{"type":"ScholarlyArticle","title":"Online Pricing for Revenue Maximization with Unknown Time Discounting Valuations","authors":[{"name":"Weichao Mao","slug":"weichao-mao","given":"Weichao","family":"Mao","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Shanghai Jiao Tong University , China"},{"name":"Zhenzhe Zheng","slug":"zhenzhe-zheng","given":"Zhenzhe","family":"Zheng","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Shanghai Jiao Tong University , China"},{"name":"Fan Wu","slug":"fan-wu","given":"Fan","family":"Wu","affiliation":"Shanghai Key Laboratory of Scalable Computing and Systems , Shanghai Jiao Tong University , China"},{"name":"Guihai Chen","slug":"guihai-chen","given":"Guihai","family":"Chen","affiliation":"Department of Computer Science and Technology , Nanjing University , China"}],"abstract":"Online pricing mechanisms have been widely applied to resource allocation in multi-agent systems. However, most of the existing online pricing mechanisms assume buyers have fixed valuations over the time horizon, which cannot capture the dynamic nature of valuation in emerging applications. In this paper, we study the problem of revenue maximization in online auctions with unknown time discounting valuations, and model it as non-stationary multi-armed bandit optimization. We design an online pricing mechanism, namely Biased-UCB, based on unique features of the discounting valuations. We use competitive analysis to theoretically evaluate the performance guarantee of our pricing mechanism, and derive the competitive ratio. Numerical results show that our design achieves good performance in terms of revenue maximization on a real-world bidding dataset.","doi":"10.24963/ijcai.2018/61","url":"https://ijcai.org/proceedings/2018/61","pdf":"https://ijcai.org/proceedings/2018/0061.pdf","markdown":"https://ijcai.org/proceedings/2018/61.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/61","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":61,"pages":{"first":440,"last":446},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Learning","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Algorithmic Game Theory"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 1995] Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. 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