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 narcotrafficking 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.
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This content will become publicly available on May 1, 2024
Predicting Wildlife Trafficking Routes with Differentiable Shortest Paths
Wildlife trafficking (WT), the illegal trade of wild fauna,
flora, and their parts, directly threatens biodiversity and conservation
of trafficked species, while also negatively impacting human health,
national security, and economic development. Wildlife traffickers obfuscate their activities in plain sight, leveraging legal, large, and globally
linked transportation networks. To complicate matters, defensive interdiction resources are limited, datasets are fragmented and rarely interoperable, and interventions like setting checkpoints place a burden on
legal transportation. As a result, interpretable predictions of which routes
wildlife traffickers are likely to take can help target defensive efforts and
understand what wildlife traffickers may be considering when selecting
routes. We propose a data-driven model for predicting trafficking routes on
the global commercial flight network, a transportation network for which
we have some historical seizure data and a specification of the possible
routes that traffickers may take. While seizure data has limitations such
as data bias and dependence on the deployed defensive resources, this is a
first step towards predicting wildlife trafficking routes on real-world data.
Our seizure data documents the planned commercial flight itinerary of
trafficked and successfully interdicted wildlife. We aim to provide predictions of highly-trafficked flight paths for known origin-destination pairs
with plausible explanations that illuminate how traffickers make decisions
based on the presence of criminal actors, markets, and resilience systems.
We propose a model that first predicts likelihoods of which commercial
flights will be taken out of a given airport given input features, and then
subsequently finds the highest-likelihood flight path from origin to destination using a differentiable shortest path solver, allowing us to automatically align our model’s loss with the overall goal of correctly predicting the
full flight itinerary from a given source to a destination. We evaluate the
proposed model’s predictions and interpretations both quantitatively and
qualitatively, showing that the predicted paths are aligned with observed
held-out seizures, and can be interpreted by policy-makers
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- NSF-PAR ID:
- 10442320
- Editor(s):
- Cire, A.A.
- Date Published:
- Journal Name:
- Part of the Lecture Notes in Computer Science book series (LNCS,volume 13884)
- Page Range / eLocation ID:
- 460–476
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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