CHS: Small: Coordination of Opportunistic Actions to Produce Globally Effective Behaviors for Physical Crowdsourcing

CHS：小型：协调机会主义行动以产生全球有效的实体众包行为

• 批准号: 1618096
• 负责人: Haoqi Zhang
• 金额: $ 49.64万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618096&HistoricalAwards=false
• 关键词: CHS; Small; Coordination; Opportunistic; Actions

This project will develop theory, algorithms, and example systems for motivating large numbers of autonomous individuals to solve problems that require coordination in the physical world. In these "physical crowdsourcing" systems, people make small contributions toward a larger collective problem, such as tracking animal species or air quality for citizen science projects or providing rides or package delivery in commercial applications. In these systems, opportunistically relying on people to do convenient parts of the problem causes incomplete solutions, while directing people to do inconvenient tasks requires high incentives. By modeling the timing and location of tasks, along with knowledge of people's routines, the project will develop algorithms that make use of ideas from decision theory to best decide when, where and to whom to suggest tasks, in a way that balances individual convenience with system needs. Doing this should increase people's willingness to participate, reduce the need to incentivize participation, and create more complete, timely, and accurate solutions to the collective problem. The investigators will develop several prototype systems that address practical problems such as package delivery and lost and found searches to demonstrate the effectiveness of their ideas; they will also release a toolkit that allows other people to use their work when designing their own physical crowdsourcing systems for solving both scientific and practical problems of interest to society. The work will be carried out in three main phases. The first phase will develop "incentive chaining", a strategy to encourage opportunistic contributors to become more directable. Using an existing prototype for surveying tree species, the team will model people's opportunistic contributions and routines, then suggest relatively easy "nearby" tasks where others have contributed but where further work is needed, building people's interest and capacity to contribute. Once the nearby collaborative tasks are done, the system will direct these experienced and motivated contributors toward new areas and tasks, which will in turn become the "nearby" contributions that attract the next round of contributors. The second phase will develop decision-theoretic hit-or-wait algorithms to decide if and when to notify contributors about tasks, using a community-based lost and found application. Building on the modeling of behavioral routines, these algorithms will use sequential decision processes to estimate the maximize total value of tasks that contributors will complete, given individuals' history and current state, the system-determined value of nearby tasks, and the system's estimate of how likely the contributor would be to respond to a notification. The third phase will develop notification policies that satisfy the system's quality of service needs while minimizing disruption to contributors. To do this, the investigators will develop a supply management framework that considers the pool of available helpers, the current task demand and values, and a range of policies for making directed requests and evaluate it in the context of a peer-to-peer package delivery system.

该项目将开发理论，算法和示例系统，用于激励大量自主个体解决需要在物理世界中协调的问题。在这些“物理众包”系统中，人们为更大的集体问题做出了少量贡献，例如跟踪动物物种或公民科学项目的空气质量，或在商业应用中提供乘车或包装交付。在这些系统中，机会主义地依靠人们进行问题的方便部分会导致解决方案不完整，同时指示人们执行不便的任务需要高度激励措施。通过对任务的时机和位置进行建模，以及对人们的日常工作的了解，该项目将开发算法，这些算法利用决策理论的想法，以最好地决定何时，何处和向谁建议任务，以平衡个人便利与系统需求的方式。这样做应该增加人们参与的意愿，减少激励参与的需求，并为集体问题创造更完整，及时和准确的解决方案。研究人员将开发几种原型系统，这些系统解决了实用问题，例如包装交付，并丢失并找到了搜索以证明其思想的有效性；他们还将发布一个工具包，该工具包使其他人在设计自己的物理众包系统来解决社会感兴趣的科学和实践问题时可以利用自己的工作。这项工作将分为三个主要阶段。第一阶段将开发“激励链接”，这是一种鼓励机会主义贡献者变得更加引导的策略。该团队使用现有的原型进行测量树种，将建模人们的机会主义贡献和常规，然后建议相对容易的“附近”任务，但需要进一步的工作，但需要进一步的工作，建立人们的兴趣和能力。一旦完成了附近的协作任务，系统将把这些经验丰富且积极进取的贡献者指导到新的领域和任务，这反过来将成为吸引下一轮贡献者的“附近”贡献。第二阶段将使用基于社区的丢失和发现的应用程序来决定是否以及何时将贡献者通知贡献者有关任务的决策理论。这些算法以行为例程的建模为基础，将使用顺序决策过程来估计鉴于个人的历史和当前状态，附近任务的系统确定的价值以及系统的估计，贡献者将完成贡献者将完成的任务的总价值以及该贡献者对通知的响应的可能性的估计。第三阶段将制定满足系统服务需求质量的通知政策，同时最大程度地减少对贡献者的破坏。为此，调查人员将开发一个供应管理框架，以考虑可用助手的库，当前的任务需求和价值观以及一系列制定定向请求的政策，并在点对点包装交付系统中进行评估。