Francesca Dominici, Elizabeth Langdon-Gray, and David C. Parkes. 2022. “Spinning Up a Data Science Initiative at Harvard.” Harvard Data Science Review, 4, 4.

Gianluca Brero, Nicolas Lepore, Eric Mibuari, and David C. Parkes. 2022. “Learning to Mitigate AI Collusion on Economic Platforms.” In https://arxiv.org/abs/2202.07106 . NeurIPS 2022.

Zhou Fan, Francisco J. Marmolejo Cossío, Ben Altschuler, He Sun, Xintong Wang, and David C. Parkes. 2022. “Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design.” In Proc. of Association for Computing Machinery (ACM) International Conference on Artificial Intelligence in Finance (ICAIF) , Pp. 9-17 .

Sai S. Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott Duke Kominers, and David C. Parkes. 8/2022. “Deep Learning for Two-Sided Matching.” In 6th workshop in an interdisciplinary and international workshop series on matching under preferences. Vienna, Austria.

Matthias Gerstgrasser, Rakshit Trivedi, and David C. Parkes. 2022. “CrowdPlay: Crowdsourcing human demonstrations for offline learning.” In International Conference on Learning Representations (ICLR) 2022. Download

Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, and Richard Socher. 2022. “The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning.” Science Advances, 8, 18, Pp. eabk2607. Download

Ignacio Palacios-Huerta, David C. Parkes, and Richard Steinberg. 2022. “Combinatorial Auctions in Practice.” Economics Literature . Download

Zhe Feng, David C. Parkes, and Sai Srivatsa Ravindranath. 2022. “Machine Learning for Matching Markets.” In Online matching theory and market design. N. Immorlica, F. Echenique, and V. Vazirani (eds), Cambridge University Press.

Hongyao Ma, Fei Fang, and David C. Parkes. 2022. “Spatio-temporal pricing for ridesharing platforms.” Operations Research , 70, 2, Pp. 1025-1041.

Rediet Abebe, Hubert Chan, Jon Kleinberg, Zhibin Liang, David C. Parkes, Mauro Sozio, and Charalampos Tsourakakis. 2022. “Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local.” ACM Transactions on Knowledge Discovery from Data, 16, 2, Pp. 33:1-33:34 .