Prosecution of complex criminal networks: a multilevel ERGMs approach to CICIG's judicial cases

TL;DR

This paper analyzes how prosecutors' discretionary decisions in CICIG's cases influence criminal network dismantling, using a multilevel ERGM approach to assess deterrence effectiveness and operational strategies.

Contribution

It introduces a multilevel ERGM methodology to study prosecutors' decision-making processes and their impact on disrupting criminal networks in Guatemala.

Findings

Network configurations align with deterrence theories.

Punishment severity disrupts criminal ties effectively.

Prosecutorial strategies should integrate data-driven approaches.

Abstract

Prosecutors are essential in combating organized crime, making key decisions about prosecution, target selection, and structuring imputation strategies. Despite their importance, the configuration of these strategies remains empirically underexplored. This study engages with that premise by considering the cases investigated by the International Commission Against Impunity in Guatemala (CICIG) and focusing specifically on the role of prosecutors, aiming to uncover how their discretionary decisions translated the CICIG mandate into operational practices intended to achieve systemic deterrence, and to what extent, can we talk about deterrence effectiveness. The research employs a multilevel Exponential Random Graph Model (ERGM) analysis, integrating three networks: the criminal network of actors involved in illegal activities, the legal framework network that represents offenses, and the prosecution network that connects actors to offenses. The model assesses whether the observed network aligns with deterrence-based theoretical assumptions and examines how punishment severity can be effective when it disrupts functional ties that sustain criminal activity -both through long-term sanctions and by increasing the perceived threat of punishment among co-offenders. This approach underscores the need for prosecutorial strategies to evolve beyond a case-by-case model toward a multi-case, multi-offender imputation framework that fully integrates intelligence and data-driven analysis to dismantle criminal networks.