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The Elements of a Computational Infrastructure for Social Simulation

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

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The Elements of a Computational Infrastructure for Social Simulation

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  1. The Elements of a Computational Infrastructure for Social Simulation Mark Birkin1, Rob Allan2, Sean Beckhofer3, Iain Buchan4, June Finch5, Carole Goble3, Andy Hudson-Smith6, Paul Lambert7, Rob Procter5, David de Roure8, Richard Sinnott9 [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. 2031 2001 Transport… 2015 Traffic Intensity * * Traffic Intensity=Traffic load/Road capacity

  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

  10. NeISS Architecture

  11. NeISS Architecture

  12. NeISS Architecture

  13. NeISS Architecture

  14. NeISS Portal

  15. NeISS Portal

  16. 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

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