This model has been created with and for the researcher-farmers of the Muonde Trust (http://www.muonde.org/), a registered Zimbabwean non-governmental organization dedicated to fostering indigenous innovation. Model behaviors and parameters (mashandiro nemisiyano nedzimwe model) derive from a combination of literature review and the collected datasets from Muonde’s long-term (over 30 years) community-based research. The goals of this model are three-fold (muzvikamu zvitatu):
A) To represent three components of a Zimbabwean agro-pastoral system (crops, woodland grazing area, and livestock) along with their key interactions and feedbacks and some of the human management decisions that may affect these components and their interactions.
B) To assess how climate variation (implemented in several different ways) and human management may affect the sustainability of the system as measured by the continued provisioning of crops, livestock, and woodland grazing area.
C) To provide a discussion tool for the community and local leaders to explore different management strategies for the agro-pastoral system (hwaro/nzira yekudyidzana kwavanhu, zvipfuo nezvirimwa), particularly in the face of climate change.
This is a simulation model of an intelligent agent that has the objective to learn sustainable management of a renewable resource, such as a fish stock.
SWIM is a simulation of water management, designed to study interactions among water managers and customers in Phoenix and Tucson, Arizona. The simulation can be used to study manager interaction in Phoenix, manager and customer messaging and water conservation in Tucson, and when coupled to the Water Balance Model (U New Hampshire), impacts of management and consumer choices on regional hydrology.
Murphy, John T., Jonathan Ozik, Nicholson T. Collier, Mark Altaweel, Richard B. Lammers, Alexander A. Prusevich, Andrew Kliskey, and Lilian Alessa. “Simulating Regional Hydrology and Water Management: An Integrated Agent-Based Approach.” Winter Simulation Conference, Huntington Beach, CA, 2015.
We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.