{"type":"ScholarlyArticle","title":"Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining","authors":[{"name":"Lvye Cui","slug":"lvye-cui","given":"Lvye","family":"Cui","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology cui"},{"name":"Haoran Yu","slug":"haoran-yu","given":"Haoran","family":"Yu","affiliation":"School of Computer Science & Technology , Beijing Institute of Technology cui"}],"abstract":"Inferring bargainers' private valuations on items from their decisions is crucial for analyzing their strategic behaviors in bilateral sequential bargaining. Most existing approaches that infer agents' private information from observable data either rely on strong equilibrium assumptions or require a careful design of agents' behavior models. To overcome these weaknesses, we propose a Bayesian Learning-based Valuation Inference (BLUE) framework. Our key idea is to derive feasible intervals of bargainers' private valuations from their behavior data, using the fact that most bargainers do not choose strictly dominated strategies. We leverage these feasible intervals to guide our inference. Specifically, we first model each bargainer's behavior function (which maps his valuation and bargaining history to decisions) via a recurrent neural network. Second, we learn these behavior functions by utilizing a novel loss function defined based on feasible intervals. Third, we derive the posterior distributions of bargainers' valuations according to their behavior data and learned behavior functions. Moreover, we account for the heterogeneity of bargainer behaviors, and propose a clustering algorithm (K-Loss) to improve the efficiency of learning these behaviors. Experiments on both synthetic and real bargaining data show that our inference approach outperforms baselines.","doi":"10.24963/ijcai.2023/292","url":"https://ijcai.org/proceedings/2023/292","pdf":"https://ijcai.org/proceedings/2023/0292.pdf","markdown":"https://ijcai.org/proceedings/2023/292.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/292","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Game Theory and Economic Paradigms","track":"Main Track","number":292,"pages":{"first":2624,"last":2632},"keywords":["Game Theory and Economic Paradigms: GTEP: Noncooperative games","Game Theory and Economic Paradigms: GTEP: Other","Game Theory and Economic Paradigms: GTEP: Auctions and market-based systems"],"videos":[],"references":[{"position":1,"raw":"[Athey and Haile, 2002] Susan Athey and Philip A Haile. Identification of standard auction models. Econometrica, 70(6):2107-2140, 2002.","title":"Identification of standard auction models","authors":"Haile Athey, Susan Athey, Philip A Haile","year":"2002","venue":"Econometrica","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Athey and Nekipelov, 2010] Susan Athey and Denis Nekipelov. A structural model of sponsored search advertising auctions. In Proceedings of the Sixth Ad Auctions Workshop, volume 15, 2010.","title":"A structural model of sponsored search advertising auctions","authors":"Nekipelov Athey, Susan Athey, Denis Nekipelov","year":"2010","venue":"Proceedings of the Sixth Ad Auctions Workshop","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Baarslag et al., 2016] Tim Baarslag, Mark JC Hendrikx, Koen V Hindriks, and Catholijn M Jonker. Learning about the opponent in automated bilateral negotiation: a com- prehensive survey of opponent modeling techniques. Au- tonomous Agents and Multi-Agent Systems, 30:849-898, 2016.","title":"Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques","authors":"Tim Baarslag, J C Mark, Koen V Hendrikx, Catholijn M Hindriks, Jonker","year":"2016","venue":"Autonomous Agents and Multi-Agent Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Backus et al., 2020] Matthew Backus, Thomas Blake, Brad Larsen, and Steven Tadelis. Sequential bargaining in the field: Evidence from millions of online bargaining interac- tions. The Quarterly Journal of Economics, 135(3):1319- 1361, 2020.","title":"Sequential bargaining in the field: Evidence from millions of online bargaining interactions","authors":"Backus","year":"2020","venue":"The Quarterly Journal of Economics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bajari et al., 2013] Patrick Bajari, Han Hong, and Denis Nekipelov. Game theory and econometrics: A survey of some recent research. In Advances in Economics and Econometrics, 10th World Congress, volume 3, pages 3- 52, 2013.","title":"Game theory and econometrics: A survey of some recent research","authors":"Patrick Bajari, Han Hong, Denis Nekipelov","year":"2013","venue":"Advances in Economics and Econometrics, 10th World Congress","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Blum et al., 2014] Avrim Blum, Nika Haghtalab, and Ariel D Procaccia. Learning optimal commitment to over- come insecurity. Advances in Neural Information Process- ing Systems, 27, 2014.","title":"Learning optimal commitment to overcome insecurity","authors":"Avrim Blum, Nika Haghtalab, Ariel D Procaccia","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Boutilier et al., 2006] Craig Boutilier, Relu Patrascu, Pas- cal Poupart, and Dale Schuurmans. Constraint-based opti- mization and utility elicitation using the minimax decision criterion. Artificial Intelligence, 170(8-9):686-713, 2006.","title":"Constraint-based optimization and utility elicitation using the minimax decision criterion","authors":"Craig Boutilier, Relu Patrascu, Pascal Poupart, Dale Schuurmans","year":"2006","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen and Weiss, 2012] Siqi Chen and Gerhard Weiss. An efficient and adaptive approach to negotiation in complex environments. In Proceedings of the 20th European Con- ference on Artificial Intelligence, volume 242, pages 228- 233, 2012.","title":"An efficient and adaptive approach to negotiation in complex environments","authors":"Chen, Weiss, Siqi Chen, Gerhard Weiss","year":"2012","venue":"Proceedings of the 20th European Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[de Jonge, 2022] Dave de Jonge. An analysis of the linear bi- lateral anac domains using the micro benchmark strategy. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, pages 23-29, 2022.","title":"An analysis of the linear bilateral anac domains using the micro benchmark strategy","authors":"; Dave De Jonge, Jonge De","year":"2022","venue":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2022/32"},{"position":10,"raw":"[Goeree and Holt, 2001] Jacob K Goeree and Charles A Holt. Ten little treasures of game theory and ten intuitive contradictions. American Economic Review, 91(5):1402- 1422, 2001.","title":"Ten little treasures of game theory and ten intuitive contradictions","authors":"K Jacob, Charles A Goeree, Holt","year":"2001","venue":"American Economic Review","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gopalakrishnan et al., 2018] Raga Gopalakrishnan, Theja Tulabandhula, and Koyel Mukherjee. Sequential individ- ual rationality in dynamic ridesharing. Available at SSRN 3288319, 2018.","title":"Sequential individual rationality in dynamic ridesharing","authors":"Raga Gopalakrishnan, Theja Tulabandhula, Koyel Mukherjee","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gujral, 2016] Kritee Gujral. Bargaining vs. Posted Prices: An Analysis Using the eBay Automobile Market. PhD the- sis, University of Florida, 2016.","title":"Kritee Gujral. Bargaining vs. Posted Prices: An Analysis Using the eBay Automobile Market","authors":"Gujral","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Guo and Sanner, 2010] Shengbo Guo and Scott Sanner. Real-time multiattribute bayesian preference elicitation with pairwise comparison queries. In Proceedings of the Thirteenth International Conference on Artificial Intelli- gence and Statistics, pages 289-296, 2010.","title":"Real-time multiattribute bayesian preference elicitation with pairwise comparison queries","authors":"Sanner Guo, Shengbo Guo, Scott Sanner","year":"2010","venue":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hartigan and Wong, 1979] John A Hartigan and Manchek A Wong. Algorithm as 136: A k-means clustering algorithm. Journal of The Royal Statistical Society. Series C (Applied Statistics), 28(1):100-108, 1979.","title":"Algorithm as 136: A k-means clustering algorithm","authors":"A John, Manchek A Hartigan, Wong","year":"1979","venue":"Journal of The Royal Statistical Society. Series C (Applied Statistics)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[He et al., 2016] Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. Dual learn- ing for machine translation. Advances in Neural Informa- tion Processing Systems, 29, 2016.","title":"Dual learning for machine translation","authors":"Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, Wei-Ying Ma","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hindriks and Tykhonov, 2008] Koen Hindriks and Dmytro Tykhonov. Opponent modelling in automated multi-issue negotiation using bayesian learning. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent systems -Volume 1, pages 331-338, 2008.","title":"Opponent modelling in automated multi-issue negotiation using bayesian learning","authors":"Tykhonov Hindriks, Koen Hindriks, Dmytro Tykhonov","year":"2008","venue":"Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Honorio and Ortiz, 2015] Jean Honorio and Luis E Ortiz. Learning the structure and parameters of large-population graphical games from behavioral data. J. Mach. Learn. Res., 16(1):1157-1210, 2015.","title":"Learning the structure and parameters of large-population graphical games from behavioral data","authors":"Ortiz Honorio, Jean Honorio, Luis E Ortiz","year":"2015","venue":"J. Mach. Learn. Res","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Huang et al., 2013] He Huang, Robert J Kauffman, Hongyan Xu, and Lan Zhao. A hybrid mechanism for heterogeneous e-procurement involving a combinatorial auction and bargaining. Electronic Commerce Research and Applications, 12(3):181-194, 2013.","title":"A hybrid mechanism for heterogeneous e-procurement involving a combinatorial auction and bargaining","authors":"Huang","year":"2013","venue":"Electronic Commerce Research and Applications","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Jiang and Leyton-Brown, 2007] Albert Xin Jiang and Kevin Leyton-Brown. Bidding agents for online auctions with hidden bids. Machine Learning, 67(1):117-143, 2007.","title":"Bidding agents for online auctions with hidden bids","authors":"Albert Xin, Jiang, Kevin Leyton-Brown","year":"2007","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kagel and Roth, 2020] John H Kagel and Alvin E Roth. The handbook of experimental economics, volume 2. Princeton university press, 2020.","title":"The handbook of experimental economics","authors":"H John, Alvin E Kagel, Roth","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Ling et al., 2018] Chun Kai Ling, Fei Fang, and J Zico Kolter. What game are we playing? end-to-end learning in normal and extensive form games. In Proceedings of the 27th International Joint Conference on Artificial Intel- ligence, pages 396-402, 2018.","title":"What game are we playing? end-to-end learning in normal and extensive form games","authors":"Chun Kai, Ling, Fei Fang, J Zico Kolter","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/55"},{"position":22,"raw":"[Ling et al., 2019] Chun Kai Ling, Fei Fang, and J Zico Kolter. Large scale learning of agent rationality in two- player zero-sum games. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 33, pages 6104- 6111, 2019.","title":"Large scale learning of agent rationality in twoplayer zero-sum games","authors":"Chun Kai, Ling, Fei Fang, J Zico Kolter","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Luce and Raiffa, 1989] R Duncan Luce and Howard Raiffa. Games and decisions: Introduction and critical survey. Courier Corporation, 1989.","title":"Games and decisions: Introduction and critical survey","authors":"Luce Duncan, Howard Raiffa","year":"1989","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Maschler et al., 2020] Michael Maschler, Shmuel Zamir, and Eilon Solan. Game theory. Cambridge University Press, 2020.","title":"Game theory","authors":"Michael Maschler, Shmuel Zamir, Eilon Solan","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Nekipelov et al., 2015] Denis Nekipelov, Vasilis Syrgkanis, and Eva Tardos. Econometrics for learning agents. In Pro- ceedings of the Sixteenth ACM Conference on Economics and Computation, pages 1-18, 2015.","title":"Econometrics for learning agents","authors":"Denis Nekipelov, Vasilis Syrgkanis, Eva Tardos","year":"2015","venue":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Nisan and Noti, 2017] Noam Nisan and Gali Noti. An ex- perimental evaluation of regret-based econometrics. In Proceedings of the 26th International Conference on World Wide Web, pages 73-81, 2017.","title":"Noam Nisan and Gali Noti. An experimental evaluation of regret-based econometrics","authors":"Noti Nisan","year":"2017","venue":"Proceedings of the 26th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Noti and Syrgkanis, 2021] Gali Noti and Vasilis Syrgkanis. Bid prediction in repeated auctions with learning. In Pro- ceedings of the Web Conference, pages 3953-3964, 2021.","title":"Bid prediction in repeated auctions with learning","authors":"Syrgkanis Noti, Gali Noti, Vasilis Syrgkanis","year":"2021","venue":"Proceedings of the Web Conference","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Noti, 2021] Gali Noti. From behavioral theories to econo- metrics: Inferring preferences of human agents from data on repeated interactions. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 35, pages 5637- 5646, 2021.","title":"From behavioral theories to econometrics: Inferring preferences of human agents from data on repeated interactions","authors":"Gali Noti, Noti","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Qin et al., 2019] Xinghong Qin, Qiang Su, Samuel H Huang, Uco Jillert Wiersma, and Ming Liu. Service qual- ity coordination contracts for online shopping service sup- ply chain with competing service providers: integrating fairness and individual rationality. Operational Research, 19(1):269-296, 2019.","title":"Service quality coordination contracts for online shopping service supply chain with competing service providers: integrating fairness and individual rationality","authors":"Qin","year":"2019","venue":"Operational Research","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Qin, 2020] Tao Qin. Dual Learning. Springer, 2020.","title":"Dual Learning","authors":"Tao Qin","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Raiffa, 1982] Howard Raiffa. The art and science of nego- tiation. Harvard University Press, 1982.","title":"Howard Raiffa. The art and science of negotiation","authors":"Raiffa","year":"1982","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Simonyan and Zisserman, 2014] Karen Simonyan and An- drew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Thomas and Wilson, 2002] Charles J Thomas and Bart J Wilson. A comparison of auctions and multilateral ne- gotiations. RAND Journal of Economics, pages 140-155, 2002.","title":"A comparison of auctions and multilateral negotiations","authors":"J Charles, Bart J Thomas, Wilson","year":"2002","venue":"RAND Journal of Economics","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Thomas and Wilson, 2014] Charles J Thomas and Bart J Wilson. Horizontal product differentiation in auctions and multilateral negotiations. Economica, 81(324):768-787, 2014.","title":"Horizontal product differentiation in auctions and multilateral negotiations","authors":"J Charles, Bart J Thomas, Wilson","year":"2014","venue":"Economica","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"Hal R Varian. Position auctions. Interna- tional Journal of Industrial Organization, 25(6):1163- 1178, 2007.","title":"Position auctions","authors":"Hal R Varian","year":"2007","venue":"International Journal of Industrial Organization","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Vendrov et al., 2020] Ivan Vendrov, Tyler Lu, Qingqing Huang, and Craig Boutilier. Gradient-based optimization for bayesian preference elicitation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 10292-10301, 2020.","title":"Gradient-based optimization for bayesian preference elicitation","authors":"Ivan Vendrov, Tyler Lu, Qingqing Huang, Craig Boutilier","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Viappiani and Boutilier, 2010] Paolo Viappiani and Craig Boutilier. Optimal bayesian recommendation sets and my- opically optimal choice query sets. Advances in neural information processing systems, 23, 2010.","title":"Paolo Viappiani and Craig Boutilier. Optimal bayesian recommendation sets and myopically optimal choice query sets","authors":"Boutilier Viappiani","year":"2010","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Vincent et al., 2010] Pascal Vincent, Hugo Larochelle, Is- abelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou. Stacked denoising autoencoders: Learn- ing useful representations in a deep network with a lo- cal denoising criterion. Journal of Machine Learning Re- search, 11(12), 2010.","title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","authors":"Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, Léon Bottou","year":"2010","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Vorobeychik et al., 2007] Yevgeniy Vorobeychik, Michael P Wellman, and Satinder Singh. Learning payoff functions in infinite games. Machine Learning, 67(1):145-168, 2007.","title":"Learning payoff functions in infinite games","authors":"Yevgeniy Vorobeychik, Satinder Michael P Wellman, Singh","year":"2007","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Waugh et al., 2011] Kevin Waugh, Brian D Ziebart, and J Andrew Bagnell. Computational rationalization: The inverse equilibrium problem. In Proceedings of the 28th International Conference on Machine Learning, pages 1169-1176, 2011.","title":"Computational rationalization: The inverse equilibrium problem","authors":"Kevin Waugh, Brian D Ziebart, Andrew Bagnell","year":"2011","venue":"Proceedings of the 28th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Williams et al., 2011] Colin R Williams, Valentin Robu, Enrico H Gerding, and Nicholas R Jennings. Using gaus- sian processes to optimise concession in complex negoti- ations against unknown opponents. In Proceedings of the 22nd International Joint Conference on Artificial Intelli- gence, pages 432-438, 2011.","title":"Using gaussian processes to optimise concession in complex negotiations against unknown opponents","authors":"Colin R Williams, Valentin Robu, Enrico H Gerding, Nicholas R Jennings","year":"2011","venue":"Proceedings of the 22nd International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2011/80"},{"position":42,"raw":"[Wu et al., 2022] Jibang Wu, Weiran Shen, Fei Fang, and Haifeng Xu. Inverse game theory for stackelberg games: the blessing of bounded rationality. Advances in Neural Information Processing Systems, 2022.","title":"Inverse game theory for stackelberg games: the blessing of bounded rationality","authors":"Wu","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}