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1. Learning Each Other’s Language and Building Trust: Community-Engaged Transdisciplinary Team Building for Research on Human Trafficking Operations and Disruption

   https://doi.org/10.1177/16094069221101966  

   Martin, Lauren; Gupta, Mahima; Maass, Kayse L.; Melander, Christina; Singerhouse, Emily; Barrick, Kelle; Samad, Tariq; Sharkey, Thomas C.; Ayler, Tonique; Forliti, Teresa; et al (January 2022, International Journal of Qualitative Methods)

   Background: Human trafficking for sexual exploitation (referred to as sex trafficking) is a complex global challenge that causes harm and violates human rights. Most research has focused on victim-level harms and experiences, with limited understanding of the networks and business functions of trafficking operations. Empirical evidence is lacking on how to disrupt trafficking operations because it is difficult to study; it is hidden and dangerous, spans academic disciplinary boundaries, and necessitates ways of knowing that include lived experience. Collaborative approaches are needed, but there is limited research on methods to best build transdisciplinary teams. Aim: The aim of this study was to understand how to form a community-engaged transdisciplinary research team that combines qualitative and operations research with a survivor-centered advisory group. Methods: We conducted a qualitative meta-study of our team that is seeking to mathematically model sex trafficking operations. Data were collected from the minutes of 16 team meetings and a survey of 13 team members. Results: Analysis of meeting minutes surfaced four themes related to content and style of communication, one related to value statements, and one capturing intentional team building efforts. Survey results highlighted respect, trust, integrity, openness and asking and answering questions as key aspects of team building. Results show that an action research approach to team building, focused on trust and communication, fostered effective collaboration among social scientists, operations researchers, and survivors of trafficking. Conclusion: Team building, shared language, and trust are essential, yet often neglected, elements of team science. This meta-study provides important methodological insights on community engaged transdisciplinary team formation to tackle vexing social challenges. 

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

   https://doi.org/10.1080/24725854.2022.2123998  

   Magliocca, Nicholas R.; Price, Ashleigh N.; Mitchell, Penelope C.; Curtin, Kevin M.; Hudnall, Matthew; McSweeney, Kendra (September 2022, IISE Transactions)

   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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3. Disrupting Labor Trafficking in the Agricultural Sector: Looking at Opportunities beyond Law Enforcement Interventions

   https://doi.org/10.1080/15564886.2022.2133036  

   Childress, Chase; Farrell, Amy; Bhimani, Shawn; Maass, Kayse Lee (April 2023, Victims & Offenders)

   Law enforcement interventions continue to be the primary mechanism used to identify offenders and illicit businesses involved in human trafficking, yet trafficking continues to be a thriving international operation. We explore alternative mechanisms to disrupt illicit operations and reduce victimization through labor trafficking supply chains using supply chain disruption theory. Using a case study approach to examine one federally prosecuted labor trafficking case in the agricultural sector, we (1) extend criminological concepts of disruption by identifying sources and methods of disruption and (2) inform criminal justice system responses by presenting novel methods of assessing effectiveness of anti-human trafficking policies and programs. 

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4. Non-traditional cyber adversaries: Combating human trafficking through data science

   Borrelli, Danielle; Caltagirone, Sherrie (January 2020, Cyber security)

   Human trafficking is a complex and challenging global crime exacerbated by the use of technology. Traffickers utilize technology for scalability, anonymity, and profitability as the Internet, social media platforms and encrypted messaging make the recruitment, exploitation and profit of an individual a low-risk, high-reward enterprise. Counter-trafficking efforts are often siloed approaches, resulting in decentralized information and analysis on the size and scope of trafficking in persons. Resources and tools such as the human trafficking kill chain methodology and Artemis, a machine learning (ML) human trafficking risk classifier, show promising disruption tactics which may also be applied to other asymmetrical threats. Recommendations for centralized data collections methods, inter-agency collaboration, and cybersecurity adjacent legislation are also made. 

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5. Towards ethical and resilient agricultural supply chains: interdicting labor trafficking and mitigating disruptions

   https://doi.org/10.17760/D20705224  

   Jafari, Arezoo (December 2024, Northeastern University Digital Repository Service)

   The food and agriculture industries are critical to the U.S. economy, ensuring the daily food supply while facing significant challenges. These issues include ethical concerns related to labor exploitation and the need to improve resilience against disruptions. Addressing these issues offers an opportunity to create supply chains that are both more ethical and more resilient. This dissertation focuses on two interconnected aspects of agricultural supply chains. The first examines strategies for disrupting exploitative labor practices and ensuring better protection for farm workers. The second explores methods to enhance the resilience of ethical supply chains against various disruptions, including natural disasters and labor shortages. Together, these aspects aim to contribute to the development of agricultural supply chains that are both ethically sound and resilient to disruptions. Although farm workers play an essential role in the success of these industries, they are vulnerable to labor exploitation and trafficking. Labor violations affecting these workers often go undetected due to limited government resources for inspection. Furthermore, many farm workers face barriers to disclosing their poor working conditions due to their immigration status and mistrust of law enforcement, making them even more susceptible to exploitation. To address this issue, we conducted research to provide strategies for government agencies involved in the H-2A visa program and the screening of H-2A employers to prioritize workplaces for inspection. In the first study, we employed multilevel zero-inflated negative binomial regression analysis to extract patterns and identify factors correlated with detecting H-2A labor violations. We provide suggestions for improving inspection strategies based on our research results. This involved identifying high-risk locations and labor-intensive worksites with a greater likelihood of labor violations and emphasizing the importance of allocating sufficient task force funding and resources to prioritize inspections in these areas. Labor trafficking networks in U.S. agricultural supply chains exploit vulnerable workers, including migrants and unauthorized laborers, while evading detection through complex structures, making them difficult to disrupt. In the second study, we developed a comprehensive labor trafficking network model that maps the intricate connections and operations of these networks. Using a bi-level integer programming approach, we optimized intervention strategies to disrupt trafficking operations, balancing resource constraints with the need for maximum impact. By employing K-means clustering, we classified interventions based on their effectiveness, providing clear, data-driven guidance for anti-trafficking agencies to prioritize efforts and allocate resources efficiently. This approach offers a powerful tool for enhancing detection and improving the overall effectiveness of anti-trafficking initiatives in limited resource environments. The importance of food and agricultural supply chains in our daily lives cannot be emphasized enough. While the prior two studies sought to disrupt exploitative work conditions in agricultural supply chains, this dissertation also seeks to help supply chains that are operating ethically do so in an effective manner. Any disruption in these chains can lead to severe consequences, from food shortages to economic instability. Therefore, it is critical to develop effective strategies to mitigate the impact of disruptions in these non-exploitative supply chains. In the third study, we developed a scenario-based two-stage stochastic model to mitigate the impact of multiple disruptions in agricultural supply chains. This approach enables a detailed evaluation of strategies such as multi-sourcing and the use of backup facilities to reduce disruption impacts. The model incorporates flexibility to handle both partial and full facility disruptions, while accounting for disruptions affecting both primary and backup facilities to provide a comprehensive analysis of supply chain vulnerability and recovery. By employing a multi-period time horizon, the model evaluates supply chain performance over time, considering random disruption start times and the possibility of simultaneous disruptions across multiple echelons with varying severity. The analysis highlights the challenges posed by multiple sources of uncertainty in supply chain decision-making and emphasizes the need for further research to develop actionable strategies for improving resilience in agricultural supply chains. 

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