In all five project areas: simulations, data analysis, cognitive experiments, computations, and statistical analysis, the project made progress beyond the state of the art. Simulation modelling and the analysis of model results confirmed that while migration processes are complex and uncertain, information flows mattered a lot for model outcomes, with trade-offs between theoretical insights from models and their empirical alignment.
The analysis of secondary sources enabled separating the bias and variance in available data. The ethnographic study corroborated other findings, and identified different forms of capital, information and chance as key factors shaping the refugees’ journeys. One surprising finding was related to gender, with women travelling on their own, contrary to popular perceptions.
The results of cognitive experiments indicated that the migration decision-making patterns are consistent with the prospect theory, with loss and risk aversion, while information from official organisations and those with previous experience were most influential on migration decisions. The subsequent research on immersive experiments additionally pointed out to the importance of simple design to make the results interpretable.
For computations, we found trade-offs between speed, productivity, expressiveness and ease of development. We defined formal semantics that meets the modelling requirements, based on precisely defined stochastic processes. The language development offered solutions with adapted rule-based syntax and separation of different concerns (e.g. model logic or implementation) in an internal domain-specific language. Formal description of the model provenance allowed documenting, querying and visualising the model-building process and its different elements.
The project results confirm key limitations of existing social simulation approaches, which do not make full use of the advances in different disciplines. We have demonstrated the usefulness of a unified, formal and iterative model-building process, following the inductive principles. Adding detail increases the model complexity and uncertainty, but also our understanding of social processes, for which uncertainty and complexity are key features. Overreliance on data, while reducing uncertainty, risks losing the richness of the description.
With the caveats about high uncertainty, the models also enabled answering substantive questions on migration processes and the impact of policies and interventions, at least in qualitative terms. In conclusion, the complexity and uncertainty of migration should not be underestimated or, worse, ignored. Such results call for humility in the face of complex processes that result from the agency of thousands of actors exercising their free will based on the best information they possess and exchange through their social networks.
The results confirmed large unused potential of social simulation studies. We hope that the project will open up exciting research possibilities beyond population studies, both methodological and substantive, and will generate academic and practical impact, offering methodological advice for policy-relevant simulations (see book review:
https://tinyurl.com/BAPS-review(opens in new window)).
