Daniel Martin Katz

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Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given topology imposes is a descriptive effort to characterize its physical properties. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure(More)
Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimerà and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first(More)
Einstein’s razor, a corollary of Ockham’s razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face—to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity’s(More)
In this paper, we compare several network representations of the corpus of United States Supreme Court decisions (1791--2005). This corpus is not only of seminal importance, but also represents a highly structured and largely self-contained body of case law. As constructed herein, nodes represent whole cases or individual "opinion units" within cases. Edges(More)
Acyclic digraphs arise in many natural and artificial processes. Among the broader set, dynamic citation networks represent an important type of acyclic digraph. For example, the study of such networks includes the spread of ideas through academic citations, the spread of innovation through patent citations, and the development of precedent in common law(More)
The United States Code (Code) is a document containing over 22 million words that represents a large and important source of Federal statutory law. Scholars and policy advocates often discuss the direction and magnitude of changes in various aspects of the Code. However, few have mathematically formalized the notions behind these discussions or directly(More)
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 R I. CONSTITUTIONAL DIRECT DEMOCRACY IN AMERICA: THE CURRENT STATE OF AFFAIRS . . . . . 300 R A. CLASSIFYING THE DOMAIN OF AMERICAN DIRECT DEMOCRACY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 R B. DATA INFORMED(More)
Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more(More)