[42] Luc Lauwers. Intertemporal objective functions: Strong pareto versus anonymity. Mathematical Social Sciences, 35(1):37-55, 1998.
[43] R Duncan Luce. Individual choice behavior. 1959.
[44] Mark J Machina. Dynamic consistency and non-expected utility models of choice under uncertainty. Journal of Economic Literature, 27(4):1622–1668, 1989.
[45] Franz Mandl. Statistical physics, volume 14. John Wiley & Sons, 1991.
[46] Shuwa Miura. On the expressivity of multidimensional markov reward. arXiv preprint arXiv:2307.12184, 2023.
[47] Hervé Moulin. *Fair division and collective welfare*. MIT press, 2004.
[48] Krikamol Muandet. Impossibility of collective intelligence. arXiv preprint arXiv:2206.02786, 2022.
[49] Sendhil Mullainathan and Richard H Thaler. Behavioral economics, 2000.
[50] Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, and Richard S Sutton. Discounted reinforcement learning is not an optimization problem. arXiv preprint arXiv:1910.02140, 2019.
[51] Geraud Nangue Tasse, Steven James, and Benjamin Rosman. A boolean task algebra for reinforcement learning. Advances in Neural Information Processing Systems, 33:9497-9507, 2020.
[52] Andrew Y Ng and Stuart J Russell. Algorithms for inverse reinforcement learning. In The Seventeenth International Conference on Machine Learning, pages 663-670, 2000.
[53] Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning. PMLR, 2017.
[54] Silviu Pitis. Rethinking the discount factor in reinforcement learning: A decision theoretic approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 7949-7956, 2019.
[55] Martin L Puterman. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014.
[56] David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, and Francis Song. Synthetic returns for long-term credit assignment. arXiv preprint arXiv:2102.12425, 2021.
[57] Sarah Rathnam, Sonali Parbhoo, Weiwei Pan, Susan Murphy, and Finale Doshi-Velez. The unintended consequences of discount regularization: Improving regularization in certainty equivalence reinforcement learning. In International Conference on Machine Learning, pages 28746-28767. PMLR, 2023.
[58] John Rawls. A theory of justice: Revised edition. Harvard university press, 2009.
[59] Zhizhou Ren, Ruihan Guo, Yuan Zhou, and Jian Peng. Learning long-term reward redistribution via randomized return decomposition. In International Conference on Learning Representations, 2023.
[60] Gavin Rens, Jean-François Raskin, Raphaël Reynoaud, and Giuseppe Marra. Online learning of non-markovian reward models. In Proceedings of the Thirteenth International Conference on Agents and Artificial Intelligence. Scitepress, 2020.
[61] Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley. A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48:67-113, 2013.
[62] Leonard J Savage. The foundations of statistics. Wiley, 1954.
[63] Matthias Schultheis, Constantin A Rothkopf, and Heinz Koeppl. Reinforcement learning with nonexponential discounting. In Advances in neural information processing systems, 2022.
[64] Mehran Shakerinava and Siamak Ravanbakhsh. Utility theory for sequential decision making. In International Conference on Machine Learning, pages 19616-19625. PMLR, 2022.
[65] David R Shanks, Richard J Tunney, and John D McCarthy. A re-examination of probability matching and rational choice. Journal of Behavioral Decision Making, 15(3):233-250, 2002.
[66] David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, André Barreto, and Thomas Degris. The predictron: End-to-end learning and planning. In *The Thirty-fourth International Conference on Machine Learning*, 2017.