Simulating Household Decision Making in Rural Malawi
- Homepage
- http://www.openabm.org/model/3946/version/1/view
- Started
- 1st June 2013
- Research Team
- Samantha Dobbie
- Investigators
- James Dyke, Kate Schreckenberg
Food security remains a deep seated issue throughout Sub-Saharan Africa. Within Malawi, the vast majority of the rural population are engaged in subsistence farming. Continued reliance upon rain-fed agriculture renders smallholders vulnerable to climatic shocks, whilst high population densities, small plot size and poor soil quality further compound food insecurity. Behavioural decisions and coping strategies of farmers in the face of drought formed the focus of this project. A participatory rural appraisal (PRA) exercise was designed to elicit greater understanding of smallholder responses to drought; as well as the perceived impact of government interventions in the form of input subsidies. A key objective of the project was to determine whether PRA exercises can be utilised in the construction of empirical agent-based models (ABM). Field data was successfully employed to inform the behavioural rules of agents. However, limited data availability was identified as a key issue undermining model integrity. Initial implementation of the model found inferences could be made concerning the impact of policy upon household decision making and food security. Overall the project provides fertile ground for future work. It is hoped that by integrating PRA exercises and ABM it will be possible to create a collaborative framework which promotes interaction between scientists, policy makers and stakeholders, alike.
Categories
Life sciences simulation: Systems biology
Physical Systems and Engineering simulation: Data Acquisition
Socio-technological System simulation: Human population, Social and Socio-economic Systems
Algorithms and computational methods: Artificial Neural Networks, Monte Carlo
Simulation software: NetLogo
Programming languages and libraries: Python
Computational platforms: Windows
Transdisciplinary tags: Complex Systems