Rigour and transparency can be considered as basic scientific principles that need to be considered in all scientific disciplines. When it comes to the application of computational methods, they facilitate the means to ensure reproducibility of the simulation model and experiment. Efforts in this manner have already been made by fostering objectivity through standardization, e.g. in form of the ODD-protocol (Grimm et al. 2006) or the systematic design of experiments (DOE) (Lorscheid et al. 2012). However, little attention has been paid to informing about the data used in models. Often a model is broadly explained, but justification in terms of decisions about what data has been used, how it has been used, and why the modeller has decided to use it in this way, is most often missing. This can be very frustrating, making it difficult to understand and perhaps replicate the model. Problems can especially arise for data from different sources or of different character (e.g. quantitative data about population distributions and qualitative data on agent behavioural rules). Therefore, we identify the need to talk about more standardization in terms of justification, specification, and documentation when it comes to data integration in agent-based models. This topic was raised and discussed during the NIAS-Lorentz Centre workshop “Integrating Qualitative and Quantitative Evidence Using Social Simulation” (8-12.04.2019). A group tackling this challenge was formed during this workshop and it started designing RAT, i.e. a protocol/framework for augmenting rigour and transparency in agent-based modelling.
Round table hosts:
- Sebastian Achter, Institute of Management Accounting and Simulation, Hamburg University of Technology, Germany
- Melania Borit, Norwegian College of Fishery Science, UiT - The Arctic University of Norway
- Edmund Chattoe-Brown, School of Media, Communication and Sociology, University of Leicester, UK
- Peer-Olaf Siebers, School of Computer Science, University of Nottingham, UK