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).

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.

Daniel J. Moroz, Daniel J. Aronoff, Neha Narula, and David C. Parkes. 2020. “Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems.” CoRR abs/2002.10736.

Debmalya Mandal, Goran Radanovic, and David C. Parkes. 2020. “The Effectiveness of Peer Prediction in Long-Term Forecasting.” In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020).

Nir Rosenfeld, Anna Hilgard, Sai S. Ravindranath, and David C. Parkes. 2020. “From predictions to decisions: Using lookahead regularization.” In In Annual Conference on Neural Information Processing Systems, NeurIPS ’20.

Nripsuta Ani Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, Yang Liu, and David C. Parkes. 2020. “How do fairness definitions fare? testing public attitudes towards three algorithmic definitions of fairness in loan allocations.” Artificial Intelligence, 283, 103238.

Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, and Richard Socher. 2020. “Improving Equality and Productivity with AI-Driven Tax Policies.” CoRR abs/2004.13332.

Malvika Rao, David F. Bacon, David C. Parkes, and Margo Seltzer. 2020. “Incentivizing Deep Fixes in Software Economies.” IEEE Transactions on Software Engineering, 46, 1, Pp. 51-70.

Sarah Keren, Haifeng Xu, Kofi Kwapong, David C. Parkes, and Barbara Grosz. 2020. “Information Shaping for Enhanced Goal Recognition of Partially-Informed Agents.” In Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI-2020).

Zhe Feng, David C. Parkes, and Haifeng Xu. 2020. “The intrinsic robustness of stochastic bandits to strategic manipulation.” In Proceedings of the 37th International Conference on Machine Learning, ICML ’20, 119: Pp. 3092-3101. Proceedings of Machine Learning Research.

Nir Rosenfeld, Aron Szanto, and David C. Parkes. 2020. “A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone.” In Proceedings of the 29th World Wide Web Conference (WWW 2020), Pp. 1018-1028.

Paul Tylkin, Goran Radanovic, and David C. Parkes. 2020. “Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments.” In NeurIPS 2020 workshop on Cooperative AI.

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai S. Ravindranath. 2020. “Optimal auctions through deep learning.” Communications of the ACM, 63, 12.

Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah. 2020. “Peer Prediction with Heterogeneous Users.” ACM Transactions on Economics and Computation, 8, 1, Pp. 2:1-2:34.

Hongyao Ma, Reshef Meir, David C. Parkes, and Elena Wu-Yan. 2020. “Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present-Bias.” In , Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), Pp. 807-815.

Hau Chan, David C. Parkes, and Karim R. Lakhani. 2020. “The Price of Anarchy of Self-Selection in Tullock Contests (Extended Abstract).” In , Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), Pp. 1795-1797.

Sarah Keren, Sara Bernardini, Kofi Kwapong, and David C. Parkes. 2020. “Reasoning about plan robustness versus plan cost for partially informed agents.” In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020, Pp. 550–559.

Gianluca Brero, Alon Eden, Matthias Gerstgrasser, David C. Parkes, and Duncan Rheingans-Yoo. 2020. “Reinforcement learning of simple indirect mechanisms.” In NeurIPS’20 Workshop on Machine learning for Economic Policy.

Rose E. Wang, Sarah A. Wu, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, and Max Kleiman-Weiner. 2020. “Too Many Cooks: Coordinating Multi-agent Collaboration Through Inverse Planning (Extended Abstract).” In Proc. of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), Pp. 2032-2034.