Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system

Abstract

Complex social-ecological systems can be difficult to study and manage. Simulation models can facilitate exploration of system behavior under novel conditions, and participatory modeling can involve stakeholders in developing appropriate management processes. Participatory modeling already typically involves qualitative structural validation of models with stakeholders, but with increased data and more sophisticated models, quantitative behavioral validation may be possible as well. In this study, we created a novel agent-basedmodel applied to a specific context: Zimbabwean non-governmental organization the Muonde Trust has been collecting data on their agro-pastoral system for the last 35 years and had concerns about land-use planning and the effectiveness of management interventions in the face of climate change. We collaboratively created an agent-based model of their system using their data archive, qualitatively calibrating it to the observed behavior of the real system without tuning any parameters to match specific quantitative outputs. We then behaviorally validated the model using quantitative community-based data and conducted a sensitivity analysis to determine the relative impact of underlying parameter assumptions, Indigenous management interventions, and different rainfall variation scenarios. We found that our process resulted in a model which was successfully structurally validated and sufficiently realistic to be useful for Muonde researchers as a discussion tool. The model was inconsistently behaviorally validated, however, with some model variables matching field data better than others. We observed increased model system instability due to increasing variability in underlying drivers (rainfall), and also due to management interventions that broke feedbacks between the components of the system. Interventions that smoothed year-to-year variation rather than exaggerating it tended to improve sustainability. The Muonde trust has used the model to successfully advocate to local leaders for changes in land-use planning policy that will increase the sustainability of their system.

Exploring the potential impacts of machine learning on trust in fishery management

“…These models have explored pursuer or evader vessel agents in order to simulate real‐world IUU fishing events (Akinbulire et al, 2017). These methods can help to include important social dynamics in the planning processes while heightening stakeholder confidence in modelling results (Eitzel et al, 2020; LaMere et al, 2020; Robinson et al, 2021).…”

Participatory futures thinking in the African context of sustainability challenges and socio-environmental change

“…"Understanding," in this context, was mentioned in different ways, for instance, as insights into future integration of food security and biodiversity conservation aims (Jiren et al Another major motivation of four of the reviewed articles was to develop "adaptive capacity" with different forms of capacity building to anticipate potential impacts or assess future impacts (Faysse et al 2018, Muhati et al 2018, Capitani et al 2019, Jiren et al 2020. Different forms of "assessments" were the motivation for a few papers, to assess if futures thinking contributes to consensus building and systems thinking (Schmitt Olabisi et al 2016) or to assess alternative development strategies (Capitani et al 2016) or impacts (König 2020). "Creative thinking" was also a motivation for a few papers (Muhati et al 2018.…”

“…2020), understanding of local stakeholder responses to change(Capitani et al 2019), managers' understanding of driving forces of landscape change(Muhati el al. 2018), or to elicit multiple stories of the future(Schmitt Olabisi et al 2018).…”

Assessing the Potential of Participatory Modeling for Decolonial Restoration of an Agro-Pastoral System in Rural Zimbabwe

“…In addition, though much of the technical implementation was done by outsider modelers, Muonde members were involved in the design, calibration, and validation of the model (Eitzel et al 2020a). This meant that they were involved in the simplification of the system as it was represented in the model, ensuring that enough complexity was still retained to make the model useful and recognizable to the community.…”

“…Publication can advance communitybased research by giving credit to the work of interdisciplinary, community-oriented, academic scholars and by raising the awareness among scientific and modeling audiences of community-based research. In addition, the improvements made to the model in the process of making it legible to academic audiences also enhanced its functionality for Muonde: A more reliable, faster-running model was easier to use in workshops as well as for calibrating and validating the model quantitatively and qualitatively (Eitzel et al 2020a), and it made the model more recognizable to the community. That said, the time spent on more precise calibration and validation processes was not necessary for community use (However, it was necessary for publication: Peer reviewers of papers presenting the model asked for justifications the model's parameters and structures).…”