Teaching Agent-Based Modelling and Machine Learning in an integrated way

P.W.M. Augustijn, O. Kounadi, Tatjana Kuznecova, R. Zurita-Milla

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The integration of Agent-Based Modelling (ABM) and Machine Learning (ML) provides many promising opportunities, yet this research field is underdeveloped. Different reasons are given for this lack of integration, including a shortage of behavioural data and technical implementation difficulties. However, we think that one crucial problem is being overlooked. In our educational system, we teach topics one by one and do not explicitly focus on the integration of various modelling paradigms. This is a missed opportunity that should be addressed, to prepare our students for a world where models are increasingly complex and where data and model integration becomes inevitable. In this paper, we share our experiences in a course in Geoinformatics, where integrated ABM and ML modelling is central. In our class, we use the Living Textbook to work on interlinked concept maps, and we have an overarching case study assignment. Preliminary outcomes show that students’ learning and project work could benefit from simplifying the case study assignment and introducing the parallel teaching of ABM and ML. In general, different teaching methods and setups still need to be explored, to ensure that our future model designers are well equipped for their task.
Original languageEnglish
Number of pages7
Publication statusPublished - 16 Sept 2019
Event15th Geocomputation 2019: Adventures in GeoComputation - Rydges Lakeland Resort Hotel , Queenstown, New Zealand
Duration: 18 Sept 201921 Sept 2019
Conference number: 15


Conference15th Geocomputation 2019
Country/TerritoryNew Zealand


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