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Pattern-oriented modeling to test frameworks of environmental migration decisions in Bangladesh
Kelsea Best, Vanderbilt University NASHVILLE Tennessee, United States. kelsea.best@gmail.com
Jonathan Gilligan, Vanderbilt University Nashville Tennessee, United States. jonathan.gilligan@vanderbilt.edu
Environmental change interacts with human migration in complex ways and across multiple scales. This complexity makes agent-based modeling (ABM) a powerful tool to investigate environment-migration dynamics. Here, we present results from an original ABM of environmental migration in Bangladesh. The model simulates an origin community and how a stylized environmental shock to the community impacts labor opportunities and household decisions surrounding migration. Pattern-oriented modeling is a useful approach for evaluating ABM’s by assessing a model’s ability to reproduce known patterns of phenomena. We use a pattern-oriented approach to test our model’s ability to reproduce multi-level patterns of environmental migration from the literature. Previous work combined pattern-oriented modeling with existing survey data and machine learning to parameterize and calibrate our ABM. We demonstrated that a strictly income-based migration decision method was able to reproduce patterns of interest, but inconsistently. However, the pattern-oriented approach allows us to implement more complex, behaviorally driven decision-making methods of migration and evaluate their success. In this work, we will present preliminary results implementing and comparing different decision-making methods in our ABM based on existing theories including Theory of Planned Behavior, Protection Motivation Theory, and a mobility potential framework. Ultimately, we hypothesize that a hybrid framework of migration decision-making that includes community norms, social networks, and place attachment will most successfully be able to replicate known patterns of environmental migration.
