Publications

Working Paper
Barton E. Lee, Daniel J. Moroz, and David C. Parkes. Working Paper. “An Analysis of Blockchain Governance via Political Economics.” 2021.
Omer Nahum, Gali Noti, David C. Parkes, and Nir Rosenfeld. Working Paper. “Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces.” CoRR abs/2306.10606 (2023).
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).
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.
Denizalp Goktas, David C. Parkes, Ian Gemp, Luke Marris, Georgios Piliouras, Romuald Elie, Guy Lever, and Andrea Tacchetti. Working Paper. “Generative Adversarial Equilibrium Solvers.” CoRR abs/2302.06607 (2023) .
Michael Neuder, Rithvik Rao, Daniel J. Moroz, David C. Parkes, Zhou Fan, and Francisco Marmolejo-Cossío. Working Paper. “Strategic Liquidity Provision in Uniswap v3.” 2023.
2023
Matheus V. X. Ferreira and David C. Parkes. 2023. “Credible Decentralized Exchange Design via Verifiable Sequencing Rules.” In Proceedings of the 55th Annual ACM Symposium on Theory of Computing (STOC 2023), Pp. 723-736 .
Tonghan Wang, Paul Duetting, Dmitry Ivanov, Inbal Talgam-Cohen, and David C. Parkes. 2023. “Deep Contract Design via Discontinuous Networks.” In Proceedings of the NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems).
Yaniv Yacoby, John Girash, and David C. Parkes. 2023. “Empowering First-Year Computer Science Ph.D. Students to Create a Culture that Values Community and Mental Health.” In Proc. of the The Technical Symposium on Computer Science Education , 1: Pp. 694-700 .
Paul Duetting, Felix A. Fischer, and David C. Parkes. 2023. “Non-Truthful Position Auctions Are More Robust to Misspecification.” Mathematics of Operations Research.
Xintong Wang, Gary Qiurui Ma, Alon Eden, Clara Li, Alexander Trott, Stephan Zheng, and David C. Parkes. 2023. “Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study.” In Proc. of the International World Wide Web Conference (WWW '23), Pp. 3592-3602 .
Jamelle Watson-Daniels, David C. Parkes, and Berk Ustun. 2023. “Predictive Multiplicity in Probabilistic Classification.” In Proc. of Association for the Advancement of Artificial Intelligence, Pp. 10306 - 10314. AAAI.
Zhun Deng, He Sun, Steven Wu, Linjun Zhang, and David C. Parkes. 2023. “Reinforcement Learning with Stepwise Fairness Constraints.” In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) , Pp. 10594-10618 .
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.
Mira Finkelstein, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenschein, and Sarah Keren. 2022. “Explainable Reinforcement Learning via Model Transforms. .” In Conference on Neural Information Processing Systems. NeurIPS 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
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 .
Mira Finkelstein, Nitsan Levy Schlot, Lucy Liu, Yoav Kolumbus, David C. Parkes, Jeffrey S. Rosenschein, and Sarah Keren. 2022. “Explainable Reinforcement Learning via Model Transforms.” In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) .

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