Abstract
Corruption, and more specifically corruption in Latin America, is a complex phenomenon that is affected by politics, social structures, and institutions, as well as individual behaviors. The Lava Jato scandal was a large-scale example of corruption in Brazil. Advances in data analysis, computation, and social networks have allowed progress to be made with these types of investigations. The Lava Jato case has been a clear example of how breaking up social networks and understanding the extent of crime and individual corruption have revealed webs of corruption that have influenced politics, as well as hindered economic development in Brazil. The several layers of interactions between individuals and institutions can be difficult to grasp and understanding the patterns and relationships within complex large-scale phenomena such as corruption can seem impossible. Agent-based models can help with understanding these complex behaviors and systems. By capturing the patterns and gaining a better understanding of how corruption emerges and is manifested, we can help inform policy, as well as create better tools and methods for crime prevention and detection.
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Al-Khulaidy, A., Vergara, V. (2021). Corruption and the Effects of Influence Within Social Networks: An Agent-Based Model of the “Lava Jato” Scandal. In: Yang, Z., von Briesen, E. (eds) Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. CSSSA 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-77517-9_2
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