Pathways to agri-food supply chains that co-benefit people and nature. This project aims to improve biodiversity outcomes of agricultural food production and consumption, and expects to generate new knowledge about impacts of interventions and shocks on the environment, human health and livelihoods in agri-food systems. This will be achieved using an interdisciplinary approach that accounts for uncertainties in links between farmers, suppliers, consumers and supply-chain outcomes. The expected o ....Pathways to agri-food supply chains that co-benefit people and nature. This project aims to improve biodiversity outcomes of agricultural food production and consumption, and expects to generate new knowledge about impacts of interventions and shocks on the environment, human health and livelihoods in agri-food systems. This will be achieved using an interdisciplinary approach that accounts for uncertainties in links between farmers, suppliers, consumers and supply-chain outcomes. The expected outcome is a value of information framework for identifying nature-friendly policies and actions with co-benefits for human well-being. Benefits include sustainability pathways with win-win outcomes for people and nature, and improved ways of meeting international commitments such as Sustainable Development Goals.Read moreRead less
Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project ....Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project aims to provide theoretically sound frameworks for solving large Markov decision processes, and exploit them to solve important combinatorial optimisation problems. This timely project can promote Australia's position in the development of such novel frameworks for many scientific and industrial applications.Read moreRead less