Developers of agent-based models of socioecological systems are in a power-laden relationship with those they presume to model. It has often been the case that these developers do not inform their model with any sort of rich cultural data, and instead rely upon established methods from areas such as economics, laboratory psychology, and machine learning. While these methods can be effective, ignoring the perspective of the humans being represented in an ABM risks validation of that model for the wrong reasons and a marginalization of the humans represented in the model. Qualitative data collection methods, such as the collection of narratives, can aid not only in the elucidation of cultural ecological complexity, but also in the anchoring of an ABM to the political and ecological perspectives presented. While qualitative methods might lead to ABMs with higher fidelity to their real-world counterparts without as many power issues, making use of qualitative data during model development can be quite challenging, and no clear general methods exist. This thesis proposes a method to utilize long-form key informant narratives in the development of spatial agent-based models by linking the textual analysis of source documents to multiple modeling steps utilizing mental mapping and Object-Process Methodology extended for Multi-Agent-Systems (OPM/MAS). To test this method, narratives from migrants during the American Dust Bowl were analyzed and used to construct grounded models. The resulting model of a migrant agent is simple, easily understood and implemented, and its components can be linked directly to elements in the source narratives.
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