This training session is delivered by Dr Nicolas Payette from the Oxford Research Software Engineering Group.
Whether it's ant colonies, traffic jams, fisheries, predator-prey interactions, segregation patterns in urban areas, or viruses spreading through populations, we are surrounded by complex systems. Those have lots of different parts that interact in non-linear ways, giving rise to patterns that are difficult to predict by looking at individual components in isolation. And when these components are agents that can adapt and learn, it gets even harder.
Agent-based models (ABMs) are one way of looking at these systems. By explicitly representing agents, their behaviours and interactions, and using simulations to work out the consequences of these mechanisms, ABMs can provide candidate explanations for the observed patterns.
In this introductory course, we will look at where ABMs come from, how they work, and what they are good for. We will learn how to build a simple model using NetLogo, a programming environment specialised in agent-based modelling. We will also see how to estimate the parameters of a model using empirical data and, once we have a calibrated model, how to use it for policy optimisation.
Some familiarity with computer programming is desirable, but no prior experience with NetLogo is expected.
Places will be allocated on a first-come, first-served basis, and once places are full, we will maintain a waiting list.
Please only register if you are certain of your availability and commitment to attend.
Booking will open in early December
