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Trust Mechanism Design on Blockchain: An Interdisciplinary Approach of Game Theory, Reinforcement Learning and Human-AI Interaction


Mechanism design theory, introduced by 2007 Nobel laureates Hurwicz, Maskin, and Myerson, has guided economic institutions worldwide by efficiently allocating resources to achieve desirable outcomes. However, current mechanism design theories rely on trusted third parties. In reality, we regret seeing that mutually beneficial transactions fail to occur due to trust issues, and social welfare cannot be fully optimized. Blockchain technology, known as the trust machine in cyberspace, can replace trusted third parties with tamper-proof algorithms. However, how blockchain will enable mechanism design remains to be studied. More importantly, the past decades have witnessed an emerging digital economy with an increasingly complex system. New application scenarios have inspired new questions. How can we analyze big data in a computationally efficient way to draw causal inferences for policy advice? How can we extend the foundation of mechanism design theory to create trust in cyberspace? How can we design experiments to identify more realistic behavioral assumptions underpinning our theory? To answer these important but challenging questions, this research agenda explores trust mechanism design on blockchains through an interdisciplinary study. First, we apply the method of machine learning and causal inference to evaluate the efficiency and fairness of existing mechanisms by analyzing the historical data of natural experiments on the blockchain. Then, we investigate the design principles of trust mechanisms on blockchains by an integrated method of algorithmic game theory, mechanism design, and reinforcement learning. Finally, we research the behavioral foundations of trust mechanism design on blockchains using behavioral experiments, especially the methods in the human-computer interaction and bounded rationality literature. This research agenda has the potential to expand the theoretical foundation of trust mechanism design and empower the digital transformation of Industry 4.0.


Prof. Luyao Zhang

Luyao (Sunshine) Zhang is an Assistant Professor of Economics and Senior Research Scientist at the Data Science Research Center at Duke Kunshan University (DKU). She has an abiding passion for interdisciplinary collaborations, especially for cutting-edge research of both profound insights and practical impacts, including computational economics, explainable AI, cryptoeconomics, behavioral science, and interdisciplinary big data. Her current research interests are in the interplay of computational science and economics around the applications of Blockchain technology. Her publications appear in economic and computational science journals and conference proceedings for general interest and beyond, including American Economic Review: Papers and Proceedings, the Review of Economics and Statistics, the World Economy, Nature Research Scientific Data, Springer Nature Social Indicators Research, Springer Nature Eastern Economic Journal, ACM CCS, AAAI/ACM AIES, Springer Nature Lecture Notes in Networks and Systems, IEEE International Conference on Blockchain Proceedings, Remote Sensing, Journal of Digital Earth, Data and Information Management. She received Ph.D. in Economics at Ohio State University, supported by Presidential Fellowship and NSF dissertation grant.
She graduated from Peking University with a B.A. in Economics and a B.S. in Math and Applied Math. She is currently supported by National Science Foundation in China for her research agenda entitled “Trust Mechanism Design on Blockchain: An Interdisciplinary Approach of Game Theory, Reinforcement Learning, and Human-AI Interactions.” She is awarded the 60 Pioneers in Blockchain Innovation by the National Collegiate Artificial Intelligence and Big Data Innovation Alliance in 2022.
Beyond academics, she is also the Founding President of SciEcon CIC, a non-for-profit organization based in U.K., aiming to cultivate integrated talents and interdisciplinary research. She has also jointly led research and developer grant projects with blockchain pioneers in both academia and industry.