Strategic Positioning in Mafia Networks

This paper analyzes two criminal networks belonging to the \u2017Ndrangheta, a mafia-type criminal organization originating from Calabria, a Southern Italian Region. The literature on criminal networks argues that differences in the degree and betweenness centrality measures may highlight strategic positioning patterns for criminals capable of reducing risk of detection and maintaining control over the criminal activities at the same time. However, the identification of this strategic pattern is difficult whenever, as frequently happens, centrality measures are highly correlated The paper analyzes network positioning in two mafia-type organizations, where degree and betweenness centrality were highly correlated. The analysis focuses on specific characteristics of the individuals in the networks (task, hierarchy and social status within each group) and how these relate to network positioning (centrality scores and clustering coefficient) and the outcome of the criminal proceedings (accusation, arrest, conviction and sentence in months). Results show that task and hierarchy are highly associated with network centrality, but also with accusation, arrest and conviction. Contrarily, high social status within the networks shows limited association with network centrality and the outcome of criminal proceedings. This may reveal patterns of strategic positioning which could not be identified solely though network analysis measures

Punishment and penal activity : the expansion of legal fines and fees in Texas from 1985 to 2015

Across the United States, legal fines and fees generate millions of dollars per year in revenue despite widening the net of criminalization and increasing penal severity for poorer individuals. Unlike in other penal policy domains, legal fines and fees represent an ambiguously defined form of punishment that has received bipartisan support. Understudied is how legal fines and fees have become an increasingly preferred policy choice among state-level political actors. In this study, I use archival data on legal statutes and legislative sessions in Texas – home to one of the largest prison and jail system in the U.S. – to investigate the development of legal fines and fees across a 30-year period. I use insights from socio-political and legal theories to offer a comprehensive analysis of the structure of legislative policy networks and the development of legal fines and fees legislation. I demonstrate that both liberal and conservative political actors facilitated the passage of legal debt legislation. Furthermore, I consider the role of legislative testimony to show the association between testimony and the rate of legislation on legal fines and fees. I discuss the implications of my findings for understanding how policy networks and legislative activity are related to criminal justice outcomes and are influenced by a variety of social actors. My study contributes to theoretical explanations on the roles of state actors in developing policies that become increasingly implicated in social inequalities.Sociolog

Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Who's Who in Crime Network. Wanted the Key Player

Criminals are embedded in a network of relationships. Social ties among criminals are modeled by means of a graph where criminals compete for a booty and benefit from local interactions with their neighbours. Each criminal decides in a non-cooperative way how much crime effort he will exert. We show that the Nash equilibrium crime effort of each individual is proportional to his equilibrium Bonacich-centrality in the network, thus establishing a bridge to the sociology literature on social networks. We then analyze a policy that consists of finding and getting rid of the key player, that is, the criminal who, once removed, leads to the maximum reduction in aggregate crime. We provide a geometric characterization of the key player identified with an optimal inter-centrality measure, which takes into account both a player's centrality and his contribution to the centrality of the others. We also provide a geometric characterization of the key group, which generalizes the key player for a group of criminals of a given size. We finally endogeneize the crime participation decision, resulting in a key player policy, which effectiveness depends on the outside opportunities available to criminals.Social Networks; Crime; Centrality Measures; Key Group; Policies

Delinquent Networks

Delinquents are embedded in a network of relationships. Social ties among delinquents are modelled by means of a graph where delinquents compete for a booty and benefit from local interactions with their neighbors. Each delinquent decides in a non cooperative way how much delinquency effort he will exert. Using the network model developed by Ballester et al. (2006), we characterize the Nash equilibrium and derive an optimal enforcement policy, called the key-player policy, which targets the delinquent who, once removed, leads to the highest aggregate delinquency reduction. We then extend our characterization of optimal single player network removal for delinquency reduction, the key player, to optimal group removal, the key group. We also characterize and derive a policy that targets links rather than players. Finally, we endogenize the network connecting delinquents by allowing players to join the labor market instead of committing delinquent offenses. The key-player policy turns out to be much more complex since it depends on wages and on the structure of the network.Social networks, delinquency decision, key group, NP-hard problem, crime policies

Peer Effects and Social Networks in Education and Crime

This paper studies whether structural properties of friendship networks affect individual outcomes in education and crime. We first develop a model that shows that, at the Nash equilibrium, the outcome of each individual embedded in a network is proportional to her Bonacich centrality measure. This measure takes into account both direct and indirect friends of each individual but puts less weight to her distant friends. Using a very detailed dataset of adolescent friendship networks, we show that, after controlling for observable individual characteristics and unobservable network specific factors, the individual's position in a network (as measured by her Bonacich centrality) is a key determinant of her level of activity. A standard deviation increase in the Bonocich centrality increases the level of individual delinquency by 45% of one standard deviation and the pupil school performance by 34% of one standard deviation.Centrality Measure; Peer Influence; Network Structure; Delinquency; School Performance