Reinforcement learning for decision-making under deep uncertainty.

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency.

The enabling institutional context for integrated water management: lessons from Melbourne.

There is widespread international acceptance that climate change, demographic shifts and resource limitations impact on the performance of water servicing in cities. In response to these challenges, many scholars propose that a fundamental move away from traditional centralised infrastructure towards more integrated water management is required. However, there is limited practical or scholarly understanding of how to enable this change in practice and few modern cities have done so successfully.

A socio-technical model to explore urban water systems scenarios.

This article reports on the ongoing work and research involved in the development of a socio-technical model of urban water systems. Socio-technical means the model is not so much concerned with the technical or biophysical aspects of urban water systems, but rather with the social and institutional implications of the urban water infrastructure and vice versa. A socio-technical model, in the view purported in this article, produces scenarios of different urban water servicing solutions gaining or losing influence in meeting water-related societal needs, like potable water, drainage, environmental health and amenity.