Crime takes a heavy toll on society and there is still little consensus on how to fight these phenomena most effectively. Yet the dynamics in criminal networks show many characteristics of complex adaptive systems, such as self-organisation and extreme resilience. Drawing on expertise from a range of disciplines, from criminology and law to sociology, computational science and artificial intelligence, we can begin to understand the patterns behind criminal activity by disentangling the complexity of this resilient ‘system’.
The goal of this research programme is to develop computational models that will allow anticipating criminal activities and to assess the effect of possible intervention against criminal activity strategies as well to explore potential alternative interventions.
Modelling opponent emergent behaviour (MOEB)
This joint research project at IAS and TNO aims to identify and understand the underlying processes that yield the adaptivity and resilience of insurgent and terrorist organisations. We observe and try to understand the emergent patterns of growth, decline, splits and mergers between insurgents and terrorists. The organisations can be described and analysed as complex, self-organized structures that inhabit social interactions, resource constraints and cognitive processes. To grasp this self-organizing process, we focus on various research lines with the application of quantitative methodologies that aim to develop more realistic models.
This project aims to uncover the dynamics of criminal network adaptation. Understanding these complex dynamics helps us better understand the effectiveness of different criminal network disruption strategies. The team aims to model the behaviour of agents before and after arrests of their co-conspirators. These models are derived from a unique set of real time law enforcement data and then applied to simulate their emergent behaviour. State-of-the-art computational analysis methods are developed in support of this project to gain insights how the complex dynamics occurred. The computational models and analysis techniques combined will allow for increased effectiveness of contemporary law enforcement strategies. In the end visualisation methods will be developed to bridge the gap from theory to practice, and jointly develop novel strategies on the research outcome.
This project focuses on combatting crimes that undermine the rule of law in a smart and comprehensive manner, in a financial public-private partnership and through artificial intelligence with a focus on human trafficking, money laundering and corruption. New (financial) data sources are explored to detect crimes, analyse patterns, discern networks and model interventions. The COMCRIM consortium includes 4 banks and 9 public sector organisations.
Research Group Members
Faculty of Law
Faculty of Law
Faculty of Science
Affiliated External Researchers
Dr. Ana Isabel Barros
Principal researcher, TNO
Dr. Paul Duijn
Strategic Intelligence Analyst, Fiscal Intelligence and Investigations Service (FIOD)