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Enhancing Social Simulation Infrastructure: NeISS Architecture

Explore the essential elements and features of a computational infrastructure for social simulation, including social science applications, methods, and the rationale for the NeISS project. Dive into the NeISS architecture and its potential impact on research and policy communities.

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Enhancing Social Simulation Infrastructure: NeISS Architecture

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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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