Webinar: Introduction to agent-based modelling for social scientists
Date:
16/01/2020
Organised by:
UK Data Service
Presenter:
Julia Kasmire
Level:
Entry (no or almost no prior knowledge)
Contact:
Venue: Online
Description:
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 free webinar, organised by the UK Data Service, is the first in a series of three on how to use agent-based models and real world data to run computational social science experiments using the example of urban mobility. Specifically, 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
Cost:
Free
Website and registration:
https://www.ukdataservice.ac.uk/news-and-events/eventsitem/?id=5558
Region:
International
Keywords:
Quantitative Data Handling and Data Analysis
Related publications and presentations:
Quantitative Data Handling and Data Analysis