[54] Saumik Narayanan, Guanghui Yu, Wei Tang, Chien-Ju Ho, and Ming Yin. How does predictive information affect human ethical preferences? In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022.
[55] Saumik Narayanan, Guanghui Yu, Chien-Ju Ho, and Ming Yin. How does value similarity affect human reliance in ai-assisted ethical decision making? In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023.
[56] Andrew Y Ng, Stuart J Russell, et al. Algorithms for inverse reinforcement learning. In International Conference on Machine Learning, 2000.
[57] Raymond S Nickerson. Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2):175-220, 1998.
[58] Neehar Peri, Michael Curry, Samuel Dooley, and John Dickerson. Preferencenet: Encoding human preferences in auction design with deep learning. Advances in Neural Information Processing Systems, 34:17532-17542, 2021.
[59] Joshua C Peterson, David D Bourgin, Mayank Agrawal, Daniel Reichman, and Thomas L Griffiths. Using large-scale experiments and machine learning to discover theories of human decision-making. Science, 372(6547):1209-1214, 2021.
[60] Drazen Prelec. The probability weighting function. Econometrica, pages 497-527, 1998.
[61] Jad Rahme, Samy Jelassi, and S Matthew Weinberg. Auction learning as a two-player game. In International Conference on Learning Representations, 2020.
[62] Luis Rayo and Ilya Segal. Optimal information disclosure. Journal of Political Economy, 118 (5):949-987, 2010.
[63] Siddharth Reddy, Sergey Levine, and Anca Dragan. Assisted perception: optimizing observations to communicate state. In Conference on Robot Learning, pages 748-764. PMLR, 2021.
[64] Marc Oliver Rieger and Mei Wang. Cumulative prospect theory and the st. petersburg paradox. Economic Theory, 28(3):665-679, 2006.
[65] Tuomas Sandholm. Automated mechanism design: A new application area for search algorithms. In International Conference on Principles and Practice of Constraint Programming, pages 19-36. Springer, 2003.
[66] Tuomas Sandholm and Anton Likhodedov. Automated design of revenue-maximizing combinatorial auctions. Operations Research, 63(5):1000-1025, 2015.
[67] Rohin Shah, Noah Gundotra, Pieter Abbeel, and Anca Dragan. On the feasibility of learning, rather than assuming, human biases for reward inference. In International Conference on Machine Learning, pages 5670-5679. PMLR, 2019.
[68] Kenneth A Small. A discrete choice model for ordered alternatives. Econometrica: Journal of the Econometric Society, pages 409-424, 1987.
[69] Wei Tang and Chien-Ju Ho. Bandit learning with biased human feedback. In International Conference on Autonomous Agents and Multiagent Systems, 2019.
[70] Wei Tang and Chien-Ju Ho. On the bayesian rational assumption in information design. In AAAI Conference on Human Computation and Crowdsourcing, 2021.
[71] Wei Tang, Chien-Ju Ho, and Yang Liu. Differentially private contextual dynamic pricing. In International Conference on Autonomous Agents and Multiagent Systems, pages 1368-1376, 2020.
[72] Wei Tang, Chien-Ju Ho, and Yang Liu. Linear models are robust optimal under strategic behavior. In International Conference on Artificial Intelligence and Statistics, pages 2584-2592. PMLR, 2021.