CAREER: System Design of Crowd Logistics via Participatory Agent-based Modeling

这项教师早期职业发展计划（Career）资助将通过对人群物流平台设计的见解，为促进国家繁荣和经济福利做出贡献。众包物流是一个描述货物和货运的众包运输和交付的术语。寄件人通过一个由数字管理系统控制的在线平台，从个别承运人那里获得具有成本效益的物流服务。为了取得成功，平台必须迅速扩大其发送者和运营商网络。如果参与者太少，发送者和接收者将对未满足的服务请求感到不满，运营商将没有足够的机会，并且主动性将失败。该奖项通过吸引和留住具有各种经济和社会动机的参与者，支持对人群物流平台设计的基本理解，以实现稳健的网络增长和性能。从这项研究中获得的知识将有助于在面临COVID-19紧急情况等大规模中断时，利用人群物流进行有弹性的粮食分配。该教育计划旨在实施参与式建模活动，以提高STEM学生的系统思维能力，这在复杂物流系统的设计和分析中至关重要。本研究将产生一种新颖的基于主体的建模方法，以系统地探索群体物流平台的设计对其快速达到临界质量参与者的能力的影响。这种方法将模拟平台管理系统对参与者数字交换的控制程度。一端是集中控制、全自动化和算法管理的平台；另一端是一个分散控制和社区驱动的交流平台。现有的众包平台设计研究表明，随着时间的推移，控制的集中化程度会影响平台的发展。然而，理解和预测将导致平台长期增长的网络效应是具有挑战性的，因为参与者的决策和互动所形成的反馈循环产生了复杂和动态的系统行为。本研究将通过将承运人和发件人描述为自主的、异构的、自适应的代理来解决这一挑战，随着时间的推移，他们参与群体物流平台的决定会影响其他代理的参与。与人群物流从业者的参与式建模会议将用于模型验证，以确保代理行为与现实世界中的对应行为相对应。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by yielding insights into crowd logistics platform design. Crowd logistics is a term that describes crowdsourced transport and delivery of goods and freight. Senders source cost-effective logistics services from individual carriers via an online platform, which is controlled by a digital management system. To be successful, a platform must rapidly scale up its sender and carrier networks. If there are too few participants, senders and recipients will be dissatisfied by unfilled service requests, carriers will have insufficient opportunities, and the initiative will fail. This award supports a fundamental understanding of crowd logistics platform design for robust network growth and performance by attracting and retaining participants having a variety of economic and social motivations. The knowledge gained from this research will facilitate the use of crowd logistics for resilient food distribution in the face of large-scale disruptions, such as the COVID-19 emergency. The educational plan seeks to implement participatory modeling activities that will improve STEM students’ capacity for systems thinking, which is critical in the design and analysis of complex logistics systems. This research will produce a novel agent-based modeling approach to systematically explore the impacts of a crowd logistics platform’s design on its ability to quickly achieve a critical mass of participants. This approach will model the degree of control that a platform management system assumes over participants’ digital exchanges. At one end of the spectrum is a platform characterized by centralized control, full automation, and algorithmic management; at the other end is a platform with decentralized control and community-driven exchanges. Existing research on crowdsourcing platform design suggests that the degree of centralization of control impacts platform growth over time. However, understanding and predicting network effects that will lead to long-term platform growth is challenging, because the feedback loop perpetuated by participant decisions and interactions yields complex and dynamic system behavior. This research will address this challenge by representing carriers and senders as autonomous, heterogeneous, and adaptive agents, whose decisions to participate in a crowd logistics platform impact the participation of other agents over time. Participatory modeling sessions with crowd logistics practitioners will be leveraged for model validation, to ensure that agent behaviors correspond to those of their real-world counterparts.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.