RI: Small: Modeling Co-Decisions: A Computational Framework Using Language and Metadata

在许多情况下，整个群体都被呈现给一个决定--例如，一组立法者可以被提交一项法案进行投票，一组科学作者可以决定是否在他们的论文中引用一项研究，或者一组社交媒体用户可能决定是否分享一项在线内容。独立地看待个人的选择是非常标准的，但从个人是否会做出同样的决定的角度来看待决策过程，考虑到个人决策者的共同之处和不同之处，可以获得对决策的更全面的科学理解。这个项目正在通过开发新的计算方法来研究这个问题，以帮助更好地理解人们做出的决定。该项目从政治背景下联合投票的计算模型开始，从“个人如何投票，为什么投票？”“这些人是否以同样的方式投票，为什么？”它概括和扩展了这些模式，超越了政党和国家等既定因素，允许纳入法案中的非结构化语言，以表征正在审议的问题，并纳入对个人语言的分析和比较。扩展后的框架将通过对来自美国国会议事程序的数据集的改进的预测性能进行验证，使针对先前工作的直接评估成为可能，并能够对政治修辞和决策进行新的实质性分析。在这一过程中，该项目还使用确定可解释的、与任务相关的语言的技术，并通过将最近开发的将协变量纳入主题分析的方法，开发了对个人语言的更丰富的分析。这些进展将通过将它们纳入扩展的联合投票框架而得到验证，并将有助于调查有关国会决策的实质性问题。最后，该项目将通过将该方法应用于政治领域以外的领域来解决更一般的用例，从联合投票的建模转移到联合决策的建模，在这种情况下，决策是普遍的投票。通用模型将通过应用于科学引文推荐问题进行验证。该奖项反映了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。

In many settings, entire groups are presented with a decision -- for example, a set of legislators can be presented with a bill to vote on, a set of scientific authors can decide whether or not to cite a piece of research in their papers, or a set of social media users might decide whether or not to share a piece of online content. It is very standard to look independently at choices made by individuals, but a more complete scientific understanding of decision-making can be obtained by looking at the decision process in terms of whether individuals will make the same decision or not, taking into account what the individual deciders do and do not have in common. This project is looking at that question by developing new computational methods to help better understand what goes into the decisions people make.The project begins with computational models of co-voting in political contexts, moving from ''how does an individual vote, and why?'' to ''do these individuals vote the same way, and why?''. It generalizes and extends such models, going beyond established factors such as party and state, by enabling incorporation of unstructured language from bills to characterize the issues under consideration and to incorporate analysis and comparison of individuals' language. The extended framework will be validated by demonstrating improved predictive performance on datasets derived from proceedings of the U.S. Congress, making possible direct evaluation against prior work and enabling new substantive analyses of political rhetoric and decision making. In the process, the project also develops richer analysis of individuals' language using techniques that identify interpretable, task-relevant language and by incorporating recently developed methods for incorporating covariates into topic analysis. These advances will be validated by incorporating them within the extended co-voting framework, and will also contribute to the investigation of substantive questions about Congressional decision-making. Finally, the project will address more general use cases by applying the approach beyond the political domain, moving from modeling of co-voting to modeling co-decisions, where a decision is a generalized vote. The generalized model will be validated via application to the problem of scientific citation recommendation.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.