CRII: CHS: Early Detection of Collective Misconceptions with Network-aware Machine Learning Tools

该项目构建了可用于设计网络感知机器学习工具的理论、算法和框架，旨在引发有用的多样性并提高集体预测的准确性。社会科学和经济学的研究人员知道，从基于网络的系统中涌现出集体智能的能力很大。然而，羊群效应和同源效应往往抑制了群体的智慧，极大地限制了这种潜力。这项研究通过引入一个新的框架来进一步研究复杂系统，该框架提高了我们对在社会影响下管理决策的机制的理解。以这种方式提出复杂系统理论，极大地提高了预测群体何时会为复杂问题提供准确的决策支持，以及何时会惨败的能力。此外，这项研究还有助于开发有效利用多样性的意见聚合机制。计划中的工作将导致发展，通过提供共同误解的早期预警信号，使集体情报检测工具变得实用。为了实现这些目标，研究将应用一个通用框架，其中包括：(1)帮助理解导致观察到的决策模式的社会过程的网络模型；(2)从未覆盖的过程中提取信号以识别优化集体判断准确性的信号的机器学习工具；以及(3)评估试验台，除了使用关于群体决策的不同阶段和表现的丰富的高维真实数据外，还使用模拟工具。这项研究将有助于取得具有社会意义的成果，包括：(A)了解在线投资和贷款环境中的决策，以促进服务不足的细分市场的经济增长；(B)产生关于集体判断的业绩益处的新知识；以及(C)量化有限的意见多样性与人群误解之间的联系。该项目将把包括女性和少数族裔在内的本科生与科学和工程研究的真实实践联系起来。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 project builds theory, algorithms, and frameworks that can be used to design network-aware machine learning tools aimed at eliciting useful diversity and improving the accuracy of collective forecasting. Researchers in the social and economic sciences know that there is great capacity for collective intelligence to emerge from Web-based systems. Yet herding and homophily effects often restrain the wisdom of crowds, vastly limiting this potential. The research furthers the study of complex systems by introducing a new framework that improves our understanding of the mechanisms that govern decision-making under social influence. Advancing complex systems theory in this way greatly enhances the ability to predict when crowds will provide accurate decision-making support for complex problems and when they will fail miserably. Further, the research aids the development of opinion aggregation mechanisms that efficiently capitalize on diversity. The planned work will result in developments that make collective intelligence detection tools practical by providing early warning signs of shared misconceptions. To attain these goals, the research will apply a general framework that incorporates (1) network models that help understand the social processes that lead to observed decision patterns; (2) machine learning tools that draw from uncovered processes to identify signals that optimize the accuracy of collective judgment; and (3) evaluation testbeds that use simulation tools in addition to rich high-dimensional, real-world data about the various stages and performance of group decisions. This research will contribute to societally-relevant outcomes, including: (a) understanding decision-making in online investment and lending settings to enhance the economic growth of underserved market segments; (b) generating novel knowledge about the performance benefits of collective judgment, and (c) quantifying the link between limited opinion diversity and crowd misconceptions. The project will connect undergraduate students, including women and under-represented minorities, to authentic practice in science and engineering research.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.