1 code implementation • 21 Feb 2024 • Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, YiLing Chen
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
no code implementations • 15 Feb 2024 • Tao Lin, YiLing Chen
We study a repeated Bayesian persuasion problem (and more generally, any generalized principal-agent problem with complete information) where the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal's signals.
no code implementations • 7 Feb 2024 • Safwan Hossain, Tonghan Wang, Tao Lin, YiLing Chen, David C. Parkes, Haifeng Xu
The core solution concept here is the Nash equilibrium of senders' signaling policies.
no code implementations • 16 Feb 2023 • Safwan Hossain, YiLing Chen
Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality.
no code implementations • 7 Feb 2023 • YiLing Chen, Tao Lin
We show that, under natural assumptions, (1) the sender can find a signaling scheme that guarantees itself an expected utility almost as good as its optimal utility in the classic model, no matter what approximately best-responding strategy the receiver uses; (2) on the other hand, there is no signaling scheme that gives the sender much more utility than its optimal utility in the classic model, even if the receiver uses the approximately best-responding strategy that is best for the sender.
no code implementations • 27 Sep 2022 • Gali Noti, YiLing Chen
Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making in a wide range of domains, such as healthcare, criminal justice, and finance.
no code implementations • 15 Jul 2021 • YiLing Chen, Fang-Yi Yu
We further remark that widely used scoring rules, such as the quadratic and log rules, as well as previously identified optimal scoring rules under full knowledge, can be far from optimal in our partial knowledge settings.
no code implementations • 2 Apr 2021 • YiLing Chen, Alon Eden, Juntao Wang
In contrast, we show that for sum-concave valuations, which include weighted-sum valuations and l_p-norms, the welfare optimal EPBB mechanism obtains half of the optimal welfare as the number of agents grows large.
no code implementations • 9 Dec 2020 • Ben Green, YiLing Chen
In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8. 3%.
no code implementations • NeurIPS 2020 • Yiling Chen, Yang Liu, Chara Podimata
We address the question of repeatedly learning linear classifiers against agents who are strategically trying to game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
no code implementations • 11 Sep 2018 • Yiling Chen, Yang Liu, Juntao Wang
We show that a broad family of randomized wagering mechanisms satisfy all desirable theoretical properties.
no code implementations • 27 May 2018 • Yiling Chen, Chara Podimata, Ariel D. Procaccia, Nisarg Shah
This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources.