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

Ignacio Palacios-Huerta, David C. Parkes, and Richard Steinberg. Working Paper. “Combinatorial Auctions in Practice”. SSRN

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”. arXiv

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.

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 .

David Parkes. 2021. “Playing with symmetry with neural networks.” Nature Machine Intelligence .

Stephan Zheng, Alexander Trott, Sunil Srinivasa, Richard Socher, and David C. Parkes. 2021. “The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning.” CoRR abs/2004.13332.

Sophie Hilgard, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2021. “Learning Representations by Humans.” In ICML 2021, Pp. 4227-4238.

Gianluca Brero, Darshan Chakrabarti, Alon Eden, Matthias Gerstgrasser, Vincent Li, and David C. Parkes. 2021. “Learning Stackelberg Equilibria in Sequential Price Mechanisms.” In . Proc. ICML Workshop for Reinforcement Learning Theory.

Rediet Abebe, Hubert Chan, Jon Kleinberg, Zhibin Liang, David C. Parkes, Mauro Sozio, and Charalampos Tsourakakis. 2021. “Opinion Dynamics with Varying Susceptibility to Persuasion via Non-Convex Local.” ACM Transactions on Knowledge Discovery from Data, 16, Pp. 1-34.

Paul Duetting, Zhe Feng, Hari Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 2021. “Optimal auctions through deep learning.” Communications of the ACM, 64, 8, Pp. 109-116.

Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David C. Parkes, and Duncan Rheingans-Yoo. 2021. “Reinforcement Learning of Simple Indirect Mechanisms.” In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).

Hongyao Ma, Fei Fang, and David C. Parkes. 2021. “Spatio-temporal pricing for ridesharing platforms.” Operations Research .

Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, and Max Kleiman-Weiner. 2021. “Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration.” Topics Cognitive Science , 13, 2, Pp. 2032-2034.

Vincent Conitzer, Zhe Feng, David C. Parkes, and Eric Sodomka. 2021. “Welfare-Preserving ε-BIC to BIC Transformation with Negligible Revenue Loss.” In . Proceedings 17th Conference on Web and Internet Economics (WINE).

Michael Neuder, Daniel J. Moroz, Rithvik Rao, and David C. Parkes. 2020. “Selfish Behavior in the Tezos Proof-of-Stake Protocol.” In Cryptoeconomic Systems (CES) Conference 2020.

Sarah A. Wu, Rose E. Wang, James A. Evans, Josh Tenenbaum, David C. Parkes, and Max Kleiman-Weiner. 2020. “Too many cooks: Coordinating multi-agent collaboration through inverse planning.” In Proc. 42nd Annual Meeting of the Cognitive Science Society, Pp. 889-895 .

Haris Aziz, Hau Chan, Barton E. Lee, and David C. Parkes. 2020. “The capacity constrained facility location problem.” Games Econ. Behavior, 124:478–490, Pp. 478-490.

Michael Neuder, Daniel J. Moroz, Rithvik Rao, and David C. Parkes. 2020. “Defending against malicious reorgs in tezos proof-of-stake.” In AFT ’20: 2nd ACM Conference on Advances in Financial Technologies (ACM ’20), Pp. 46–58.