Situated, Collaborative Modeling: Critical Participatory Data Science in Rural Zimabawe with the Muonde Trust

 

Melissa Viola Eitzel Solera

Science and Justice Research Center, UC Santa Cruz

 

As a practicing modeler by training and experience, I have encountered many issues concerning the way data are handled, how models are made, and, in particular, how truth claims emerge from modeling. "Big data" are increasingly discussed in academic venues and elsewhere as an unquestioned good. However, there is dangerous potential for models to marginalize people based on biased or inappropriate data, affording them no recourse to those same tools to defend themselves. Therefore, modeling needs to be practiced more critically and less automatically.  I suggest practices grounded in Haraway's "Situated Knowledge," requiring more descriptive methods and ways to model responsibly and collaboratively, and then apply these principles to my collaborative modeling efforts with the Muonde Trust in Mazvihwa Communal Area, Zimbabwe. We created an agent-based model in NetLogo to represent land-use decisions and other management interventions in Mazvihwa's agro-pastoral system, investigating the feedbacks and tradeoffs in land allocation to arable production versus woodland grazing area and the corresponding impact on livestock populations.  We held workshops with community researchers, farmers, and leaders demonstrating and discussing the model at several points, which both led to alterations of the model and of land-use policy in the area. Local authorities are now in conversation with Muonde's researchers about ways to re-cultivate fallow fields rather than converting woodland to new arable production. Our modeling and community engagement process exemplifies many of the suggested situated modeling practices, and I will evaluate the success of critical participatory data science as a framing for this collaborative project.