MOST: Modeling Operations of Sex Trafficking

Modeling Operations of Sex Trafficking (MOST) Project Overview

The MOST project was foundational to our team since it helped to build a unique, trandisciplinary, highly-functioning team of academic researchers and lived experience experts to address the complexities of modeling sex trafficking networks. Mathematical models of sex trafficking networks have the potential for identifying the impact of well-meaning action into disrupting them; however, models are only as useful as the assumptions they are built on and the data used to populate them. The MOST project helped to gather data on the operational structures of sex trafficking networks that make them unique among illicit networks. It created an approach to systematically convert qualitative law enforcement case file data to quantitative network data for use in operations research models. It helped to understand the limitations of such case file data and found new ways to identify network structures.

The MOST Project was supported by the National Science Foundation through grant number 1838315. The views and conclusions presented here are those of the project team and should not be interpreted as reflecting the views of the National Science Foundation.

Research Overview

Building a Strong Community-Engaged, Transdisciplinary Team

Our team included qualitative researchers, mathematical modelers (in operations research), survivor-centered practitioners, and law enforcement. Early on in our work together, we focused on building shared trust and effective communications across academic disciplines and sources of knowledge. This early work has helped to build a strong, community-engaged team that can tackle the vexing social challenge that is human trafficking in a way that all members are equally valued and are comfortable asking questions about the research.

Integrating Knowledge Across Disciplines and Sources: Courtesy of Martin et al. (2022)

Sex Trafficking Network from LE Case File: Courtesy of Kosmas et al. (2024)

Constructing Network Data from Case Files

Our team worked together to create a qualitative coding process that identified key features, such as nodes and arcs, to build a network representation of a sex trafficking operation from law enforcement case files. Nodes represent critical people, places, and items in the operation and arcs represent connections. However, despite the success of the process, this data only presents a narrow view of the overall operations since case files are collected to prove that a crime was committed.

Constructing Network Data from Survivor-Centered Expertise

Our community-engaged process allowed for all team members to participate in "network thinking" activities. The results of these activities help to shed new light onto the structure of trafficking network and how different control strategies influence the network structure of an individual trafficking operation. This data helps to identify information that may not be known to those outside of the trafficking network.

Sample Network from Survivor-Centered Brainstorming Activities: Courtesy of Kosmas et al. (2024)

MOST Publications

Note: Presented in reverse chronological order.

Kosmas, D., Melander, C., Singerhouse, E., Martin, L., Barrick, K., Maass, K.L., & Sharkey, T.C. (2024). A transdisciplinary approach for generating synthetic but realistic domestic sex trafficking networks. IISE Transactions, 56(3), 340-354.
Martin, L., Gupta, M., Maass, K. L., Melander, C., Singerhouse, E., Barrick, K., Samad, T., Sharkey, T.C., Ayler, T., Forliti, T., Friedman, J., Nelson, C., & Sortillion, D. (2022). Learning each other’s language and building trust: Community-engaged transdisciplinary team building for research on human trafficking operations and disruption. International Journal of Qualitative Methods, 21, 16094069221101966.
Sharkey, T.C., Barrick, K., Farrell, A., Maass, K.L., Martin, L. and Song, Y. (2021) Better together: A transdisciplinary approach to disrupt human trafficking. ISE Magazine, 51(11), 34–39.

Banner picture is courtesy of team member Kayse Maass.

Page updated

Google Sites

Report abuse