Year: 2020
Type: article
Abstract: According to United Nations, corruption is a systemic and adaptive phenomenon that requires comprehensive and multidisciplinary approaches for its effective prevention and combat. However, traditional approaches lack the analytical tools to handle the structural and dynamical aspects that characterize modern social, political and technological systems where corruption takes place. On this matter, complex systems science has emerged as a comprehensive framework to study highly adaptive phenomena from natural to socio-technical settings. Thus, in this article we present an empirical approach to model corruption using the concepts and tools of complexity science, mainly, complex networks science. Under this framework, we describe a major corruption scandal that took place in Mexico involving a network of hundreds of shell companies used to embezzle billions of dollars. We describe the structure and dynamics of this corporate network using available information related to their personnel and the date of the companies’ creation. We measured some global parameters, such as density, diameter, average path length, and average degree in order to provide systematic evidence on which corporate characteristics are likely to signal corruption. Moreover, this analysis also provides an objective perspective of the systemic nature of events where companies are abused for corrupt purposes, and the shortcomings of reductionistic analyses. Major corruption scandals comprise both legal and illegal deeds, in addition to several parties acting simultaneously over extended time periods. As a whole, such scandals pose enormous challenges for the study of law and put the legal design of administrative and criminal controls to the test.
Source: Applied Network Science
Authors: Issa Luna-Pla, José R. Nicolás-Carlock
Institution: Universidad Nacional Autónoma de México
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,290
Award: G2688297284
OpenAlex ID: W3013733130