{"type":"ScholarlyArticle","title":"Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy","authors":[{"name":"Pengzhan Zhou","slug":"pengzhan-zhou","given":"Pengzhan","family":"Zhou","affiliation":"Dept. of Electrical and Computer Engineering , Stony Brook University , Stony Brook , NY 11794 , USA"},{"name":"Xin Wei","slug":"xin-wei","given":"Xin","family":"Wei","affiliation":"Dept. of Computer Science , Old Dominion University , Norfolk , VA 23529 , USA"},{"name":"Cong Wang","slug":"cong-wang","given":"Cong","family":"Wang","affiliation":"Dept. of Computer Science , Old Dominion University , Norfolk , VA 23529 , USA"},{"name":"Yuanyuan Yang","slug":"yuanyuan-yang","given":"Yuanyuan","family":"Yang","affiliation":"Dept. of Electrical and Computer Engineering , Stony Brook University , Stony Brook , NY 11794 , USA"}],"abstract":"Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users' cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users' cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.","doi":"10.24963/ijcai.2019/94","url":"https://ijcai.org/proceedings/2019/94","pdf":"https://ijcai.org/proceedings/2019/0094.pdf","markdown":"https://ijcai.org/proceedings/2019/94.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/94","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":94,"pages":{"first":665,"last":671},"keywords":["Multidisciplinary Topics and Applications: Transportation","Machine Learning: Reinforcement Learning","Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Machine Learning Applications: Applications of Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"[Archer and Tardos, 2007] Aaron Archer and Éva Tardos. 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