Date of Award

5-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Industrial Engineering

Committee Chair/Advisor

Dr. Thomas Sharkey

Committee Member

Dr. Kayse Maass

Committee Member

Dr. Yongjia Song

Abstract

This thesis aims to further advance the understanding and modeling of life trajectories of sex trafficking survivors. The pathways sex trafficking survivors encounter pre- and post-trafficking can vary significantly and are affected by multiple susceptibilities. For that reason, we initially focus on survivors’ transitions across key attributes such as their mental health, substance use, and housing stability over time. We utilize a Markov Chain framework that captures the complexities of these various situations to model the potential experience encountered by survivors and visualize the life trajectory of survivors of trafficking. This is done by incorporating a history component to the Markov Chain model that allows us to capture how a survivor’s previous experience with a particular susceptibility influences their life trajectory, thus providing a more accurate representation of a survivor’s trafficking experience that prior literature does not address.

Further, we offer another Markov Chain model that tracks the amount of time a survivor has spent outside of trafficking as they move along a healing and recovery journey. This model is simpler than the initial layered Markov Chain model and, more importantly, is better supported empirically from recently collected qualitative data.  As a result, we provide a method to convert this recent qualitative data into data that can, potentially, populate the simpler Markov Chain model.

We then investigate how various final exit definitions, in terms of length of time since trafficking, impacted survivors exit trajectories.  Our results show that early and consistent support are good predictors of long-term exit outcomes.

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