Fetching the paper…
Reading the bibliography…
Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth.
Quantifying the Carbon Emissions of Machine Learning
Lacoste, A.; Luccioni, A.; Schmidt, V.; and Dandres, T. 2019 · 1910
Earlier work this paper cites.
The tragedy of the commons: the population problem has no technical solution; it requires a fundamental extension in morality
Hardin, G. 1968 · 1968
Earlier work this paper cites.
A theory of demand for products distinguished by place of production
Armington, P. S. 1969 · 1969
Earlier work this paper cites.
The Strategy of Conflict: with a new Preface by the Author
Schelling, T. C. 1980 · 1980
Earlier work this paper cites.
A theory of self-enforcing agreements
Telser, L. G. 1980 · 1980
Earlier work this paper cites.
Report of the United Nations Conference on Environment and Development
Assembly, U. N. G.; et al. 1992 · 1992
Earlier work this paper cites.
Agent-based modeling: Methods and techniques for simulating human systems
Bonabeau, E. 2002 · 2002
Earlier work this paper cites.
International environmental agreements: a survey of their features, formation, and effects
Mitchell, R. B. 2003 · 2003
Earlier work this paper cites.
Performance assessment of multiobjective optimizers: An analysis and review
Zitzler, E.; Thiele, L.; Laumanns, M.; Fonseca, C. M.; and Da Fonseca, V. G. 2003 · 2003
Earlier work this paper cites.
A review of the Stern review on the economics of climate change
Nordhaus, W. D. 2007 · 2007
Earlier work this paper cites.
A comprehensive survey of multiagent reinforcement learning
Busoniu, L.; Babuska, R.; and De Schutter, B. 2008 · 2008
Earlier work this paper cites.
Multiagent systems: Algorithmic, game-theoretic, and logical foundations
Shoham, Y.; and Leyton-Brown, K. 2008 · 2008
Earlier work this paper cites.
The effects of tariffs on coalition formation in a dynamic global warming game
Lessmann, K.; Marschinski, R.; and Edenhofer, O. 2009 · 2009
Earlier work this paper cites.
total factor productivity , 260–263
Comin, D. 2010 · 2010
Earlier work this paper cites.
API design for machine learning software: experiences from the scikit-learn project
Buitinck, L.; Louppe, G.; Blondel, M.; Pedregosa, F.; Mueller, A.; Grisel, O.; Niculae, V.; Prettenhofer, P.; Gramfort, A.; Grobler, J.; Layton, R.; VanderPlas, J.; Joly, A.; Holt, B.; and Varoquaux, G. 2013 · 2013
Earlier work this paper cites.
Game theory and climate diplomacy
DeCanio, S. J.; and Fremstad, A. 2013 · 2013
Earlier work this paper cites.
Modeling international climate change negotiations more responsibly: Can highly simplified game theory models provide reliable policy insights?
Madani, K. 2013 · 2013
Earlier work this paper cites.
Dynamic mechanism design: A myersonian approach
Pavan, A.; Segal, I.; and Toikka, J. 2014 · 2014
Earlier work this paper cites.
A bargaining game analysis of international climate negotiations
Smead, R.; Sandler, R. L.; Forber, P.; and Basl, J. 2014 · 2014
Earlier work this paper cites.
Multi-objective reinforcement learning using sets of pareto dominating policies
Van Moffaert, K.; and Nowé, A. 2014 · 2014
Earlier work this paper cites.
Breaking the tragedy of the horizon–climate change and financial stability
Carney, M. 2015 · 2015
Earlier work this paper cites.
A third wave in the economics of climate change
Farmer, J. D.; Hepburn, C.; Mealy, P.; and Teytelboym, A. 2015 · 2015
Cited alongside, same era.
Climate clubs: Overcoming free-riding in international climate policy
Nordhaus, W. 2015 · 2015
Cited alongside, same era.
The emergence of climate change and mitigation action by society: an agent-based scenario discovery study
Greeven, S.; Kraan, O.; Chappin, É. J.; et al. 2016 · 2016
Cited alongside, same era.
Science and policy characteristics of the Paris Agreement temperature goal
Schleussner, C.-F.; Rogelj, J.; Schaeffer, M.; Lissner, T.; Licker, R.; Fischer, E. M.; Knutti, R.; Levermann, A.; Frieler, K.; and Hare, W. 2016 · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; Dieleman, S.; Grewe, D.; Nham, J.; Kalchbrenner, N.; Sutskever, I.; Lillicrap, T.; Leach, M.; Kavukcuoglu, K.; Graepel, T.; and Hassabis, D. 2016 · 2016
Cited alongside, same era.
International Climate Agreements Under Review: The Potential of Negotiation Linkage Between Climate Change and Preferential Free Trade
Zenker, A. 2019 · 2019
Later among the works it cites.
Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J.; Antonoglou, I.; Hubert, T.; Simonyan, K.; Sifre, L.; Schmitt, S.; Guez, A.; Lockhart, E.; Hassabis, D.; Graepel, T.; Lillicrap, T.; and Silver, D. 2020 · 2020
Later among the works it cites.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P.; Gommers, R.; Oliphant, T. E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; van der Walt, S. J.; Brett, M.; Wilson, J.; Millman, K. J.; Mayorov, N.; Nelson, A. R. J.; Jones, E.; Kern, R.; Larson, E.; Carey, C. J.; Polat, İ.; Feng, Y.; Moore, E. W.; VanderPlas, J.; Laxalde, D.; Perktold, J.; Cimrman, R.; Henriksen, I.; Quintero, E. A.; Harris, C. R.; Archibald, A. M.; Ribeiro, A. H.; Pedregosa, F.; van Mulbregt, P.; and SciPy 1.0 Contributors. 2020 · 2020
Later among the works it cites.
Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings
Yu, L.; Sun, Y.; Xu, Z.; Shen, C.; Yue, D.; Jiang, T.; and Guan, X. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning with Opponent-Learning Awareness
Foerster, J. N.; Chen, R. Y.; Al-Shedivat, M.; Whiteson, S.; Abbeel, P.; and Mordatch, I. 2017 · 2017
Cited alongside, same era.
Why equity is fundamental in climate change policy research
Klinsky, S.; Roberts, T.; Huq, S.; Okereke, C.; Newell, P.; Dauvergne, P.; O’Brien, K.; Schroeder, H.; Tschakert, P.; Clapp, J.; et al. 2017 · 2017
Cited alongside, same era.
Maintaining cooperation in complex social dilemmas using deep reinforcement learning
Lerer, A.; and Peysakhovich, A. 2017 · 2017
Cited alongside, same era.
Obviously strategy-proof mechanisms
Li, S. 2017 · 2017
Cited alongside, same era.
Evolution of modeling of the economics of global warming: changes in the DICE model, 1992–2017
Nordhaus, W. 2018 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
Cited alongside, same era.
Emergent communication through negotiation
Cao, K.; Lazaridou, A.; Lanctot, M.; Leibo, J. Z.; Tuyls, K.; and Clark, S. 2018 · 2018
Cited alongside, same era.
Critical slowing down suggests that the western Greenland Ice Sheet is close to a tipping point
Boers, N.; and Rypdal, M. 2021 · 2021
Later among the works it cites.
Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective
Chelarescu, P. 2021 · 2021
Later among the works it cites.
The Paris Climate Agreement and future sea-level rise from Antarctica
DeConto, R. M.; Pollard, D.; Alley, R. B.; Velicogna, I.; Gasson, E.; Gomez, N.; Sadai, S.; Condron, A.; Gilford, D. M.; Ashe, E. L.; et al. 2021 · 2021
Later among the works it cites.
Ethical choices behind quantifications of fair contributions under the Paris Agreement
Dooley, K.; Holz, C.; Kartha, S.; Klinsky, S.; Roberts, J. T.; Shue, H.; Winkler, H.; Athanasiou, T.; Caney, S.; Cripps, E.; et al. 2021 · 2021
Later among the works it cites.
Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control
Hanumaiah, V.; and Genc, S. 2021 · 2021
Later among the works it cites.
Multi-agent reinforcement learning for renewable integration in the electric power grid
Mai, V.; Zhang, T.; and Lesage-Landry, A. 2021 · 2021
Later among the works it cites.
Artificial intelligence and the law in Canada
Martin-Bariteau, F.; and Scassa, T. 2020 · 2021
Later among the works it cites.
CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing
Schmidt, V.; Goyal, K.; Joshi, A.; Feld, B.; Conell, L.; Laskaris, N.; Blank, D.; Wilson, J.; Friedler, S.; and Luccioni, S. 2021 · 2021
Later among the works it cites.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Yu, C.; Velu, A.; Vinitsky, E.; Wang, Y.; Bayen, A.; and Wu, Y. 2021 · 2021
Later among the works it cites.
The impact of climate summits
Bakaki, Z. 2022 · 2022
Closest in time.
Assessing the effectiveness of orchestrated climate action from five years of summits
Chan, S.; Hale, T.; Deneault, A.; Shrivastava, M.; Mbeva, K.; Chengo, V.; and Atela, J. 2022 · 2022
Closest in time.
Towards circular and asymmetric cooperation in a multi-player Graph-based Iterated Prisoner’s Dilemma
Le Gléau, T.; Marjou, X.; Lemlouma, T.; and Radier, B. 2022 · 2022
Closest in time.
Determinants of emissions pathways in the coupled climate–social system
Moore, F. C.; Lacasse, K.; Mach, K. J.; Shin, Y. A.; Gross, L. J.; and Beckage, B. 2022 · 2022
Closest in time.
Climate change 2022: impacts, adaptation and vulnerability
Pörtner, H. O.; Roberts, D. C.; Adams, H.; Adler, C.; Aldunce, P.; Ali, E.; Begum, R. A.; Betts, R.; Kerr, R. B.; Biesbroek, R.; et al. 2022 · 2022
Closest in time.
The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning
Zheng, S.; Trott, A.; Srinivasa, S.; Parkes, D. C.; and Socher, R. 2022 · 2022
Closest in time.
A Survey of Multi-Agent Reinforcement Learning with Communication
Zhu, C.; Dastani, M.; and Wang, S. 2022 · 2022
Closest in time.