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 seeking

To 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.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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William Bainbridge
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Northwestern University at Chicago
United States
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