Three Essays on the Democratic Party

In the United States today the two parties struggle to find common ground on anything, and strong partisan attachments lead many members of the public to follow the lead of their preferred party on nearly every political decision. However, the sorting of the public does not mean that the parties are monoliths. The Democratic Party in particular is composed of a wide range of groups which often do not easily come to consensus when there are decisions to be made. The occasional lack of consensus within the Democratic Party leads to questions about how the party as a whole is able to make decisions. During the party’s primaries or when controversial policies become salient partisans are not able to rely on cues from their party as they normally would. In this dissertation I seek to determine what factors play into partisan’s candidate and policy preferences when partisanship cannot. I first examine the impact of group sentiments on candidate preferences in primary elections, then I investigate whether partisan follow the lead of politicians on salient and controversial policy proposals or whether their preferences are driven by their core political values, and finally I seek to determine the relationship between group sentiments and core political values. I show that group sentiments not only shape candidate preferences in primaries, but also that core values shape policy preferences on controversial issues and those values that people hold are in turn influenced by group sentiments. I conclude that group sentiments have a pervasive impact over a great deal of intraparty dynamics and understanding this impact and the other factors that influence intraparty dynamics would help us more fully understand political behavior

構造化データに対する予測手法：グラフ，順序，時系列

京都大学新制・課程博士博士(情報学)甲第23439号情博第769号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

The Impact of Rivalry Antecedents on Mediated Demand for an Individual Sport

In contrast to research examining the social-psychological aspects of how sport fans perceive rivalry games in team sports, far less is known regarding the impact rivalries have on mediated consumer demand, a marketing outcome of interest to sport researchers and practitioners. Guided by economic demand theory, the current study developed a model to empirically examine the impact of Tyler and Cobbs\u27 (2015) rivalry antecedents (conflict, peer, bias) on fan interest for an individual sport. The three-dimensional framework provided the foundation for the selection of thirteen rivalry-related variables, in addition to control determinants established from prior literature. Results from the estimation indicate rivalry conflict is the primary driver of demand for Ultimate Fighting Championship pay-per-view buys, while peer and bias are less influential dimensions. Short-term performance similarities (recent winning percentage) and long-term performance dissimilarities (historical winning percentage) among the main and co-main event fighters are significant to generating increased buyrates. Organizational marketing activities (i.e., event poster - defining moment) were the strongest overall predictor of pay-per-view buys. Conceptual discussion and practical implications are provided, including recommendations for future research

Music March Madness: Predicting the Winner of Locura de Marzo

Each Spring, thousands of middle and high school students enrolled in Spanish classes vote for their favorite songs in the annual Locura De Marzo competition. This alternative March Madness competition gives us an opportunity to build and test models to predict which songs will win which furthers the Hit Song Science literature. Using decision trees and support vector machine (SVM) models we find similarities with the challenge of predicting the popular NCAA Basketball bracket including the importance of seed and the difficulty in predicting a “perfect” bracke

Hope Over Experience Desirability and the Persistence of Optimism

Many important decisions hinge on expectations of future outcomes. Decisions about health, investments, and relationships all depend on predictions of the future. These expectations are often optimistic: People frequently believe that their preferred outcomes are more likely than is merited. Yet it is unclear whether optimism persists with experience and, surprisingly, whether optimism is truly caused by desire. These are important questions because life’s most consequential decisions often feature both strong preferences and the opportunity to learn. We investigated these questions by collecting football predictions from National Football League fans during each week of the 2008 season. Despite accuracy incentives and extensive feedback, predictions about preferred teams remained optimistically biased through the entire season. Optimism was as strong after 4 months as it was after 4 weeks. We exploited variation in preferences and matchups to show that desirability fueled this optimistic bias

Reflecting Voter Preferences in U.S. Presidential Primaries Using Ranked-Choice Election Systems

This paper assesses the effectiveness of various ranked-choice voting systems, as compared to the current plurality system, in reflecting aggregate voter preferences in United States presidential primary elections. Survey data, gathered in February 2020 by FairVote, elicited the ranked preferences of a random sample of likely Democratic primary voters for eight remaining candidates in the election at that point. I use these data to estimate a model of voter preferences and to simulate various election outcomes, including a simple plurality election and various types of ranked-choice elections. Voter preferences, which are analyzed using a multinomial logit choice model, inform the social welfare for the voting population that each candidate provides. Using these estimates of social welfare for each candidate, I assess how each ranked-choice election system compares to the current plurality system and which systems best reflect aggregate voter preferences. I also evaluate each system on the basis of various desirable criteria for practical application. I find that certain RCV mechanisms provide, in some instances, higher social utility for the voting population and that they meet desirable criteria better than does the simple plurality mechanism.Bachelor of Scienc

The delegate paradox: Why polarized politicians can represent citizens best

Many advocate for political reforms intended to resolve apparent disjunctures between politicians’ ideologically polarized policy positions and citizens’ less polarized policy preferences. We show these apparent disjunctures can arise even when politicians represent their constituencies well and that resolving them would likely degrade representation. These counterintuitive results arise from a paradox whereby polarized politicians can best represent constituencies composed of citizens with idiosyncratic preferences. We document this paradox among US House members, often criticized for excessive polarization. We show that if House members represented their constituencies’ preferences as closely as possible, they would still appear polarized. Moreover, current members nearly always represent their constituencies better than counterfactual less polarized members. A series of experiments confirms that even “moderate” citizens usually prefer ostensibly polarized representatives to many less polarized alternatives

Mason: Real-time NBA Matches Outcome Prediction

abstract: The National Basketball Association (NBA) is the most popular basketball league in the world. The world-wide mighty high popularity to the league leads to large amount of interesting and challenging research problems. Among them, predicting the outcome of an upcoming NBA match between two specific teams according to their historical data is especially attractive. With rapid development of machine learning techniques, it opens the door to examine the correlation between statistical data and outcome of matches. However, existing methods typically make predictions before game starts. In-game prediction, or real-time prediction, has not yet been sufficiently studied. During a match, data are cumulatively generated, and with the accumulation, data become more comprehensive and potentially embrace more predictive power, so that prediction accuracy may dynamically increase with a match goes on. In this study, I design game-level and player-level features based on realtime data of NBA matches and apply a machine learning model to investigate the possibility and characteristics of using real-time prediction in NBA matches.Dissertation/ThesisMasters Thesis Computer Science 201