{"type":"ScholarlyArticle","title":"The Economics of Machine Learning","authors":[{"name":"Haifeng Xu","slug":"haifeng-xu","given":"Haifeng","family":"Xu","affiliation":"Department of Computer Science , University of Chicago"}],"abstract":"This survey    overviews  a new  research agenda on the  economics of machine learning, pursued at the Strategic IntelliGence for Machine Agent (SIGMA) Lab  at UChicago.  This overall research agenda has two themes:  machine learning for economics and, conversely, economics for machine learning (ML). The first theme focuses on designing and analyzing ML algorithms for economic problems, ranging from foundational economic models to influential real-world applications such as recommender systems and national security. The second theme  employs economic principles to study  machine learning itself, such as the valuation and pricing of data, information and ML models, and designing incentive  mechanisms to improve large-scale ML research peer reviews.  While   our research focuses primarily on developing methodologies,  in each theme we  also highlight  some real-world impacts of these works, including ongoing large-scale live experiments and potential deployments for various applications.","doi":"10.24963/ijcai.2023/807","url":"https://ijcai.org/proceedings/2023/807","pdf":"https://ijcai.org/proceedings/2023/0807.pdf","markdown":"https://ijcai.org/proceedings/2023/807.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/807","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":"Early Career","track":"Early Career","number":807,"pages":{"first":7057,"last":7061},"keywords":["EC: Machine Learning And Multi-agent Systems"],"videos":[],"references":[{"position":1,"raw":"[Bubeck et al., 2012] Sébastien Bubeck, Nicolo Cesa- Bianchi, et al. 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In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 9405-9412, 2022.","title":"When can the defender effectively deceive attackers in security games","authors":"Xu ; ] Thanh Nguyen, Haifeng Nguyen, Xu","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Su, 2021] Weijie Su. You are the best reviewer of your own papers: An owner-assisted scoring mechanism. Advances in Neural Information Processing Systems, 34:27929- 27939, 2021.","title":"You are the best reviewer of your own papers: An owner-assisted scoring mechanism","authors":"Weijie Su","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Sundaram et al., 2021] Ravi Sundaram, Anil Vullikanti, Haifeng Xu, and Fan Yao. Pac-learning for strategic clas- sification. In International Conference on Machine Learn- ing, pages 9978-9988. PMLR, 2021.","title":"Pac-learning for strategic classification","authors":"Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Tambe, 2011] Milind Tambe. Security and game theory: al- gorithms, deployed systems, lessons learned. Cambridge university press, 2011.","title":"Security and game theory: algorithms, deployed systems, lessons learned","authors":"Milind Tambe","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Wu et al., 2022a] Jibang Wu, Weiran Shen, Fei Fang, and Haifeng Xu. Inverse game theory for stackelberg games: the blessing of bounded rationality. In NeurIPS, 2022.","title":"Inverse game theory for stackelberg games: the blessing of bounded rationality","authors":"Wu","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Wu et al., 2022b] Jibang Wu, Haifeng Xu, and Fan Yao. Multi-agent learning for iterative dominance elimination: Formal barriers and new algorithms. In Conference on Learning Theory, pages 543-543. PMLR, 2022.","title":"Multi-agent learning for iterative dominance elimination: Formal barriers and new algorithms","authors":"Wu","year":"2022","venue":"Conference on Learning Theory","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Wu et al., 2023] Jibang Wu, Haifeng Xu, Yifan Guo, and Weijie J. Su. An isotonic mechanism for overlapping own- ership. ArXiv, abs/2306.11154, 2023.","title":"An isotonic mechanism for overlapping ownership","authors":"Jibang Wu, Haifeng Xu, Yifan Guo, J Weijie, Su","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Yao et al., 2023a] Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, and Haifeng Xu. How bad is top-k rec- ommendation under competing content creators? In Inter- national Conference on Machine Learning. PMLR, 2023.","title":"How bad is top-k recommendation under competing content creators?","authors":"Yao","year":"2023","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Yao et al., 2023b] Fan Yao, Chuanhao Li, Karthik Abi- nav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, and Haifeng Xu. Rethinking incentives in recommender systems: Are monotone rewards always beneficial? ArXiv, abs/2306.07893, 2023.","title":"Rethinking incentives in recommender systems: Are monotone rewards always beneficial?","authors":"Yao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zu et al., 2021] You Zu, Krishnamurthy Iyer, and Haifeng Xu. Learning to persuade on the fly: Robustness against ignorance. In Proceedings of the 22nd ACM Conference on Economics and Computation, pages 927-928, 2021.","title":"Learning to persuade on the fly: Robustness against ignorance","authors":"Zu","year":"2021","venue":"Proceedings of the 22nd ACM Conference on Economics and Computation","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}