A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change

Jul 12, 2020

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Coastal communities are at high risk of natural hazards due to unremitting global warming and sea level rise. Both the catastrophic impacts, e.g., tidal flooding and storm surges, and the long-term impacts, e.g., beach erosion, inundation of low lying areas, and saltwater intrusion into aquifers, cause economic, social, and ecological losses. Creating policies through appropriate modeling of the responses of stakeholders€™, such as government, businesses, and residents, to climate change and sea level rise scenarios can help to reduce these losses. In this work, we propose a Markov decision process (MDP) formulation for an agent (government) which interacts with the environment (nature and residents) to deal with the impacts of climate change, in particular sea level rise. Through theoretical analysis we show that a reasonable government's policy on infrastructure development ought to be proactive and based on detected sea levels in order to minimize the expected total cost, as opposed to a straightforward government that reacts to observed costs from nature. We also provide a deep reinforcement learning-based scenario planning tool considering different government and resident types in terms of cooperation, and different sea level rise projections by the National Oceanic and Atmospheric Administration (NOAA).

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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