Agent-Based Modelling for the Social Scientist – A Practical Guide to Model Building and NetLogo
Monday 19th June 2023 – Wednesday 21st June 2023 (three-day course), 09:00 – 17:00, Durham University
Tutors: Dr Corinna Elsenbroich and Dr Jennifer Badham
Computational methods have revolutionised the sciences, including the social sciences. By being able to investigate dynamics in silico, model complex, interdependent systems and experiment with different hypotheses, computer modelling has become an important social science research tool.
Agent-based modelling is a computational simulation method for understanding complex systems. An agent-based model is a computer model simulating the interrelationships and interactions of components of a system over time. These models are particularly good at modelling the heterogeneity and interaction of a population, tackling emergence and feedback loops and dealing with non-linear dynamic relationships.
This course will guide you through the research process of agent-based modelling in the social sciences: formulating a research question, specifying a model, creating a simulation and interpreting the output. During the course you will build a model that includes personal, social and environmental factors using NetLogo, acquiring basic and intermediate programming skills.
This is a three-day, in-person course and delegates are required to attend all three days.
The syllabus includes:
- conceptualising agent-based models
- operationalising and calibrating from data
- experimenting and analysing
- interpreting models
- verifying and validating
Each step of the research process will be complemented by:
- Hands-on sessions of model building in NetLogo, a widely used and powerful language for social science modelling. The sessions are designed in such a way that you will understand the structure of a model and learn to write the program code yourself.
- Model development sessions. These sessions will facilitate the development of a model relevant to your research, from conception through specification to first steps of implementation.
At the end of this course you will be able to see the world through modeller’s eyes and start programming your own agent-based models.
Policy analysts, commissioners of evaluation, professional evaluators, social science postgraduate students and researchers.
Dr Jennifer Badham is Assistant Professor in Social Data Science in the Department of Sociology at Durham University. She originally trained as a mathematician and developed an interest in applying mathematical modelling methods to social policy while working for government and nongovernment health organisations in Australia. Her main research interest concerns the way that social structures affect transmission – of disease, information, beliefs and behaviour.
Dr Corinna Elsenbroich is a Reader in Computational Modelling at the MRC/CSO Social and Public Health Sciences Unit at the University of Glasgow. She is also a member of the Centre for the Evaluation of Complexity Across the Nexus (CECAN) and a Non-executive Director of CECAN Ltd. Corinna is a complexity social scientist interested in developing methods to better understand the social world. She is in particular interested in complex causality, epistemological questions of simulation modelling and developing complexity methods in co-production with stakeholders.
- Government / commercial sector: £1,000 + VAT
- Staff from educational or charitable institutions: £600 + VAT
- Students (including postgraduate researchers): £300 + VAT
The above prices are for one three-day ticket.
How to Book:
Reserve your place by registering and paying via our Eventbrite page. Payment can be made by credit/debit card, Paypal or by requesting an invoice. Booking terms and conditions are also available on the booking site.
If you have any questions, please email the CECAN Ltd training team.
CECAN Ltd is the commercial arm of the ESRC funded Centre for Evaluating Complexity across the Nexus (CECAN). It offers access to innovative policy evaluation approaches and methods to support decision makers.