC. IN A NUTSHELL

  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. 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. 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. Simulation as a Method Model Simulated Data Simulation Implementation and Verification PopulationReconstruction Model Validation Abstraction Collected Data Data Gathering or Re-Use Target Adapted from Gilbert & Troitzsch, p. 17

  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. 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. ABM Conceptual Components Agents Context Behaviour Relationships Environment

  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. 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. 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. Social Simulation: Reading • Nigel Gilbert and Klaus G. Troitzsch: Simulation for the Social Scientist(cress.soc.surrey.ac.uk/s4ss/) • Joshua M. EpsteinGenerative Social Science:Studies in Agent-Based Computational Modeling(http://press.princeton.edu/titles/8277.html)

More Related