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

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.

Keywords:

• Central America
• complex adaptive systems
• operational environment
• spatial dynamics

Data availability statement

The data that support the findings of this study are openly available through the Laboratory for Human-Environment Interactions Modeling and Analysis (HEIMA) data repository at https://heima.ua.edu/data.html.

Additional information

Funding

This research was supported by the U.S. National Science Foundation (NSF) EAGER ISN #1837698 and NSF D-ISN: #2039975.

Notes on contributors

Nicholas R. Magliocca

Nicholas R. Magliocca received his PhD in the Department of Geography at the University of Maryland, Baltimore County. He is currently an Associate Professor in the Department of Geography at the University of Alabama. His research interests include social-ecological systems, agent-based modeling, and geospatial analysis.

Ashleigh N. Price

Ashleigh N. Price received her MS in Geography from the University of Southern Mississippi. She is currently a PhD candidate in the Department of Geography at the University of Alabama. Her research interests are in crime geography, hazard vulnerability & resilience, and spatial optimization.

Penelope C. Mitchell

Penelope C. Mitchell received her MS in Geography from the University of Alabama. She is currently a PhD candidate in the Department of Geography at the University of Alabama. Her research interests are geographic information systems, spatial analysis, and spatial optimization methods in the context of health and behavioral geography.

Kevin M. Curtin

Kevin M. Curtin received his PhD in Geography from the University of California, Santa Barbara. He is currently a Professor in the Department of Geography at the University of Alabama. He performs primary research in the field of Geographic Information Science with specializations in location science, transportation and logistics, urban resource allocation, spatial statistics, and network GIS.

Matthew Hudnall

Matthew Hudnall received his PhD in Computer Science at the University of Alabama. He is currently an Assistant Professor in Management Information Systems in the Department of Information Systems, Statistics, and Management Science at the University of Alabama. Dr. Hudnall is also the Deputy Director of the Institute of Data & Analytics. His research interests include data security, big-data analytics, cybersecurity, and leveraging software systems to improve society.

Kendra McSweeney

Kendra McSweeney received her PhD in Geography from McGill University. She is currently a Professor of in the Department of Geography at the Ohio State University. Her research interests are in human-forest interactions and uses of political ecology approaches to study them.