1. School of Geography FACULTY OF ENVIRONMENT The Elements of a Computational Infrastructure for Social Simulation Mark Birkin 1, Rob Allan 2, Sean Beckhofer 3, Iain Buchan 4, June Finch 5, Carole Goble 3, Andy Hudson-Smith 6, Paul Lambert 7, Rob Procter 5, David de Roure 8, Richard Sinnott 9 [1] School of Geography, University of Leeds [2] STFC, Daresbury [3] School of Computer Science, University of Manchester [4] School of Medicine, University of Manchester [5] School of Social Sciences, University of Manchester [6] Centre for Applied Spatial Analysis, UCL [7] Applied Social Science, University of Stirling [8] Electronics and Computer Science, University of Southampton [9] NeSC, University of Glasgow 6649386

2. Simulation of Epidemics Ferguson et al, Nature, 2006

3. The El Farol Bar Problem  Everyone wants to go the bar -unless it’s too crowded!  Must relax neoclassical economic assumptions (homogeneity of preferences, simultaneous decision- making)  Individual actors/ agent-based decision-making -generic template for real markets heterogeneous out of equilibrium (Arthur, 1994)

4. Public Policy Source: MAPS2030

5. 2001 2031 2015 * Traffic Intensity=Traffic load/Road capacity Traffic Intensity * Transport…

6. Social Simulation  Applications  Economics, geography, sociology  Health sciences, politics, anthropology  Methods  Agent-based models  Microsimulation  Impact  Theory to policy  Analysis, projection, forecasting, scenarios

7. Features of social simulation  Widespread data requirements  Plug-and-play simulation and analysis components  Visualise complex outcomes  Computationally demanding  Need to reproduce and share results with a community of users

8. Rationale for NeISS  Growing demand for social simulation models  Critical mass in NCeSS  International collaboration with solid foundations  Ongoing innovation  Leverage existing investments in computation and data

9. NeISS Architecture

14. NeISS Portal

16. School of Geography FACULTY OF ENVIRONMENT

17. Conclusion NeISS will:  Combine research lifecycle elements within a unified social simulation infrastructure  Leverage skills and relationships from the UK e- social science programme (NCeSS)  Build user communities in both public policy and academia