Computational modelling for resilient societies

Disasters and crises are complex and they usually entail multiple dimensions (human, infrastructural, environmental, and economic) that are difficult to disentangle. At the same time, actors' behaviors are determined by multiple and changing factors, from individual traits to social networks, thus making it impossible to reduce disasters and crises to linear processes. 

This research group aims to bring together a range of experts from different disciplines who share an interest in complexity approaches and want to collaborate on:

• the development of interdisciplinary computational models in order to explore/test under which conditions a given system is resilient to crises and disasters

• the development of interdisciplinary computational models of different kinds of crises (disasters, conflicts, pandemic) in order to overcome the siloed approach to disaster research

• the integration of computational models with empirical data in order to test insights coming from the models and calibrate the models with real data from alternative sources such as in-situ observations, as well as satellite and citizen science data

• the establishment of collaborations with different actors (e.g., policy/decision makers, citizens, and emergency responders) in order to co-create transdisciplinary approaches. Computational models are especially useful when co-developed with end-users and stakeholders, and they allow not only to inform decision making, but also to co-create trainings and other tools for improving resilience.