Eliciting Truth in the
Information Age
We explore the intersection of Theoretical Computer Science and Economics (EconCS), designing mechanisms to aggregate information and benchmark intelligence.
Research Areas
Information Elicitation
In the absence of ground truth, how can we incentivize participants to provide truthful information (Peer Prediction)? How do we evaluate the quality of information and predictions?
Prediction Markets
Designing market mechanisms to aggregate dispersed information for accurate forecasting of future events.
Mechanism Design
Designing incentive-compatible mechanisms where maximizing individual interest aligns with system goals.
Applications to Crowdsourcing & ML
Robust aggregation against adversarial experts, benchmarking LLMs' judgments with information-theoretic methods, and exploring AI-Human collaboration.
Our Team
Yuqing Kong (孔雨晴)
Principal Investigator
Tenured Associate Professor at CFCS, Peking University. Ph.D. from University of Michigan. Research focuses on the intersection of TCS and Economics (EconCS).
PhD Students
Yongkang Guo (郭永康)
2021 PhD Student
Mingyu Song (宋铭宇)
2024 PhD Student
Yichong Xia (夏一翀)
2025 PhD Student
Alumni
Selected Publications
Algorithmic Robust Forecast Aggregation
Y. Guo, J. D. Hartline, Z. Huang, Y. Kong, A. Shah, F.-Y. Yu
Benchmarking LLMs' Judgments with No Gold Standard
S. Xu, Y. Lu, G. Schoenebeck, Y. Kong
Mitigating the Participation Bias by Balancing Extreme Ratings
Y. Guo, Y. Kong, J. Liu
Robust Aggregation with Adversarial Experts
Y. Guo, Y. Kong
Learning against Non-credible Second-Price Auctions
Q. Wang, X. Xia, Z. Yang, X. Deng, Y. Kong, Z. Zhang, L. Wang, C. Yu, J. Xu, B. Zheng
Aggregation of Antagonistic Contingent Preferences: When Is It Possible?
X. Deng, B. Tao, Y. Wang
Dominantly Truthful Peer Prediction Mechanisms with a Finite Number of Tasks
Y. Kong
The Surprising Benefits of Base Rate Neglect in Robust Aggregation
Y. Kong, S. Wang, Y. Wang
Eliciting Informative Text Evaluations with Large Language Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
Robust Decision Aggregation with Second-order Information
Y. Pan, Z. Chen, Y. Kong†
False Consensus, Information Theory, and Prediction Markets
Y. Kong, G. Schoenebeck
Calibrating 'Cheap Signals' in Peer Review without a Prior
Y. Lu, Y. Kong
Near-optimal experimental design under the budget constraint in online platforms
Y. Guo, Y. Yuan, J. Zhang, Y. Kong, Z. Zhu, & Z. Cai
Learning to bid in repeated first-price auctions with budgets
Q. Wang, Z. Yang, X. Deng, Y. Kong
Eliciting Thinking Hierarchy without a Prior
Y. Kong, Y. Li, Y. Zhang, Z. Huang, J. Wu
BONUS! Maximizing Surprise
Z. Huang, Y. Kong, X. Liu, G. Schoenebeck, S. Xu
More Dominantly Truthful Multi-task Peer Prediction with a Finite Number of Tasks
Y. Kong
SURPRISE! and When to Schedule It.
Z. Huang*, S. Xu*, Y. Shan, Y. Lu, Y. Kong, X. Liu, G. Schoenebeck
Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks
Y. Kong
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
X. Sun*, Y. Xu*, P. Cao, Y. Kong, L. Hu, S. Zhang, Y. Wang
Information Elicitation Mechanisms for Statistical Estimation
Y. Kong, G. Schoenebeck, B. Tao, F. Yu
Outsourcing Computation: the Minimal Refereed Mechanism
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao
LDMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
Y. Xu*, P. Cao*, Y. Kong, Y. Wang
Max-MIG: an Information-Theoretic Approach for Joint Learning from Crowds
P. Cao*, Y. Xu*, Y. Kong, Y. Wang
f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition
B. Zhang*, Y. Kong*, G. Essl, E. M. Provost
An Information-Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck
Eliciting Expertise without Verification
Y. Kong, G. Schoenebeck
Water from Two Rocks: Maximizing the Mutual Information
Y. Kong, G. Schoenebeck
Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
Y. Kong, G. Schoenebeck
Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
Y. Kong, G. Schoenebeck
Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal
Y. Kong, K. Ligett, G. Schoenebeck