Capturing the complexity of water uses and water users within a multi-agent framework

TLDR: In this paper, a case study from Chile is used as an example to demonstrate the potential of the MAS framework for water resources management in an efficient, equitable, and sustainable way.

Abstract: Due to the hydrological and socio-economic complexity of water use within river basins and even sub-basins, it is a considerable challenge to manage water resources in an efficient, equitable and sustainable way. This paper shows that multi-agent simulation (MAS) is a promising approach to better understand the complexity of water uses and water users within sub-basins. This approach is especially suitable to take the collective action into account when simulating the outcome of technical innovation and policy change. A case study from Chile is used as an example to demonstrate the potential of the MAS framework. Chile has played a pioneering role in water policy reform by privatizing water rights and promoting trade in such rights, devolving irrigation management authority to user groups, and privatizing the provision of irrigation infrastructure. The paper describes the different components of a MAS model developed for four micro-watersheds in the Maule river basin. Preliminary results of simulation experiments are presented, which show the impacts of technical change and of informal rental markets on household income and water use efficiency. The paper also discusses how the collective action problems in water markets and in small-scale and large-scale infrastructure provision can be captured by the MAS model. To promote the use of the MAS approach for planning purposes, a collaborative research and learning framework has been established, with a recently created multi-stakeholder platform at the regional level (Comision Regional de Recursos Hidricos) as the major partner. Finally, the paper discusses the potentials of using MAS models for water resources management, such as increasing transparency as an aspect of good governance. The challenges, for example the need to build trust in the model, are discussed as well.