Agent-based modelling to explore the co-evolution of social-ecological systems
Agent-based modelling is a computational method that enables a researcher to create, analyse and experiment with models composed out of agents that interact within an environment. Agent-based models (ABMs) are characterised by the multiple levels in which they represent a complex system. The behaviour produced by the agents on the individual level generates patterns on the macro-level, i.e., emergence, such as cooperation in a community. They are particularly suitable to study the co-evolutionary behaviour of SES because they allow for the representation of micro-scale drivers of human behaviour that determine agents’ actions in an environment characterised by social and ecological interactions as well as institutions and biophysical settings. ABMs like any model can be used for understanding, reproducing, prediction, illustration, or entertaining purposes. We primarily use them for enhancing the understanding of SES as complex systems, and for integrating interdisciplinary views, theories and data. We also use them in combination with behavioral economic experiments to generate a mechanism-based understanding of drivers of cooperation in common-pool resource use. In our team we make use of Netlogo, Repast, and MATLAB, as supporting toolkits for developing our models.