Knowledge-Brokering with Agent-Based Models: Some Experiences from Irrigation-

Abstract: One key advantage of agent-based modeling (ABM) is the one-to-one correspondence of real-world and computational agents, which facilitates participatory simulation and model-enhanced learning. Using ABM effectively poses a number of challenges that have not been fully resolved yet. Some of these challenges relate to organizational and institutional factors, such as finding an appropriate boundary arrangement in which scientists, policy makers and stakeholders can interact and jointly make use of the models. Other challenges relate to technical and economic factors, as the models must ensure continuous stakeholder involvement and actually provide some returns to end-users. This research tested computer-based decision tools in a knowledge broker arrangement. We applied the MP-MAS software to simulate how farmers interact with each other and react to changes in their economic and natural environment. In particular, we used the model for evaluating the willingness-to-pay for the construction of a new reservoir. A key innovation of the research was the development of the decision-support tools in close interaction with multiple stakeholders, including water user associations and members of the irrigation and agricultural administration. This interaction, which was organized in the form of individual consultations, workshops and training sessions, ensured that the simulations addressed the needs and priorities of different stakeholders and took their local knowledge into account