- About us
Within the Computational Criminology Cluster the focus lies on the use of computational models for investigating criminal behaviour. Two approaches can be distinguished, namely knowledge-driven (top down) and data-driven (bottom-up) methods.
Knowledge-driven methods take existing knowledge from behavioural sciences as input and try to formalise this knowledge in dynamic computational models. Using the appropriate software these models can be used to simulate human behaviour: virtual scenarios that (dynamically) simulate behaviour over time. These types of computer simulations offer opportunities to develop innovative methods and tools to better understand, predict and possibly even prevent deviant behaviour. Examples are predicting the dynamics of crowds, simulating the behaviour of robbers or simulating aggression management strategies for training purposes in public transport.
Data-driven methods take a large amount of existing empirical data as input and try to automatically (without interference of a human user) detect patterns. Since computers have much more computational power than humans, this approach offers high potential to discover new insights and develop new theories. Examples of this approach are detecting patterns in messages on social media, or finding correlations between some risk factors and deviant behaviour.
Cluster members collaborate intensively with the Artificial Intelligence department at the VU University Amsterdam. Due to the immediate practical significance of the interdisciplinary research conducted in this cluster, many partnerships exist with societal partners (Netherlands Police Academy, GVB, G4S) and the Ministry of Security and Justice.
Tibor Bosse (Radboud University)
Nick Malleson (University of Leeds)
Jean-Louis van Gelder (Twente University)
Cluster members collaborate with:
Griffith University (dr. Daniel Birks en dr. Michael Townsley )
Temple University (dr. Elizabeth Groff)
Danube University Krems (dr. Thomas Lampoltshammer)
University of Cincinnati (dr. Lin Liu)