Abstract

For the majority of votes that take place in Congress, over 90% of legislators' votes can be explained purely by ideology. In years when no party has a significant majority, party unity becomes imperative in the scope of advancing the party's agenda. Various theories seek to explain congressional voting behavior as a catch-all approach, and often fail to further our understanding of the inconsistencies in voting behavior. Our lack of understanding of against party-line voting hinders our ability to explain and predict voting behavior. This project seeks to utilize various machine learning techniques to gain a better understanding of what causes against party-line voting in Congress.

Advisor

van Doorn, Bas

Second Advisor

Visa, Sofia

Department

Computer Science; Political Science

Disciplines

American Politics

Publication Date

2017

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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© Copyright 2017 John W. O'Neill