CHS: Small: Computer-supported Collective Deliberation for the Future of Work

本研究项目将开发和测试支持大型小组审议的审议平台和工作软件的理论设计原则。一种帮助工作场所团队审议开放式问题的新型社会技术系统--数字装配--可以提高决策的质量，并在多方利益攸关方的决策中达成共识。传统上，许多关于地方政府政策、组织战略或跨学科研究项目的最重要决策都是由首席执行官、市长或首席研究员等个人做出的。但是，考虑到多个利益攸关方的观点可以产生更好的决策和执行决策所需的更大的合规性。在过去的几十年里，关于商议的研究已经产生了严格的模型，比如涉及大量人做出政策决策的商议民意调查，然而，这些技术缓慢且昂贵，使得这些模型对于大多数现实世界的工作场所来说过于昂贵。通过支持在线社区、众包和社交媒体的最新技术进步，商议可以变得更加可行。要做到这一点，技术必须克服大型小组讨论中固有的挑战，如激励参与、捕获和整合想法、决策和信息寻找。为了促进更广泛的参与，这项研究将与一个组织变革管理团队合作，与多个利益相关者合作，改善第一代大学生的大学体验，并与当地市政府合作，获得一个多元化城市中多个利益相关者的视角，以实现其2050年环境可持续发展目标。它将开发的主要方法包括：(1)排名选择投票，以基于参与者的兴趣动态形成小组；(2)审议画布，用于捕捉和组合超越传统民意调查限制的想法；以及(3)问题跟踪，以支持决策讨论和信息寻求之间的迭代。一种新的模式将更好地理解大型团体如何利用计算技术更好地开发解决方案，并为未来工作中的多方利益攸关方决策建立必要的承诺。在回答这个问题时，该模型打开了黑匣子，以及协调和学习机制在这一过程中的基础作用，为社会计算、学习科学和政治学领域做出了贡献。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project will develop and test theoretical design principles for deliberation platforms and working software to support large-group deliberation. A new genre of socio-technical system that helps workplace teams deliberate on open-ended problems, digital assemblies, can increase the quality of decisions and consensus in multi-stakeholder decision making. Traditionally, many of the most important decisions about local government policy, or organization strategy, or interdisciplinary research projects are made by single individuals such as a CEO, mayor, or principal investigator. But taking into account the perspectives of multiple stakeholders can produce better decisions and greater compliance needed to implement decisions. In the last few decades, research on deliberation has produced rigorous models such as deliberative polls that involve large numbers of people making policy decisions, however, these techniques are slow and expensive, making these models too costly for most real-world workplaces. Deliberation can become more feasible through recent advances in technologies supporting online communities, crowdsourcing, and social media. To do so, technologies must overcome the challenges inherent in large group discussion such as motivating participation, capturing and combining ideas, decision making and information seekingTo advance broadening participation, this research will partner with an organizational change management team working with multiple stakeholders to improve the university experience for first-generation college students and with a local city government to gain the perspective of multiple stakeholders across a diverse city to achieve its 2050 environmental sustainability goals. Among the primary methods it will develop are: (1) ranked-choice voting to dynamically form groups based on participant interests, (2) deliberation canvases for capturing and combining ideas that advance beyond the limitations of traditional opinion polls, and (3) issue-tracking to support iteration between decision-making discussion and information seeking. A new model will provide a better understanding of how large groups can use computing technology to better develop solutions and build commitment necessary for multi-stakeholder decision making in the future of work. In answering this question, the model opens the black box and the fundamental role of coordination and learning mechanisms in this process, contributing to the fields of social computing, learning sciences, and political science.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.