This is a collection of research and review papers of auction mechanism design through data-driven methods. The sharing principle of these references here is for research. If any authors do not want their paper to be listed here, please feel free to contact me.
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- Mosavi, Amirhosein, et al. "Comprehensive review of deep reinforcement learning methods and applications in economics." Mathematics 8.10 (2020): 1640.
- Charpentier, Arthur, Romuald Elie, and Carl Remlinger. "Reinforcement learning in economics and finance." Computational Economics (2021): 1-38.
- Bichler, Martin, et al. "Learning equilibria in symmetric auction games using artificial neural networks." Nature Machine Intelligence (2021): 1-9.
- Finocchiaro, Jessie, et al. "Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness." Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 2021.
- Dütting, Paul, et al. "Machine learning for optimal economic design." The Future of Economic Design. Springer, Cham, 2019. 495-515. Short version
- Chen, Jiafeng, and Scott Duke Kominers. "Auctions with entry versus entry in auctions." Available at SSRN (2018).
- Nedelec, Thomas, et al. "Learning in repeated auctions." arXiv preprint arXiv:2011.09365 (2020).
- Myerson, Roger B. "Optimal auction design." Mathematics of operations research 6.1 (1981): 58-73.
- Vickrey, William. "Counterspeculation, auctions, and competitive sealed tenders." The Journal of finance 16.1 (1961): 8-37.
- Clarke, Edward H. "Multipart pricing of public goods." Public choice (1971): 17-33.
- Groves, Theodore. "Incentives in teams." Econometrica: Journal of the Econometric Society (1973): 617-631.
- Roberts, Kevin. "The characterization of implementable choice rules." Aggregation and revelation of preferences 12.2 (1979): 321-348.
- Daskalakis, Constantinos, Alan Deckelbaum, and Christos Tzamos. "Mechanism design via optimal transport." Proceedings of the fourteenth ACM conference on Electronic commerce. 2013.
- Golrezaei, Negin, et al. "Boosted second price auctions: Revenue optimization for heterogeneous bidders." Available at SSRN 3016465 (2017).
- Che, Yeon-Koo, and Ian Gale. "Standard auctions with financially constrained bidders." The Review of Economic Studies 65.1 (1998): 1-21.
- Che, Yeon-Koo, and Ian Gale. "The optimal mechanism for selling to a budget-constrained buyer." Journal of Economic theory 92.2 (2000): 198-233.
- Pai, Mallesh M., and Rakesh Vohra. "Optimal auctions with financially constrained buyers." Journal of Economic Theory 150 (2014): 383-425.
- Malakhov, Alexey, and Rakesh V. Vohra. "Optimal auctions for asymmetrically budget constrained bidders." Review of Economic Design 12.4 (2008): 245-257.
- Maskin, Eric S. "Auctions, development, and privatization: Efficient auctions with liquidity-constrained buyers." European Economic Review 44.4-6 (2000): 667-681.
- Laffont, Jean-Jacques, and Jacques Robert. "Optimal auction with financially constrained buyers." Economics Letters 52.2 (1996): 181-186.
- Borgs, Christian, et al. "Multi-unit auctions with budget-constrained bidders." Proceedings of the 6th ACM Conference on Electronic Commerce. 2005.
- Bhattacharya, Sayan, et al. "Budget constrained auctions with heterogeneous items." Proceedings of the forty-second ACM symposium on Theory of computing. 2010.
- Chawla, Shuchi, David L. Malec, and Azarakhsh Malekian. "Bayesian mechanism design for budget-constrained agents." Proceedings of the 12th ACM conference on Electronic commerce. 2011.
- Ashlagi, Itai, Mark Braverman, and Avinatan Hassidim. "Ascending unit demand auctions with budget limits." Massachusetts Inst. Technol., Cambridge, MA, USA, Working Paper (2009).
- Ashlagi, Itai, et al. "Position auctions with budgets: Existence and uniqueness." (2010).
- Dobzinski, Shahar, Ron Lavi, and Noam Nisan. "Multi-unit auctions with budget limits." Games and Economic Behavior 74.2 (2012): 486-503.
- Dütting, Paul, Monika Henzinger, and Martin Starnberger. "Auctions for heterogeneous items and budget limits." ACM Transactions on Economics and Computation (TEAC) 4.1 (2015): 1-17.
- Dütting, Paul, Monika Henzinger, and Ingmar Weber. "An expressive mechanism for auctions on the web." ACM Transactions on Economics and Computation (TEAC) 4.1 (2015): 1-34.
Typically modeled as LP or search problem with constraints here.
- Conitzer, Vincent, and Tuomas Sandholm. "Automated mechanism design: Complexity results stemming from the single-agent setting." Proceedings of the 5th international conference on Electronic commerce. 2003.
- Conitzer, Vincent, and Tuomas Sandholm. "Complexity of mechanism design." arXiv preprint cs/0205075 (2002).
- Cai, Yang, Constantinos Daskalakis, and S. Matthew Weinberg. "An algorithmic characterization of multi-dimensional mechanisms." Proceedings of the forty-fourth annual ACM symposium on Theory of computing. 2012.
- Conitzer, Vincent, and Tuomas Sandholm. "Self-interested automated mechanism design and implications for optimal combinatorial auctions." Proceedings of the 5th ACM Conference on Electronic Commerce. 2004.
- Conitzer, Vincent, and Tuomas Sandholm. "Applications of automated mechanism design." (2003).
- Conitzer, Vincent, and Tuomas W. Sandholm. "Automated mechanism design with a structured outcome space." (2003).
- Conitzer, Vincent, and Tuomas Sandholm. "An algorithm for automatically designing deterministic mechanisms without payments." Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004.. IEEE, 2004.
- Sandholm, Tuomas. "Automated mechanism design: A new application area for search algorithms." International Conference on Principles and Practice of Constraint Programming. Springer, Berlin, Heidelberg, 2003.
- Heidekrüger, Stefan, et al. "Equilibrium Learning in Combinatorial Auctions: Computing Approximate Bayesian Nash Equilibria via Pseudogradient Dynamics." arXiv preprint arXiv:2101.11946 (2021).
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Dütting, Paul, et al. "Payment rules through discriminant-based classifiers." (2015): 1-41.
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Dütting, Paul, et al. "Optimal auctions through deep learning." International Conference on Machine Learning. PMLR, 2019.
To enforce additional desirable constraints:
- Feng, Zhe, Harikrishna Narasimhan, and David C. Parkes. "Deep learning for revenue-optimal auctions with budgets." Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. 2018.
- Kuo, Kevin, et al. "ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning." arXiv preprint arXiv:2010.06398 (2020).
- Peri, Neehar, et al. "PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning." arXiv preprint arXiv:2106.03215 (2021).
- Golowich, Noah, Harikrishna Narasimhan, and David C. Parkes. "Deep Learning for Multi-Facility Location Mechanism Design." IJCAI. 2018.
- Sakurai, Yuko, et al. "Deep false-name-proof auction mechanisms." International Conference on Principles and Practice of Multi-Agent Systems. Springer, Cham, 2019.
Other training architecture:
- Rahme, Jad, Samy Jelassi, and S. Matthew Weinberg. "Auction learning as a two-player game." arXiv preprint arXiv:2006.05684 (2020).
- Curry, Michael, et al. "Certifying Strategyproof Auction Networks." Advances in Neural Information Processing Systems 33 (2020).
- Rahme, Jad, et al. "A permutation-equivariant neural network architecture for auction design." arXiv preprint arXiv:2003.01497 (2020).
- Curry, Michael J., et al. "Learning Revenue-Maximizing Auctions With Differentiable Matching." arXiv preprint arXiv:2106.07877 (2021).
Regret loss design:
- Balcan, Maria-Florina, Tuomas Sandholm, and Ellen Vitercik. "Estimating approximate incentive compatibility." arXiv preprint arXiv:1902.09413 (2019).
- Deng, Yuan, et al. "A data-driven metric of incentive compatibility." Proceedings of The Web Conference 2020. 2020.
- Colini-Baldeschi, Riccardo, et al. "Envy, regret, and social welfare loss." Proceedings of The Web Conference 2020. 2020.
BIC Transform
- Cai, Yang, et al. "An Efficient $\varepsilon $-BIC to BIC Transformation and Its Application to Black-Box Reduction in Revenue Maximization." arXiv preprint arXiv:1911.10172 (2019).
- Conitzer, Vincent, et al. "Welfare-Preserving $\varepsilon $-BIC to BIC Transformation with Negligible Revenue Loss." arXiv preprint arXiv:2007.09579 (2020).
- Dütting, Paul, et al. "Optimal auctions through deep learning." International Conference on Machine Learning. PMLR, 2019. (MyersonNet and RochetNet)
- Shen, Weiran, Pingzhong Tang, and Song Zuo. "Automated mechanism design via neural networks." arXiv preprint arXiv:1805.03382 (2018).
- Brero, Gianluca, Benjamin Lubin, and Sven Seuken. "Machine learning-powered iterative combinatorial auctions." arXiv preprint arXiv:1911.08042 (2019).(another)
- Weissteiner, Jakob, and Sven Seuken. "Deep Learning—Powered Iterative Combinatorial Auctions." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 02. 2020.
- Weissteiner, Jakob, et al. "Fourier analysis-based iterative combinatorial auctions." arXiv preprint arXiv:2009.10749 (2020).
- Manisha, Padala, C. V. Jawahar, and Sujit Gujar. "Learning optimal redistribution mechanisms through neural networks." arXiv preprint arXiv:1801.08808 (2018).
- Tacchetti, Andrea, et al. "A neural architecture for designing truthful and efficient auctions." arXiv preprint arXiv:1907.05181 (2019).
- Brero, Gianluca, et al. "Reinforcement Learning of Sequential Price Mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 6. 2021.
- Nedelec, Thomas, Noureddine El Karoui, and Vianney Perchet. "Learning to bid in revenue-maximizing auctions." International Conference on Machine Learning. PMLR, 2019.
- Nedelec, Thomas, et al. "Adversarial Learning in Revenue-Maximizing Auctions" Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS '21).
- Nedelec, Thomas, et al. "Thresholding at the monopoly price: an agnostic way to improve bidding strategies in revenue-maximizing auctions." arXiv preprint arXiv:1808.06979 (2018).
- Nedelec, Thomas, et al. "Robust Stackelberg buyers in repeated auctions." International Conference on Artificial Intelligence and Statistics. PMLR, 2020.
- Shen, Weiran, et al. "Reinforcement mechanism design: With applications to dynamic pricing in sponsored search auctions." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 02. 2020.
- Zhang, Zhilin, et al. "Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising." Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM '2021).
- Liu, Xiangyu, et al. "Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising." Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21).
- Celis, Elisa, Anay Mehrotra, and Nisheeth Vishnoi. "Toward controlling discrimination in online ad auctions." International Conference on Machine Learning. PMLR, 2019.
- Lahaie, Sébastien, et al. "Testing incentive compatibility in display ad auctions." Proceedings of the 2018 World Wide Web Conference. 2018.
- Feng, Zhe, Okke Schrijvers, and Eric Sodomka. "Online learning for measuring incentive compatibility in ad auctions?." The World Wide Web Conference. 2019.
- Schlechtinger, Michael, et al. ["Winning at Any Cost--Infringing the Cartel Prohibition With Reinforcement Learning."](Schlechtinger, Michael, et al. "Winning at Any Cost--Infringing the Cartel Prohibition With Reinforcement Learning." arXiv preprint arXiv:2107.01856 (2021).) arXiv preprint arXiv:2107.01856 (2021).
- Luong, Nguyen Cong, et al. "Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach." 2018 IEEE International Conference on Communications (ICC). IEEE, 2018.
- Lee, Haemin, et al. "Auction-based Deep Learning Computation Offloading for Truthful Edge Computing: A Myerson Auction Approach." 2021 International Conference on Information Networking (ICOIN). IEEE, 2021.
- Nwogugu, Michael CI. "Complexity, Stability Properties Of Mixed Games And Dynamic Algorithms, And'Learning'In The Sharing Economy." And'Learning'In The Sharing Economy.(2017 (Revised 2019)) (2017).
Citation from paper Automated mechanism design: A new application area for search algorithms
- Viqueira, Enrique Areyan, et al. "Empirical mechanism design: Designing mechanisms from data." Uncertainty in Artificial Intelligence. PMLR, 2020.
- Kroer, Christian, and Tuomas Sandholm. "Computational Bundling for Auctions." AAMAS. 2015.
- Balcan, Maria-Florina, Tuomas Sandholm, and Ellen Vitercik. "Sample complexity of multi-item profit maximization." arXiv preprint arXiv:1705.00243 3 (2017).
- Ochal, Mateusz, et al. "Online Mechanism Design using Reinforcement Learning for Cloud Resource Allocation."
- Balcan, Maria-Florina, Siddharth Prasad, and Tuomas Sandholm. "Learning Within an Instance for Designing High-Revenue Combinatorial Auctions."
Citation from paper Complexity of Mechanism design
- Albert, Michael, Vincent Conitzer, and Peter Stone. "Automated design of robust mechanisms." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31. No. 1. 2017
- Berg, Kimmo, and Harri Ehtamo. ["Learning in nonlinear pricing with unknown utility functions."] (https://link.springer.com/content/pdf/10.1007/s10479-009-0640-2.pdf) Annals of Operations Research 172.1 (2009): 375.
- Berg, Kimmo, and Harri Ehtamo. "Continuous learning methods in two-buyer pricing problem." Mathematical Methods of Operations Research 75.3 (2012): 287-304.
- Balcan, Maria-Florina, Siddharth Prasad, and Tuomas Sandholm. "Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs." IJCAI. 2020.
- Brero, Gianluca, et al. "Reinforcement Learning of Simple Indirect Mechanisms." arXiv preprint arXiv:2010.01180 (2020).
- Dütting, Paul, et al. "Machine learning for optimal economic design." The Future of Economic Design. Springer, Cham, 2019. 495-515.
- Mguni, David, and Marcin Tomczak. "Efficient Reinforcement Dynamic Mechanism Design."
- Wang, Guanhua, et al. "Mechanism Design for Public Projects via Neural Networks." arXiv preprint arXiv:2002.11382 (2020).
Citation from paper "Designing and learning optimal finite support auctions."
- Cole, Richard, and Tim Roughgarden. "The sample complexity of revenue maximization." Proceedings of the forty-sixth annual ACM symposium on Theory of computing. 2014.
- Morgenstern, Jamie, and Tim Roughgarden. "The pseudo-dimension of near-optimal auctions." arXiv preprint arXiv:1506.03684 (2015).
- Morgenstern, Jamie, and Tim Roughgarden. "Learning simple auctions." Conference on Learning Theory. PMLR, 2016.
- Devanur, Nikhil R., Zhiyi Huang, and Christos-Alexandros Psomas. "The sample complexity of auctions with side information." Proceedings of the forty-eighth annual ACM symposium on Theory of Computing. 2016.
- Gonczarowski, Yannai A., and Noam Nisan. "Efficient empirical revenue maximization in single-parameter auction environments." Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. 2017.
- Roughgarden, Tim, and Okke Schrijvers. "Ironing in the dark." Proceedings of the 2016 ACM Conference on Economics and Computation. 2016.
- Cai, Yang, and Constantinos Daskalakis. "Learning multi-item auctions with (or without) samples." 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2017.
- Roughgarden, Tim, and Joshua R. Wang. "Minimizing regret with multiple reserves." ACM Transactions on Economics and Computation (TEAC) 7.3 (2019): 1-18.
- Balcan, Maria-Florina F., Tuomas Sandholm, and Ellen Vitercik. "Sample complexity of automated mechanism design." Advances in Neural Information Processing Systems. 2016.
- Dudík, Miroslav, et al. "Oracle-efficient online learning and auction design." 2017 ieee 58th annual symposium on foundations of computer science (focs). IEEE, 2017.
- Gonczarowski, Yannai A., and S. Matthew Weinberg. "The sample complexity of up-to-ε multi-dimensional revenue maximization." Journal of the ACM (JACM) 68.3 (2021): 1-28.
- Guo, Chenghao, Zhiyi Huang, and Xinzhi Zhang. "Settling the sample complexity of single-parameter revenue maximization." Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. 2019.
- Hartline, Jason, and Samuel Taggart. "Sample complexity for non-truthful mechanisms." Proceedings of the 2019 ACM Conference on Economics and Computation. 2019.
- Brustle, Johannes, Yang Cai, and Constantinos Daskalakis. "Multi-item mechanisms without item-independence: Learnability via robustness." Proceedings of the 21st ACM Conference on Economics and Computation. 2020.
- Bubeck, Sébastien, et al. "Multi-scale online learning: Theory and applications to online auctions and pricing." The Journal of Machine Learning Research 20.1 (2019): 2248-2284.
- Abernethy, Jacob D., et al. "Learning Auctions with Robust Incentive Guarantees." NeurIPS. 2019.
- Morgenstern, Jamie. Market Algorithms: Incentives, Learning and Privacy. Diss. Stanford University, 2015.
- Balcan, Maria-Florina, et al. "How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design." Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing. 2021.
- Vitercik, Ellen. Automated algorithm and mechanism configuration. Diss. Carnegie Mellon University, 2021.
- Schrijvers, Okke. Learning and Incentives in Computer Science. Diss. Stanford University, 2017.
Citation from paper "The sample complexity of revenue maximization."
- Medina, Andrés Muñoz, and Sergei Vassilvitskii. "Revenue optimization with approximate bid predictions." Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017
- Balkanski, Eric, Aviad Rubinstein, and Yaron Singer. "The limitations of optimization from samples." Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing. 2017.
- Mohri, Mehryar, and Andrés Munoz Medina. "Revenue Optimization against Strategic Buyers." NIPS. 2015.
- Syrgkanis, Vasilis. "A Sample Complexity Measure with Applications to Learning Optimal Auctions." Advances in Neural Information Processing Systems 30 (2017): 5352-5359.
- Bubeck, Sebastien, et al. "Online auctions and multi-scale online learning." Proceedings of the 2017 ACM Conference on Economics and Computation. 2017.
- Fu, Hu, et al. "Optimal auctions for correlated buyers with sampling." Proceedings of the fifteenth ACM conference on Economics and computation. 2014.
- Huang, Zhiyi, Jinyan Liu, and Xiangning Wang. "Learning optimal reserve price against non-myopic bidders." Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018.
- Blum, Avrim, Yishay Mansour, and Jame Morgenstern. "Learning valuation distributions from partial observation." Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
- Lahaie, Sébastien, et al. "Testing incentive compatibility in display ad auctions." Proceedings of the 2018 World Wide Web Conference. 2018.
- Balkanski, Eric, Umar Syed, and Sergei Vassilvitskii. "Statistical cost sharing." Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017.
- Trovò, Francesco, et al. "Improving multi-armed bandit algorithms in online pricing settings." International Journal of Approximate Reasoning 98 (2018): 196-235.
- Shen, Weiran, Sébastien Lahaie, and Renato Paes Leme. "Learning to clear the market." International Conference on Machine Learning. PMLR, 2019.
- Allouah, Amine, and Omar Besbes. "Sample-based optimal pricing." Available at SSRN 3334650 (2019).
- Rong, Jiang, et al. "Revenue maximization for finitely repeated ad auctions." Thirty-First AAAI Conference on Artificial Intelligence. 2017.
- Jha, Tushant, and Yair Zick. "A learning framework for distribution-based game-theoretic solution concepts." Proceedings of the 21st ACM Conference on Economics and Computation. 2020.
- Fu, Hu, et al. "Full surplus extraction from samples." Journal of Economic Theory 193 (2021): 105230.
- Mahdian, Mohammad, Vahab Mirrokni, and Song Zuo. "Incentive-aware learning for large markets." Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee. 2017
- Camara, Modibo K., Jason D. Hartline, and Aleck Johnsen. "Mechanisms for a no-regret agent: Beyond the common prior." 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2020.
- Guo, Wenshuo, Michael I. Jordan, and Manolis Zampetakis. "Robust Learning of Optimal Auctions." arXiv preprint arXiv:2107.06259 (2021).
- Feng, Zhe, and Sébastien Lahaie. "Robust Clearing Price Mechanisms for Reserve Price Optimization." arXiv preprint arXiv:2107.04638 (2021).
- Hu, Yihang, et al. "Targeting Makes Sample Efficiency in Auction Design." arXiv preprint arXiv:2105.05123 (2021).
- Curry, Michael J., et al. "Learning Revenue-Maximizing Auctions With Differentiable Matching." arXiv preprint arXiv:2106.07877 (2021).
- Automated Mechanism Design by Tuomas Sandholm
- Heymann, Benjamin. "How to bid in unified second-price auctions when requests are duplicated." Operations Research Letters 48.4 (2020): 446-451.
- Dafoe, Allan, et al. "Open problems in cooperative AI." arXiv preprint arXiv:2012.08630 (2020).
- Dimitriadis, Nikolaos, and Petros Maragos. "Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra." arXiv preprint arXiv:2011.07643 (2020).
- https://openreview.net/attachment?id=HygYmJBKwH&name=original_pdf
- “Optimal auctions with restricted allocations
- A necessary and sufficient condition for rationalizability in a quasi-linear context
- Towards Deep Learning Models Resistant to Adversarial Attacks
game 均衡 机制设计
可解释性 严格ic或者计算regret
强调deep learning的kernel通用性