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蒋忠中，现任东北大学工商管理学院院长、教授(破格)、博导，入选国家“万人计划”青年拔尖人才，行为与服务运作管理研究所所长；曾任美国明尼苏达大学访问教授，国家自然科学基金委创新研究群体及国际重大合作项目骨干成员；兼任国际期刊International Journal of Engineering Business Management副主编、中国运筹学会随机服务与运作管理分会常务理事、中国运筹学会行为运筹与管理分会常务理事、中国优选法统筹法与经济数学研究会服务科学与运作管理分会常务理事、中国管理科学与工程学会理事、辽宁省工业和信息化厅服务型制造专家等。
近年来，围绕电子商务与共享经济、行为运作与收益管理、物流与供应链优化、服务运作与服务型制造等领域承担国家自然科学基金青年、面上及重点项目等多项；在MSOM、NRL、TRB、EJOR、管理科学学报等国内外顶级/重要学术期刊发表论著60余篇，获省部级优秀成果奖12项及省部级领导批示2项；荣获辽宁青年科技奖、辽宁省“兴辽英才计划”首届青年拔尖人才、辽宁省“百千万人才工程”百层次人才、辽宁省高校杰出青年学者、沈阳市高层次领军人才、沈阳市青年岗位能手等荣誉。
时间：2020年10月25日，上午9:00
地点：科学园2H栋308室
讲座题目：Making the Most of Your Regret: Workers’ Relocation Decisions in On-Demand Platforms
讲座摘要：We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. While these platforms allow workers to choose their own working hours, they have limited leverage in maintaining availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers’ relocation decisions and ultimately the system performance. A combination of behavioral modeling and controlled lab experiments is used in this study. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled lab experiments. Results show that regret aversion plays an important role in workers’ relocation decisions. Regret averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers’ regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system.
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行政办公室
2020年10月21日