Coupling agent-based simulation and spatial optimization models to understand spatially complex and co-evolutionary behavior of cocaine trafficking networks and counterdrug interdiction.

Keywords: Spatial Optimization, Agent Based Modeling, Central America, Complex Adaptive Systems
Abstract Type: Virtual Paper Abstract

Abstract

Despite more than 40 years of counterdrug interdiction efforts in the Western Hemisphere, cocaine trafficking, or ‘narco-trafficking’, networks continue to evolve and increase their global reach. Counterdrug interdiction continues to fall short of performance targets due to the adaptability of narco-trafficking networks and spatially complex constraints on interdiction operations (e.g., resources, jurisdictional). Due to these dynamics, current modeling approaches offer limited strategic insights into time-varying, spatially optimal allocation of counterdrug interdiction assets. This study presents coupled agent-based and spatial optimization models to investigate the co-evolution of counterdrug interdiction deployment and narco-trafficking networks’ adaptive responses. Increased spatially optimized interdiction assets were found to increase seizure volumes. However, the value per seized shipment concurrently decreased and the number of active nodes increased or was unchanged. Narco-trafficking networks adaptively responded to increased interdiction pressure by spatially diversifying routes and dispersing shipment volumes. Thus, increased interdiction pressure had the unintended effect of expanding the spatial footprint of narco-trafficking networks. This coupled modeling approach enabled the study of narco-trafficking network evolution while being subjected to varying interdiction pressure as a spatially complex adaptive system. Capturing such co-evolution dynamics is essential for simulating traffickers’ realistic adaptive responses to a wide range of interdiction scenarios.