Multi-Objective Calibration For Agent-Based Models

Agent-based modelling is already proving to be an immensely useful tool for scientific and industrial modelling applications. Whilst the building of such models will always be something between an art and a science, once a detailed model has been built, the process of parameter calibration should be performed as precisely as possible. This task is often made difficult by the proliferation of model parameters with non-linear interactions. In addition to this, these models generate a large number of outputs, and their ‘accuracy’ can be measured by many different, often conflicting, criteria. In this paper we demonstrate the use of multi-objective optimisation tools to calibrate just such an agent-based model. We use an agent-based model of a financial market as an exemplar and calibrate the model using a multi-objective genetic algorithm. The technique is automated and requires no explicit weighting of criteria prior to calibration. The final choice of parameter set can be made after calibration with the additional input of the domain expert