Publications

2020
Yiling Chen, Haifeng Xu, and Shuran Zheng. 2020. “Selling Information Through Consulting.” In ACM-SIAM Symposium on Discrete Algorithms (SODA20). Download
Ben Green and Salome Viljoen. 2020. “Algorithmic realism: expanding the boundaries of algorithmic thought.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20), Pp. 19-31. Association for Computing Machinery. Download
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) . Download
Ben Green. 2020. “The false promise of risk assessments: epistemic reform and the limits of fairness.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20), Pp. 594-606. Association for Computing Machinery. Download
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. Download
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). Download
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). Download
Arpit Agarwal, Debmalya Mandal, David C. Parkes, and Nisarg Shah. 2020. “Peer Prediction with Heterogeneous Users.” Transactions on Economics and Computation. Download
2019
Ben Green. 4/2019. The Smart Enough City, Putting Technology in It's Place to Reclaim Our Urban Future., Pp. 240. Cambridge: MIT Press. Download
Ben Green and Yiling Chen. 1/2019. “Disparate Interactions: An Algorithm-in-the-Loop Analysis of Fairness in Risk Assessments.” In Conference on Fairness, Accountability, and Transparency (FAT* ’19). Download
Hongyao Ma, Fei Fang, and David C. Parkes. 2019. “Spatio-Temporal Pricing for Ridesharing Platforms.” In Proceedings of the 20th ACM Conference on Economics and Computation (EC'19), Pp. 583. Download
Berk Ustun, Alexander Spanghler, and Yang Liu. 2019. “Actionable Recourse in Linear Classification.” In ACM Conference on Fairness, Accountability, and Transparency (FAT '19). Download
David C. Parkes and Rakesh V. Vohra. 2019. Algorithmic and Economic Perspectives on Fairness. Download
Gregory D. Hager, Ann W. Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria, Stephen F. Smith, and Milind Tambe. 2019. Artificial Intelligence for Social Good. Download
Christos Dimitrakakis, Yang Liu, David C. Parkes, and Goran Radanovic. 2019. “Bayesian Fairness.” In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI). Download
Haris Aziz, Hau Chan, Barton E. Lee, and David C. Parkes. 2019. “The Capacity Constrained Facility Location Problem.” In Proceedings of the 15th Int. Conf. on Web and Internet Economics, (WINE 2019), Pp. 336. Download
Hongyao Ma, Reshef Meir, David C. Parkes, and James Zou. 2019. “Contingent Payment Mechanisms for Resource Utilization.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), Pp. 422-430. Download
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2019. “An Exponential Speedup in Parallel Running Time for Submodular Maximization without Loss in Approximation.” In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) 2019 . Download
Paul Duetting, Felix A. Fischer, and David C. Parkes. 2019. “Expressiveness and Robustness of First-Price Position Auctions.” Mathematics of Operations Research , 44, 1, Pp. 196-211. Download
Berk Ustun, Yang Liu, and David C. Parkes. 2019. “Fairness without Harm: Decoupled Classifiers with Preference Guarantees.” In Proceedings of the 36th International Conference on Machine Learning (ICML'19), Pp. 6373-6382. Download

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