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

University Of Maryland, College Park, College Park MD

Investigators

Abstract

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.