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
School of Geography FACULTY OF ENVIRONMENT The Elements of a Computational Infrastructure for Social Simulation Mark Birkin 1, Rob Allan 2, Sean Beckhofer.
Jan 01, 2016
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School of GeographyFACULTY OF ENVIRONMENT
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
Simulation of Epidemics
Ferguson et al, Nature, 2006
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)
Public Policy
Source: MAPS2030
2001 2031
2015
* Traffic Intensity=Traffic load/Road capacity
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Traffic Intensity *
Transport…
Social Simulation
Applications
Economics, geography, sociology
Health sciences, politics, anthropology
Methods
Agent-based models
Microsimulation
Impact
Theory to policy
Analysis, projection, forecasting, scenarios
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
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
NeISS Architecture
NeISS Architecture
NeISS Architecture
NeISS Architecture
NeISS Portal
NeISS Portal
School of GeographyFACULTY OF ENVIRONMENT
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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