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

在许多情况下，整个群体都面临着一个决定——例如，一组立法者可以提出一项法案来投票，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以决定是否分享一段在线内容。独立地看待个人所做的选择是非常标准的，但是，考虑到个人决策者有什么共同之处和没有共同之处，从个人是否会做出相同的决定的角度来看待决策过程，可以获得对决策更全面的科学理解。这个项目正在研究这个问题，通过开发新的计算方法来帮助更好地理解人们做出决定的原因。该项目从政治背景下共同投票的计算模型开始，从“个人如何投票，为什么投票？”到“这些人的投票方式相同吗，为什么？”它概括和扩展了这些模型，超越了政党和国家等既定因素，允许从法案中纳入非结构化语言，以描述正在审议的问题，并纳入对个人语言的分析和比较。扩展框架将通过展示来自美国国会会议记录的数据集的改进预测性能来验证，使对先前工作的直接评估成为可能，并使对政治言论和决策的新的实质性分析成为可能。在此过程中，该项目还利用识别可解释的、与任务相关的语言的技术，并结合最近开发的将协变量纳入主题分析的方法，对个人语言进行了更丰富的分析。将这些进展纳入扩大的共同表决框架内加以验证，并将有助于调查有关国会决策的实质性问题。最后，该项目将通过应用超越政治领域的方法来处理更一般的用例，从共同投票的建模转移到共同决策的建模，其中决策是一般的投票。将广义模型应用于科学引文推荐问题进行验证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.