scholarly journals An Evolutionary View of the U.S. Supreme Court

2021 ◽  
Vol 26 (2) ◽  
pp. 37
Author(s):  
Noah Giansiracusa
Keyword(s):  
Supreme Court ◽  
Voting Behavior ◽  
Evolutionary Biology ◽  
Ideal Point ◽  
The United States ◽  
Point Estimation ◽  
Hyperbolic Metrics ◽  
Tree Estimation ◽  
Evolutionary View ◽  
Euclidean Setting

The voting patterns of the nine justices on the United States Supreme Court continue to fascinate and perplex observers of the Court. While it is commonly understood that the division of the justices into a liberal branch and a conservative branch inevitably drives many case outcomes, there are finer, less transparent divisions within these two main branches that have proven difficult to extract empirically. This study imports methods from evolutionary biology to help illuminate the intricate and often overlooked branching structure of the justices’ voting behavior. Specifically, phylogenetic tree estimation based on voting disagreement rates is used to extend ideal point estimation to the non-Euclidean setting of hyperbolic metrics. After introducing this framework, comparing it to one- and two-dimensional multidimensional scaling, and arguing that it flexibly captures important higher-dimensional voting behavior, a handful of potential ways to apply this tool are presented. The emphasis throughout is on interpreting these judicial trees and extracting qualitative insights from them.

Nonparametric Ideal-Point Estimation and Inference

2018 ◽  
Vol 26 (2) ◽  
pp. 131-146 ◽  
Author(s):  
Alexander Tahk
Keyword(s):  
Supreme Court ◽  
Voting Behavior ◽  
Ideal Point ◽  
Point Estimation ◽  
The Other ◽  
Hypothesis Tests ◽  
Consistent Estimation ◽  
Nonparametric Approach ◽  
Ideal Points ◽  
Ideal Point Estimation

Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

Libertarian Motivations on the Vinson Court

1953 ◽  
Vol 47 (2) ◽  
pp. 321-336
Author(s):  
C. Herman Pritchett
Keyword(s):  
Supreme Court ◽  
Voting Behavior ◽  
The United States ◽  
United States Senate ◽  
Thought Processes ◽  
Anglo Saxon ◽  
Legislative Voting ◽  
The Supreme Court ◽  
The Mind ◽  
The Devil

Justice Frankfurter is fond of quoting an old English saying that “the devil himself knoweth not the mind of men.” The mind of a man who happens to be a judge is the center of many contending impulses when he is making it up, and an external reconstruction of the process is quite impossible. However, the rules of the game require that judges supply clues to their thought processes in the form of written opinions. In every major case decided by the Supreme Court, one or more of its members provide a written justification for the decision announced. The individualistic tradition of Anglo-Saxon jurisprudence, moreover, permits justices who do not agree with the views of their brethren to say so, and to give their reasons for dissenting. Thus the Supreme Court on decision day takes on the aspect of a small legislature in which votes are cast pro and con on significant issues of public policy, with accompanying explanations much more coherent and systematic and better-reasoned than are customarily available in explanation of votes cast, say, in the United States Senate.While it has not been usual to do so, these judicial votes can be subjected to the same kinds of analysis as have been traditionally employed for the study of legislative voting behavior.

scholarly journals Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data

2015 ◽  
Vol 23 (1) ◽  
pp. 76-91 ◽  
Author(s):  
Pablo Barberá
Keyword(s):  
Social Networks ◽  
Latent Variable ◽  
Ideal Point ◽  
The United States ◽  
Point Estimation ◽  
Potential Contribution ◽  
Online Behavior ◽  
The Social ◽  
Party Registration ◽  
Twitter Users

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.

Monetary Policy Committees and Voting Behavior

2019 ◽  
pp. 38-58
Author(s):  
Sylvester Eijffinger ◽  
Ronald Mahieu ◽  
Louis Raes
Keyword(s):  
Monetary Policy ◽  
Voting Behavior ◽  
Ideal Point ◽  
Point Estimation ◽  
Point Estimates ◽  
Monetary Policy Committees ◽  
Using Data ◽  
Traditional Approaches ◽  
Ideal Point Estimation

In this chapter we suggest to use Bayesian ideal point estimation to analyze voting in monetary policy committees. Using data from the Riksbank we demonstrate what this entails and we compare ideal point estimates with the results from traditional approaches. We end by suggesting possible extensions.

Estimates of the Spatial Voting Model

2019 ◽  
pp. 818-838
Author(s):  
Christopher Hare ◽  
Keith T. Poole
Keyword(s):  
Spatial Model ◽  
Ideal Point ◽  
Empirical Work ◽  
Geometric Theory ◽  
The United States ◽  
Point Estimation ◽  
Empirical Estimates ◽  
Spatial Voting ◽  
The Social ◽  
Political Choices

In this chapter, the authors survey the empirical success of the spatial (or geometric) theory of voting. Empirical work lagged behind the development of theory until about 30 years ago and since then has exploded, with ideal-point estimation emerging as an important methodological subfield in political science. Empirical applications of spatial theory are now legion, and the basic news is that the spatial model has been enormously successful in explaining observed political choices and outcomes at both the elite and mass levels. In the United States, empirical estimates of the spatial model also help to explain incongruities between the median voter theorem and party polarization. These empirical estimates have demonstrated that the theory is extremely powerful on a number of levels—indeed, that it is one of the most successful mathematical theories in the social sciences.

Small Chamber Ideal Point Estimation

2009 ◽  
Vol 17 (3) ◽  
pp. 276-290 ◽  
Author(s):  
Michael Peress
Keyword(s):  
Supreme Court ◽  
Latin American ◽  
Ideal Point ◽  
Point Estimation ◽  
Multidimensional Space ◽  
Incidental Parameters ◽  
Ideal Points ◽  
Point Estimators ◽  
The Ideal ◽  
Ideal Point Estimation

Ideal point estimation is a topic of central importance in political science. Published work relying on the ideal point estimates of Poole and Rosenthal for the U.S. Congress is too numerous to list. Recent work has applied ideal point estimation to the state legislatures, Latin American chambers, the Supreme Court, and many other chambers. Although most existing ideal point estimators perform well when the number of voters and the number of bills is large, some important applications involve small chambers. We develop an estimator that does not suffer from the incidental parameters problem and, hence, can be used to estimate ideal points in small chambers. Our Monte Carlo experiments show that our estimator offers an improvement over conventional estimators for small chambers. We apply our estimator to estimate the ideal points of Supreme Court justices in a multidimensional space.

Ideal Point Estimation in Political Hierarchies: A Framework and an Application to the US Executive Branch

2019 ◽  
Author(s):  
Alex Acs
Keyword(s):  
Ideal Point ◽  
The United States ◽  
Executive Branch ◽  
Point Estimation ◽  
The Us ◽  
Political Hierarchy ◽  
Public Bureaucracy ◽  
Ideal Points ◽  
Using Data ◽  
The Ideal

Abstract This article develops a procedure for estimating the ideal points of actors in a political hierarchy, such as a public bureaucracy. The procedure is based on a spatial auditing model and is motivated by the idea that while agents within a political hierarchy are typically segregated in different policy fiefdoms, they are bound to a common principal that can scrutinize their policy proposals through selective reviews, or audits. The theoretical model shows how a principal’s decision to audit an agent’s proposal can reveal both actors’ spatial preferences, despite the strategic nature of the interaction. Empirical identification of the ideal points comes from leveraging settings where elections replace principals over time, but not agents. Although the procedure is quite general, I provide an illustration using data on federal regulatory policymaking in the United States and recover ideal point estimates for presidents and agencies across three administrations.

Voting Behavior on the United States Courts of Appeals, 1961–1964

1966 ◽  
Vol 60 (2) ◽  
pp. 374-383 ◽  
Author(s):  
Sheldon Goldman
Keyword(s):  
United States ◽  
Supreme Court ◽  
Voting Behavior ◽  
United States Supreme Court ◽  
The United States ◽  
Courts Of Appeals ◽  
Party Affiliation ◽  
The Supreme Court ◽  
Research Problems ◽  
Relationship Of

Voting behavior of public decision-makers has been of central concern for political scientists. For example, studies of legislatures (notably of Congress) have investigated such research problems as: (1) the extent to which voting on one issue is related to voting on other issues; (2) the potency of party affiliation as an organizer of attitudes and a predictor of voting behavior; and (3) the relationship of demographic characteristics to voting behavior. These and related concerns have more recently occupied the attention of students of the judiciary whose focus has primarily been on the United States Supreme Court. State courts of last resort have also provided a testing ground primarily for problems (2) and (3). However, the United States courts of appeals, second only to the Supreme Court in judicial importance, have been largely neglected. This paper considers the above research problems with reference to the voting behavior on all eleven courts of appeals from July 1, 1961 through June 30, 1964.

Large-Scale Ideal Point Estimation

2021 ◽  
pp. 1-18
Author(s):  
Michael Peress
Keyword(s):  
Voting Behavior ◽  
Large Scale ◽  
Ideal Point ◽  
Computation Time ◽  
Large Data ◽  
R Package ◽  
Point Estimation ◽  
Political Actors ◽  
Ideal Point Estimation ◽  
Data Matrices

Abstract Recent advances in the study of voting behavior and the study of legislatures have relied on ideal point estimation for measuring the preferences of political actors, and increasingly, these applications have involved very large data matrices. This has proved challenging for the widely available approaches. Limitations of existing methods include excessive computation time and excessive memory requirements on large datasets, the inability to efficiently deal with sparse data matrices, inefficient computation of standard errors, and ineffective methods for generating starting values. I develop an approach for estimating multidimensional ideal points in large-scale applications, which overcomes these limitations. I demonstrate my approach by applying it to a number of challenging problems. The methods I develop are implemented in an r package (ipe).

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