1. An Introduction to Social Simulation Andy Turner http://www.geog.leeds.ac.uk/people/a.turner/ Presentation as part of Social Simulation Tutorial at the 5 th International e-Social Science ConferenceSocial Simulation Tutorial at the 5 th International e-Social Science Conference Cologne, Germany, 2009-06-24

2. Outline What Why Progress –MoSeS –GENESIS –NeISS

3. What Social simulation from a geographical perspective –Main agents are people –Each person agent Has attributes Can interact with others Is effected by and effects the environment Can learn about itself, the environment and other agents Is located in and can move within the environment Changes over time –The environment Has 3 spatial dimensions although often only 2 are used. Changes over time

4. Why do Social Simulation? To help estimate risk and predict change so that planning can be more proactive To try to understand how things work and how things have evolved To build models that we can use in education, to capture/integrate knowledge and for decision support It can be fun…

5. Social simulation is complex There are many interactions in a complex modern society, where do we start? Birth and Death –This requires some basic concept of fertility and mortality Next, we get the agents located in space and moving around Once this is done and we have a population which moves around in space and evolves over time, we move onto detail –What interactions effect fertility and mortality? What do we add in next? –Application dependent? –Resource –Health –Education –Services

6. Coping with complexity Build from the bottom up –When adding something new into the model it is important to consider how this effects and is effected by what is already there –The model becomes an attempt to build in realism to the model E.g. How can a person become pregnant at a given time? What constraints are there? –How do these effect outcomes? Randomness has to be substituted for lack of knowledge There are many possible alternatives and to perform scientific experiments and to be able to recreate results we need to be able to recreate results –Set random number seeds and create provenance data Ensemble modelling may help us identify the extremes and the most likely outcomes

7. Challenges Grid enabling the data and tools Visualisation –Global Earth –Computer Games Collaboration Retaining a problem focus Design and Development Keeping up with the State of the Art

8. MoSeS http://portal.ncess.ac.uk/access/wiki/site/moses/home.html –First phase research node of NCeSS –Complete The idea was to work towards providing UK planners, policy makers and the public with a tool to help them analyse the potential impacts and the likely effect of planning and policy changes. Example Applications: –Health and social care –Transport –Housing Key was the development of individual and household level population data for the UK from 2001 UK human population census data. –This now an input into GENESIS

9. GENESIS http://portal.ncess.ac.uk/access/wiki/site/GENESIS/home.html –Second phase research node of NCeSS –Began in October and ongoing for 3 years subject to review Focus on generative geographical simulation Combines the modelling strengths of MoSeS with the visualisation strengths of GeoVUE My work on GENESIS –Developing "agent based models" of two types: 1.Based on publicly available data that can be freely distributed 2.That take account of more restricted data, but which might be more realistic/detailed Target temporal scales and resolutions –Daily time step, run for years –Second time step, run for days Target spatial scales –A city region approximately10km in diameter –National –Global Attribute/Agent detail –Upto and over 1Million agents The computational requirements for these models are considerable and I am investigating ways to scale and partition the task

10. NeISS http://portal.ncess.ac.uk/access/wiki/site/e-iss/home.html UK JISC funded –Part of the Information Environment 2009-2011 Programme –3 years –Started in April Developing a national e-Infrastructure for social simulation Key components of the work: –orchestrate the data and computation –develop modelling and analysis tools –Loop around a publication cycle by encapsulating the process in workflows that can be enacted and re-enacted Open development

11. Thanks and Acknowledgements eResearch community –NCeSS University of Leeds –School of Geography –Centre for Computational Geography EC/ESRC/JISC Organisers You