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，以激励大量自主个体解决物理世界中需要协调的问题。在这些“物理众包”系统中，人们为更大的集体问题做出小小的贡献，例如为公民科学项目跟踪动物物种或空气质量，或者在商业应用中提供乘车或包裹递送。在这些系统中，机会主义地依赖人们做问题中方便的部分会导致解决方案不完整，而指导人们做不方便的任务则需要很高的激励。通过对任务的时间和地点进行建模，以及对人们日常生活的了解，该项目将开发算法，利用决策理论的思想来最好地决定何时、何地以及向谁建议任务，以平衡个人便利性和系统需求。这样做应该增加人们的参与意愿，减少激励参与的需要，并为集体问题创造更完整、及时、准确的解决方案。研究人员将开发几个原型系统来解决包裹投递和失物招领等实际问题，以证明他们的想法的有效性；他们还将发布一个工具包，允许其他人在设计自己的物理众包系统时使用他们的工作，以解决社会感兴趣的科学和实际问题。这项工作将分三个主要阶段进行。第一阶段将开发“激励链”，这是一种鼓励机会主义贡献者变得更加直接的策略。使用现有的树种调查原型，该团队将模拟人们的机会主义贡献和惯例，然后提出相对简单的“附近”任务，其他人已经做出了贡献，但还需要进一步的工作，从而培养人们的兴趣和贡献能力。一旦附近的协作任务完成，系统会将这些经验丰富、积极主动的贡献者引导到新的领域和任务，而这些贡献者又将成为吸引下一轮贡献者的“附近”贡献者。第二阶段将开发决策理论的“点击或等待”算法，以决定是否以及何时使用基于社区的失物招领应用程序通知贡献者有关任务的信息。基于行为例程的建模，这些算法将使用顺序决策过程来估计贡献者将完成的任务的最大总价值，考虑到个人的历史和当前状态、系统确定的附近任务的价值以及系统对贡献者响应通知的可能性有多大。第三阶段将制定通知策略，以满足系统的服务质量需求，同时最大限度地减少对贡献者的干扰。为此，研究人员将开发一个供应管理框架，该框架考虑可用助手池、当前任务需求和价值，以及一系列用于提出定向请求的政策，并在点对点包的背景下对其进行评估输送系统。