Dynamic incentive mechanism design for regulation‐aware systems

International Journal of Intelligent SystemsVolume 37, Issue 2 p. 1299-1321 RESEARCH ARTICLE Dynamic incentive mechanism design for regulation-aware systems Sixuan Dang, Sixuan Dang orcid.org/0000-0002-3241-9530 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this authorSheng Cao, Corresponding Author Sheng Cao [email protected]stc.edu.cn orcid.org/0000-0002-0929-9067 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, China Correspondence Sheng Cao, School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, Sichuan Province, China. Email: [email protected]Search for more papers by this authorJingwei Li, Jingwei Li orcid.org/0000-0001-8457-0454 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this authorXiaosong Zhang, Xiaosong Zhang orcid.org/0000-0001-8673-9284 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this author Sixuan Dang, Sixuan Dang orcid.org/0000-0002-3241-9530 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this authorSheng Cao, Corresponding Author Sheng Cao [email protected] orcid.org/0000-0002-0929-9067 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, China Correspondence Sheng Cao, School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, Sichuan Province, China. Email: [email protected]Search for more papers by this authorJingwei Li, Jingwei Li orcid.org/0000-0001-8457-0454 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this authorXiaosong Zhang, Xiaosong Zhang orcid.org/0000-0001-8673-9284 School of Computer Science and Engineering, Institute for Cyber Security, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaSearch for more papers by this author First published: 17 September 2021 https://doi.org/10.1002/int.22670Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride-hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance behaviors, such as detecting fatigue driving by computer vision or pattern recognition methods. However, few of them indicate how to efficiently exploit the detection results to regulate workers' behaviors. In this paper, we point out that workers' noncompliance behaviors and their incomes should be correlated, and propose a quantifiable computation framework that includes a price-based incentive mechanism and a method to verify the effectiveness of the mechanism. Historical behaviors of workers are summarized as credits and stored in nonfungible token called CreditToken to ensure that it cannot be tampered with. CreditToken will further affect workers' incomes. We abstract the decision-making behavior of workers as a Markov decision process and demonstrate the effectiveness of the incentive mechanism with model checking and formal methods. The analysis shows that our framework is able to provide a rational price strategy formation for gig service platforms, and can be flexibly integrated into existing pricing schemes to maximize the value of the detection results. Extensive experiments illustrate the advanced nature and practicality of our framework. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Volume37, Issue2February 2022Pages 1299-1321 RelatedInformation