Agent-Based Modelling (ABM) has long been applied to the study of complex phenomena. It has become increasingly popular across many scientific fields including the study of biological, ecological and agricultural systems. Agent-Based models provide a rich environment for the experimentation and analysis of these systems at different levels of complexity by conceptually breaking them down into individual interacting components. However, the likelihoods, the functions that describe the probability of the observed data given parameter values, for these models are not analytically or computationally tractable. This makes statistical inference for these models challenging. This session aims to highlight the advances in ABM approaches applied to the development of simulation systems in biology, ecology and agriculture. Statistical inferential methods for ABM will also be discussed. Submissions addressing the scientific challenges of modelling these systems using the ABM paradigm and novel approaches for dealing with statistical inference of these models are especially welcomed. Areas of interest, not necessarily exhaustive, include geospatial ABMs, hybrid models, model validation and verification, integration with commercial off-the-shelf or open source Geographical Information Systems, optimisation techniques, ABM-enhanced Decision Support Systems, mobile applications, likelihood-free or simulation-based inferential methods for ABMs such as Generalized Bayesian inference, Approximate Bayesian Computation, Bayesian indirect inference integration with machine learning (ML) and active learning techniques.
Key topics: Agent-based modelling, Individual-based modelling, Simulation-based inference, Optimisation techniques