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07/05/2021 - Many standard economic models assume people make perfectly rational, individual decisions. But new research suggests economic phenomena like inequality and business cycles are better explained by models which recognize that people’s decisions are affected by the decisions and the behaviors of people around them.

07/16/2020 - Communication is the key to overcoming the social dilemma of mitigating climate change, which requires investments from various actors towards a common goal. According to a new study published in Proceedings of the National Academy of Sciences (PNAS) and co-authored by Jürgen Kurths and Hans Joachim Schellnhuber from the Potsdam Institute for Climate Impact Research, communicating sentiment and outlook significantly improves group interactions in climate change mitigation processes.

13/02/2020 - Climate risks for finance were at the centre of a joint workshop at the Potsdam Institute for Climate Impact Research (PIK) with experts from the German Bundesbank. Both the physical impacts of global warming, for instance weather extremes, and the economical impacts of transitioning to net zero CO2 emissions have huge implications. The experts and scientists discussed potential future collaborations, namely on data exchange and computer simulation modelling.

20/12/2019 - For the first time, a specific way of machine learning has been used to find novel pathways for sustainable development. So far, the so-called 'Deep Reinforcement Learning' has mostly been used to make computers excel in certain games, such as AlphaGo, or navigate robots through rough terrain. Now, scientists from the Potsdam Institute for Climate Impact Research developed a mathematical framework combining recently developed machine learning techniques with more classical analysis of trajectories in computer simulations of the global climate system and the global economy. The results, published in the interdisciplinary journal on nonlinear phenomena 'Chaos', are promising.

11/04/2019 - Recent advancements in Artificial Intelligence (AI) research result from the combination of deep neuronal networks and reinforcement learning. In the latter, agents are able to learn rewarding behaviours in unknown environments by an iterative trial-and-error behaviour update process. But this process is not yet fully understood. Reinforcement learning agents are a specific area of AI. As AI can have a big impact on society, a better understanding AI systems is crucial to assess potential challenges and risks. Already today, AI is employed to steer cars, manage production lines, or even draft texts. A team of scientists from the Potsdam Institute for Climate Impact Research has developed a new method to investigate those algorithms using insights from statistical physics. Published in the journal Physical Reviews E, their insights can help to improve the design of large-scale AI reinforcement learning systems.