[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127319-en":3,"doc-seo-127319-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127319,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Human-Centered Machine Learning in Operations Management - Thesis Abstract","Human-Centered Machine Learning in Operations Management investigates operational challenges and opportunities at the intersection of machine learning, algorithmic design, and human behavior on modern digital platforms. The dissertation studies human-in-the-loop systems where platform-level optimization can conflict with participant agency. Through empirical studies, theoretical models, and new algorithms, it improves system design and performance across retail delivery learning, task assignment with worker agency, AI matchmaking engagement effects, and inventory control for on-demand vehicle sharing under censored demand.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nHuman-Centered Machine Learning in Operations Management  \nPermalink  \n[https://escholarship.org/uc/item/6389h616](https://escholarship.org/uc/item/6389h616)  \nISBN  \n9798293893492  \nAuthor  \nJiang, Shunan  \nPublication Date  \n2025-07-01  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nHuman-Centered Machine Learning in Operations Management  \nBy  \nShunan Jiang  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy  \nin  \nEngineering-Industrial Engineering and Operations Research  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Zuo-Jun (Max) Shen, Co-Chair Assistant Professor Park Sinchaisri, Co-Chair Associate Professor Zeyu Zheng  \nSummer 2025  \nHuman-Centered Machine Learning in Operations Management  \nCopyright 2025  \nby  \nShunan Jiang  \n1  \nAbstract  \nHuman-Centered Machine Learning in Operations Management  \nby  \nShunan Jiang  \nDoctor of Philosophy in Engineering-Industrial Engineering and Operations Research  \nUniversity of California, Berkeley  \nProfessor Zuo-Jun (Max) Shen, Co-Chair  \nAssistant Professor Park Sinchaisri, Co-Chair  \nThis dissertation investigates the operational challenges and opportunities at the intersection of machine learning, algorithmic design, and human behavior in modern digital platforms. As services in human-in-the-loop systems increasingly rely on AI-driven systems, a critical tension emerges between platform-level optimization and the agency of autonomous human participants. This work addresses this tension through a series of empirical studies, theoretical models, and novel algorithms, adopting a human-centered perspective to improve system design and performance.  \nFirst, we conduct a large-scale empirical study of a retail delivery platform to understand how gig workers learn and adapt to algorithmic recommendations. Our analysis reveals that workers evolve sophisticated, personalized strategies over time, transitioning from initial reliance on platform guidance to a dynamic co-adaptation with the system.  \nSecond, motivated by these empirical findings, we formalize the problem of assigning tasks toa workforce of independent, learning agents. We introduce a novel two-phase bandit model that captures worker agency and endogenous skill development. We prove that the optimal policy is intractable and propose a practical algorithm, Combinatorial UCB (C-UCB), which we show achieves sublinear regret against a strong greedy oracle.  \nThird, we shift our focus to user engagement, examining the impact of AI-driven matchmaking in an online gaming context. We find that AI’s influence is a “double-edged sword”: while moderate AI involvement can enhance short-term engagement, particularly for novice players, excessive exposure is negatively associated with long-term retention. This highlights the need for careful calibration of AI to avoid undermining user trust and intrinsic motivation.  \nFinally, we address the classic operations problem of inventory management in the context of on-demand vehicle sharing networks with censored demand. We model the system as an  \n2  \ninfinite horizon Markov Decision Process and develop an online learning algorithm, Learning While Repositioning (LWR), that learns an effective repositioning policy despite incomplete information. We establish a sublinear regret bound for our algorithm, demonstrating its efficacy in a high-dimensional, dynamic setting.  \nCollectively, these studies contribute a multi-faceted understanding of human-algorithm interaction in operations management. By bridging empirical analysis with theoretical rigor, this dissertation provides new models and actionable insights for designing more efficient, adaptive, and human-responsive platforms.  \ni  \nTo Xuefen, Liyun, Zon","cbCaidfo1RG20RJn","https://ap.wps.com/l/cbCaidfo1RG20RJn","pdf",19520233,2,1,174,"English","en",105,"# Introduction\n## Learning on the Go: Understanding How Gig Economy Workers Learn with Recommendation Algorithms\n## Learning to Gig: Two-Phase Bandits with Arm Agency and Learning\n## The Double-Edged Sword of AI Player Matchmaking\n## Learning While Repositioning in On-Demand Vehicle Sharing Networks","[{\"question\":\"What central tension does the dissertation address in human-in-the-loop platforms?\",\"answer\":\"It addresses the tension between platform-level optimization and the agency of autonomous human participants as services increasingly rely on AI-driven systems.\"},{\"question\":\"How do gig workers adapt to algorithmic recommendations in the retail delivery study?\",\"answer\":\"Workers evolve sophisticated, personalized strategies over time, moving from initial reliance on platform guidance to dynamic co-adaptation with the system.\"},{\"question\":\"What does the dissertation show about AI-driven player matchmaking effects?\",\"answer\":\"AI’s influence is a double-edged sword: moderate involvement can improve short-term engagement for novice players, while excessive exposure is associated with lower long-term retention.\"}]","Human-Centered Machine Learning in Operations Management - 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