Gianluca Brero, Nicolas Lepore, Eric Mibuari, and David C. Parkes. Working Paper. “Learning to Mitigate AI Collusion on Economic Platforms.” CoRR abs/2202.07106 (2022).

Jamelle Watson-Daniels, David C. Parkes, and Berk Ustun. Working Paper. “Predictive Multiplicity in Probabilistic Classification.” CoRR abs/2206.01131 (2022).

Barton E. Lee, Daniel J. Moroz, and David C. Parkes. Working Paper. “An Analysis of Blockchain Governance via Political Economics.” 2021.

Sai Srivatsa Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott D. Kominers, and David C. Parkes. Working Paper. “Deep Learning for Two-Sided Matching.” CoRR abs/2107.03427 (2021).

Zhou Fan, Francisco J. Marmolejo Cossío, Ben Altschuler, He Sun, Xintong Wang, and David C. Parkes. Working Paper. “Differential Liquidity Provision in Uniswap v3 and Implications for Contract Design.” CoRR abs/2204.00464 (2022).

Daniel J. Moroz, Daniel J. Aronoff, Neha Narula, and David C. Parkes. Working Paper. “Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems.” CoRR abs/2002.10736.

Michael Neuder, Daniel J. Moroz, Rithvik Rao, and David C. Parkes. Working Paper. “Low-cost attacks on Ethereum 2.0 by sub-1/3 stakeholders.” CoRR abs/2102.02247.

Hongyao Ma, Reshef Meir, David C. Parkes, and Elena Wu-Yan. Working Paper. “Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias”. arXiv

Michael Neuder, Rithvik Rao, Daniel J. Moroz, and David C. Parkes. Working Paper. “Strategic Liquidity Provision in Uniswap v3.” CoRR abs/2106.12033 (2021).

Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, and David C. Parkes. Working Paper. “Using Reinforcement Learning to Study Platform Economies under Market Shocks.” CoRR abs/2203.13395 (2022).

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.

Gianluca Brero, Nicolas Lepore, Eric Mibuari, and David C. Parkes. 2022. “Learning to Mitigate AI Collusion on Economic Platforms.” In Workshop on Learning with Strategic Agents at AAMAS 2022.

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

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

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.

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 .

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

Paul Tylkin, Goran Radanovic, and David C. Parkes. 2021. “Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments.” In Proc. 20th Int. Conf. on Auton. Agents and Multiagent Systems (AAMAS), Pp. 1686-1688 .

Matheus V. X. Ferreira, Daniel J. Moroz, David C. Parkes, and Mitchell Stern. 2021. “Dynamic posted-price mechanisms for the blockchain transaction-fee market.” In AFT 2021, Pp. 86-99.