FW-HTF-RM: Collaborative Research: Understanding the Algorithmic Workplace: A Multi-Method Study for Comprehensive Optimization of Platforms

FW-HTF-RM：协作研究：理解算法工作场所：平台综合优化的多方法研究

• 批准号: 1928608
• 负责人: H.C. Robinson
• 金额: $ 119.52万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928608&HistoricalAwards=false
• 关键词: FW; HTF; RM; Collaborative; Research

The emergence of the digital platform economy in recent years is rapidly transforming some kinds of economic activity. This project will develop new evidence about the workers, business organizations, and government institutions that are involved in the "algorithmic workplace" or "gig economy". The research team includes experts in social science, public policy, and engineering. The team will conduct research to document the experiences, attitudes, and needs of platform workers. They will use these findings to build a mathematical model of worker behavior that will be combined with business data from an industrial partner. The result will be a model that can predict how changes in business operations will affect worker outcomes. The team will use field experiments to test the model predictions. The results of this project will be new knowledge for businesses that need to retain and motivate their workforce, new evidence for policymakers that seek to understand how neighborhoods and communities are affected by the growth of platform work, and new empirical evidence about the benefits and risks of platform work for the US workforce.The team will undertake an interdisciplinary program of research aimed at generating theoretical and practical knowledge of issues that result from the advent of the algorithmic workplace in the United States. The project begins by conducting research on the attitudinal and behavioral characteristics of platform workers. Interviews and surveys will shed light on the occupational trajectories, experiences of risk, and regulatory preferences found among workers on several types of labor platforms. The team will analyze the qualitative data to shed new light over ongoing debates about the nature of algorithmic control of workers, the labor market dynamics which the platform economy fosters, and the policy preferences of workers themselves. A mathematical framework of worker behavior will be designed to model the fusion of these coded qualitative data with internal data from Deliv, a digital platform for package delivery. This will lead to a novel contribution of this project: agent-based modeling that can simulate the action of platform workers as they respond to shifts in the design of the algorithms on which platforms rely. This second stage of the research will be used to provide a deeper and better informed understanding of the strategies which workers employ as they respond to the technological changes programmers introduce in their efforts to refine the algorithms governing transactions made possible by platforms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，数字平台经济的出现正在迅速改变某些经济活动。该项目将开发有关参与“算法工作场所”或“零工经济”的工人，商业组织和政府机构的新证据。研究团队包括社会科学、公共政策和工程方面的专家。该团队将进行研究，以记录平台工作人员的经验、态度和需求。他们将利用这些发现建立一个工人行为的数学模型，并将其与工业合作伙伴的业务数据相结合。其结果将是一个模型，可以预测业务运营的变化将如何影响工人的成果。该团队将使用现场实验来测试模型预测。该项目的成果将为需要留住和激励员工的企业提供新的知识，为寻求了解社区和社区如何受到平台工作增长影响的政策制定者提供新的证据，以及关于平台工作对美国劳动力的好处和风险的新的经验证据。该团队将开展一项旨在产生理论和实践知识的跨学科研究计划算法工作场所在美国的出现所带来的问题。该项目首先对平台工人的态度和行为特征进行研究。访谈和调查将揭示职业轨迹，风险体验以及几种劳动力平台上工人的监管偏好。该团队将分析定性数据，为正在进行的关于工人算法控制的性质，平台经济促进的劳动力市场动态以及工人本身的政策偏好的辩论提供新的见解。将设计一个工人行为的数学框架，以模拟这些编码的定性数据与包裹交付数字平台Deliv的内部数据的融合。 这将导致该项目的一个新的贡献：基于代理的建模，可以模拟平台工作人员的行动，因为他们响应平台依赖的算法设计的变化。该研究的第二阶段将用于提供更深入和更明智的理解，以了解工作人员在应对程序员在努力完善管理平台交易的算法时所引入的技术变化时所采用的策略。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.