Abstract
Sustainability and artificial intelligence both offer major instruments for addressing complex future challenges. In mass media and throughout various societal circles, their discussion and promotion enjoy increasing popularity. At the level of attitude and opinion formation, both have indeed major influence. Both have their unconditional followers and fans, and both have also important critics and even outright “deniers.” Utopian and dystopian sentiment changes hands. Within this context, it might seem challenging to find a principled way of describing and relating useful and acceptable concepts about the constructive interdependence of AI and sustainability. We propose to outline in which respect the notions and concepts of both artificial intelligence and sustainability are highly relevant for future human well-being. Both describe a complicated system of goals with important interdependence. In terms of social or behavioral mechanics, they may both be regarded as games on many levels. In order to elucidate ways of employing AI as an enabler of sustainability, we choose to focus on energy-sustainability as a subset of all sustainability concerns. Next, we highlight that AI may importantly play into sustainability by eventually enabling to solve some highly relevant but hard energy-technology problems. The next stages are modifying goals according to newly arriving perspectives and playing a repeated sustainability supergame, which includes different stakeholders on demand. Games may be employed as a means of strategy assessment as is the case in standard gaming but also as an AI-supported vehicle for evaluating, learning, or discovering structures. The argumentation is mainly nontechnical, in the sense of refraining from numeric or other concrete computational experiments which at this stage may appear rather arbitrary. Nevertheless, care is taken to connect to all technical contents in a precise and logically consistent manner.
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Abdulla, A., Ford, M. J., Morgan, M. G., & Victor, G. (2017). A retrospective analysis of funding and focus in US advanced fission innovation. Environmental Research Letters, 12(8). https://doi.org/10.1088/1748-9326/aa7f10.
Ahmadinejad, A. M., et al. (2017). From duels to battlefields: Computing equilibria of blotto and other games. Retrieved September 22, 2018, from https://arxiv.org/abs/arXiv:1603.00119.
Amabilino, S., et al. (2019). Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality. Prepublication manuscript downloaded from http://pubs.acs.org (on May 21, 2019).
Bhattacharyya, A., Braverman, M., Chazelle, B., & Nguyen, H. L. (2013). On the convergence of the Hegselmann-Krause system. In Proceedings of the 4th Conference on Innovations in Theoretical Computer Science (pp. 61–66). ACM.
Boixo, S., et al. (2018). Characterizing quantum supremacy in near-term devices. Nature Physics, 14, 595–600.
Borders, W. A., et al. (2019). Integer factorization using stochastic magnetic tunnel junctions. Nature Research, 573(7774), 390–393.
Boyer, M. D. Kaye S., & Erickson K. (2018). Real-time capable modeling of neutral beam injection on NSTX-U using neural networks. Nuclear Fusion 59, 056008, 15 pp.
Brockman, J. (2019). (Ed.). Possible minds: Twenty-five ways of looking at AI. Penguin Press.
Chastain, E., Livnat, A., Papadimitriou, C., & Vazirani, U. (2014). Algorithms, games, and evolution. PNAS, 111(29), 10621–10623.
Chater, N. (2019). The mind is flat: The remarkable shallowness of the improvising brain. Penguin Press.
Etesami, S. R., & Bașar, T. (2014). Game-theoretic analysis of the Hegselmann-Krause model for opinion dynamics in finite dimensions. Retrieved March 18, 2017, from https://arxiv.org/abs/arXiv:1412.6546v1.
EuroFusion. (2018). European research roadmap to the realisation of fusion energy, EUROfusion 2018. ISBN 978-3-00-061152-0. Retrieved May 26, 2019, from www.euro-fusion.org/eurofusion/roadmap.
Galla, T., & Farmer, J. D. (2013). Complex dynamics in learning complicated games. PNAS, 110(4), 1232–1236.
Gil, Y., & Selman, B. (2019). A 20-year community roadmap for artificial intelligence research in the US, Computing Community Consortium (CCC) and Association for the Advancement of Artificial Intelligence (AAAI), Washington DC. https://cra.org/crn/2019/09/a-20-year-community-roadmap-for-ai-research-in-the-us-is-released/.
Hoic-Bozic, N., Holenko Dlab, M., & Mornar, V. (2015). Recommender system and web 2.0 tools to enhance a blended learning model. IEEE Transactions on Education, 59, 1.
Hwangbo, H., & Kim, Y. (2016). Session-based recommender system for sustainable digital marketing, sustainability (Vol. 11, 3336); open access article.
Jackson, M. O., & Xing, Y. (2014). Culture-dependent strategies in coordination games. PNAS, 111(suppl. 3), 10889–10896.
Kates-Harbeck, J., Alexey Svyatkovskiy, A., & Tang, W. (2019). Predicting disruptive instabilities in controlled fusion plasmas through deep learning. Nature, 568, 526–531.
Karras, T., Laine S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks, CVPR Open Access Paper. http://openaccess.thecvf.com/content_CVPR_2019/papers/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.pdf.
Koepnick, B., et al. (2019). De novo protein design by citizen scientists. Nature, 570, 390–394.
Leonardos, N., Leonardos, S., & Piliouras, G. (2019). Oceanic games: Centralization risks and incentives in blockchain mining. Retrieved July 10, 2019, from https://arxiv.org/abs/arXiv:1904.02368v3.
Pournaras, E. (2019). Proof of witness presence: Blockchain consensus for augmented democracy in smart cities. Retrieved August 22, 2019, from https://arxiv.org/abs/arXiv:1907.00498v1.
Sadovskyy, I. A., et al. (2019). Targeted evolution of pinning landscapes for large superconducting critical currents. PNAS, 116(21), 10291–10296.
Schebesch, K. B. (2017). Some facilitators and inhibitors of knowledge-based socio-technological transformations. In F. Marimoan, M. Mas-Machuca, J. Berbegal-Mirabent, & R. Bastida (Eds.), Proceeding of the 18th European Conference on Knowledge Management, IUC Barcelona, Spain, 2017 (Vol. II, pp 872–880).
Stewart, A. J., & Plotkin, J. B. (2014). Collapse of cooperation in evolving games. PNAS, 111(4), 17558–17563.
Strnad, F. M., Barfuss, W., Donges, J. F., & Heitzig, J. (2019). Deep reinforcement learning in World-Earth system models to discover sustainable management strategies. Retrieved August 27, 2019, from https://arxiv.org/abs/arXiv:1908.05567v1.
Tautenhain, C. P. S., Barbosa-Povoa, A. P., Mota, B., & Nascimento, M. C. V. (2019). An efficient Lagrangian-based heuristic to solve a multi-objective sustainable supply chain problem. Retrieved August 12, 2019, from https://arxiv.org/abs/arXiv:1906.06375v1.
The Economist. (2019a). Popenomics, Schumpeter, September 7 (p. 58).
The Economist. (2019b). Climate change, briefing, September 21 (p. 58).
The Economist. (2019c). Automatic investing, briefing, October 5 (pp. 20–22).
Toporik, H., Li, J., Williams, D., Chiu, P.-L., & Mazor, Y. (2019). The structure of the stress-induced photosystem I-IsiA antenna supercomplex. Nature Structural & Molecular Biology, 26, 443–449.
Valdiviezo-Díaz, P., Aguilar, J., & Riofrio, G. (2016). A fuzzy cognitive map like recommender system of learning resources. In 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (pp. 1539–1546).
Vinuesa, R., et al. (2019. The role of artificial intelligence in achieving the sustainable development goals. Retrieved August 25, 2019, from https://arxiv.org/abs/arXiv:1905.00501.
Wang, L., Short, M. B., & Bertozzi, A. L. (2017). Efficient numerical methods for multiscale crowd dynamics with emotional contagion. Mathematical Models and Methods in Applied Sciences, 27(1), 205–230.
Wolfram, S. (2019). Artificial intelligence and the future of civilization. In Brockman (Ed.), Possible minds: Twenty-five ways of looking at AI (pp. 318–336). Penguin Press.
Wu, Y., et al. (2019, September 3). Nuclear safety in the unexpected second nuclear era. PNAS, 116(36), 17673–17682.
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Schebesch, K.B. (2020). The Interdependence of AI and Sustainability: Can AI Show a Path Toward Sustainability?. In: Fotea, S., Fotea, I., Văduva, S. (eds) Challenges and Opportunities to Develop Organizations Through Creativity, Technology and Ethics. GSMAC 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-43449-6_23
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