Social science seeks to understand and predict patterns involving human behaviour, many of which are large-scale and complex. But social science explanations or predictions can be difficult to test and refine because of the serious ethical and practical barriers to controlling, manipulating and replicating conditions within experiments. For example, there are many theories behind some of the complex patterns of urban mobility, but when traffic calming measures fail to produce the desired results it can be difficult to identify why or how the situation can be improved.
One possible solution is to run social science experiments in silico, with simulated actors whose features, behaviours and actions are informed by real world data. This allows social scientists to test and refine their understanding of how an observed pattern can be recreated. Computational social science experiments also allow researchers to explore how emergent patterns might change under experimental, or even counter-factual, conditions.
This webinar:
- introduces the important concepts of emergent patterns, bottom-up processes, and other theoretical ideas underpinning agent-based modelling
- presents several examples of agent-based models
- discusses the pros and cons of agent-based models
- presents several software options for agent-based modelling and where to get more information
This webinar:
- introduces the important concepts downloading, cleaning and preparing shapefiles and other data files for importing into an existing agent-based model
- presents an extensive exploration of how a commuting model differs when based on random data or imported real world data
- discusses some problems and limitations of using real world data in agent-based models
- presents links so that users can access and use the model data presented in the webinar
3. Conducting experiments, recording output and analysing results of agent-based modelling for social scientists
This webinar:
- how to conduct parameter sweeps for model testing in NetLogo
- how to automate the process of computational experiments (also in NetLogo)
- two different methods of exporting experimental data to saved files for further analysis
- briefly displays what exported experimental data looks like and how it might be analysed to support experimental conclusions
The webinars are published on the UK Data Service Youtube channel: https://www.youtube.com/user/UKDATASERVICE/ The models demonstrated by Kavin Narasimhan in the guest lecture (March 2022) can be found here:
The slides and other resources (e.g., reading lists) can be found in the webinars folder.