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

Salman Sadiq Shuvo, Yasin Yilmaz, Alan Bush, Mark Hafen
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8872-8883, 2020.

Abstract

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).

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-shuvo20a, title = {A {M}arkov Decision Process Model for Socio-Economic Systems Impacted by Climate Change}, author = {Shuvo, Salman Sadiq and Yilmaz, Yasin and Bush, Alan and Hafen, Mark}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {8872--8883}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/shuvo20a/shuvo20a.pdf}, url = {https://proceedings.mlr.press/v119/shuvo20a.html}, abstract = {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).} }
Endnote
%0 Conference Paper %T A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change %A Salman Sadiq Shuvo %A Yasin Yilmaz %A Alan Bush %A Mark Hafen %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-shuvo20a %I PMLR %P 8872--8883 %U https://proceedings.mlr.press/v119/shuvo20a.html %V 119 %X 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).
APA
Shuvo, S.S., Yilmaz, Y., Bush, A. & Hafen, M.. (2020). A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:8872-8883 Available from https://proceedings.mlr.press/v119/shuvo20a.html.

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