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Social Simulation Tutorial

International Symposium on Grid Computing

Taipei, Taiwan, 7th March 2010

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AgendaTime Session Speaker

09:00 Registration

09:30 Welcome, What is social simulation? What is Agent-Based Modelling (ABM)?

Alex Voss

10:00 The RePast Simphony Toolkit, an example model Alex Voss

10:30 Coffee break

11:00 Practical I: installing RePast and running model Alex Voss

12:00 Population reconstruction Andy Turner

12:30 Lunch

14:00 Infrastructures for social simulation Rob Procter

14:30 Introduction to grids and clouds

15:00 Coffee break

15:30 Practical II: running model ensembles on grids and clouds Alex Voss

16:45 Closing Remarks and feeback Alex Voss

17:00 End

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The Team

• Alex VossSchool of Computer ScienceUniversity of St Andrews

• Andy TurnerSchool of GeographyUniversity of Leeds

• Rob ProcterManchester e-Research CentreUniversity of Manchester

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Aims of the Tutorial

• To provide a brief introduction to agent-based simulation, population reconstruction and RePast Simphony.

• To motivate the use of grids and clouds for running social simulation ensembles

• To demonstrate use of grids and clouds and provide practical instructions for running your own models

• Not to replace RePast tutorial!

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Practicalities

• Using your own laptop

• Participant sheet with login

• URL for instructions: https://e-research.cs.st-andrews.ac.uk/SimISGC2010

• Download software and certificates from local server (see URL above) – also USB sticks to pass around

• Using GILDA training infrastructure

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PART I: SOCIAL SIMULATION ETC. IN A NUTSHELL

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Background

• Much social science does not use advanced ICT but emergence of new analytical methods is driven by:

– Increased availability of data about social phenomena

– But data is ‘messy’, anonymised, aggregated, incomplete

– Challenges to analyse social phenomena at scale

– Challenges to inform practical policy and decision making (e.g., evidence-based policy making)

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What is Social Simulation?

• A new approach to modeling social phenomena

• Based on empirical data

• Based on existing theories

• A new way to explore them, complementing other forms of modelling and prediction

• Used to understand and predict

• Not just one form of simulation: systems dynamics, microsimulation, queueing models, etc.

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What is Social Simulation? (II)

• Models necessarily incomplete

• There can always be more detail– Higher spatial and temporal resolution

– More and more detailed attributes

– Geography and social science is no different to any other type of science in this respect

• Need to assess impact of decision about how to model

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Simulation as a Method

ModelModel Simulated Data

Simulated Data

TargetTarget

Collected Data

Collected Data

Adapted from Gilbert & Troitzsch, p. 17

Abstraction

Simulation

Data Gatheringor Re-Use

Validation

PopulationReconstruction

ModelModel

Implementation and Verification

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What is Agent-Based Modelling?

• Simulating interactions between dynamic populations in changing environment

• Heterogeneous populations – each individual has specific attributes such as age, gender, socio-economic status, health, etc.

• Stochastic process – each run can differ from previous

• Notion of emergence – larger-scale phenomena produced through many small interactions / events

• Sets of simple rules produce complex behaviour – sets can be large…

• Can help model and analyse phenomena too complex for closed form, can be used in absence of knowledge about causality

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ABM Frameworks

• Rapid growth over last 10 years

• Many implementations

• General frameworks (open source):– Swarm

– MASON

– NetLogo

– Repast

– Repast Simphony

• Aim to separate concerns clearly to maximise modeling capability

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ABM Conceptual Components

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Building Simulation Models

• Model Design – about the choices made, cf. research design

• Model implementation – cf. research method (‘the logistics’, ‘plumbing’)

• Verification – checking the implementation matches the design

• Validation – checking the design represents the aspects of the world modeled

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Building Simulation Models (II)

• Simple models can be built using graphical editors (cf. RePast tutorial)

• More complex models and behaviours inevitably require programming

• Data management etc. become important as size grows

• Calls for interdisciplinary collaboration, division of labour see above

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Trust in Models

• Scientific codes often many years old and carefully maintained

• Social simulation is in its infancy – relatively speaking

• Need to build community development approaches that produce robust, reliable code

• Yet need for flexibility and adaptability – research is about doing things not done before…

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Social Simulation: Reading

• Nigel Gilbert and Klaus G. Troitzsch: Simulation for the Social Scientist(cress.soc.surrey.ac.uk/s4ss/)

• Joshua M. EpsteinGenerative Social Science:Studies in Agent-Based Computational Modeling(http://press.princeton.edu/titles/8277.html)