The scientists are stressing that the model of our world they used for testing their way of ‘Deep Reinforcement Learning’ is deliberately simplified. Hence the pathways for sustainable development identified by the artificial intelligence in the model, including for instance a specific mix of CO2 emissions taxation and subsidies for renewable energies, cannot directly be used in the real world. Yet the results show that the scientists’ application of machine learning does indeed find pathways that are innovative, compared to what classical analysis yielded. The study is hence of substantial methodological value. The authors call upon other scientists to apply their tool to further models in order to learn more about the potential of machine learning for sustainable development.
Article: Felix M. Strnad, Wolfram Barfuss, Jonathan Donges, Jobst Heitzig (2019): Deep reinforcement learning in World-Earth system models to discover sustainable management strategies. Chaos (Vol.29, Issue 12) ]DOI: 10.1063/1.5124673]
Weblink to the article: https://doi.org/10.1063/1.5124673
