The course covers a range of conceptual and practical aspects of modelling and is designed around a practical tutorial which allows students to learn NetLogo through programming a real social science agent-based model from scratch.
Indicative content includes:
What is agent-based modelling
Basics of agent-based model implementations
Approaches to behaviour rules (eg. game theory, BDI, social psychology)
Running and analysing experiments
Sensitivity analysis and robustness tests
Verification and validation
On successful completion of this course, particpants will be able to:
Understand the foundations of agent-based modelling (K)
Understand application areas of agent-based modelling (C,K)
Understand different implementations of social phenomena (C,K)
Be able to program in NetLogo (K,T,P)
Be able to provide a basic model specification and a basic implementation (P)
Key: C-Cognitive/Analytical; K-Subject Knowledge; T-Transferable Skills; P- Professional/ Practical skills
Dr Corinna Elsenbroich is computational social scientist. Her main research interests are in methods development, in particular methods for complexity social science and methodological and epistemological aspects of agent-based modelling and social simulation. She is interested in understanding decision mechanisms, in particular collective decision-making and context dependency of decisions.
Dr Jennifer Badham 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. This brings together aspects of social simulation, social network analysis and social psychology. She is currently working at Queen’s University Belfast modelling health behaviour interventions over social networks.