Researchers at Virginia Polytechnic Institute and State University's (Virginia Tech) Discovery Analytics Center propose in a new paper using computer-based machine learning to model the U.S. Supreme Court.
The researchers have developed a data-driven framework that can infer justices' judicial preferences and voting behavior, as well as answering questions about this behavior.
The researchers say their Supreme Court Ideal Point Miner harnesses information on judicial preferences gathered from opinion texts to enhance existing research. The model assumes every case entails a combination of issues or topics on which justices have different views, and it studies the text of opinions and counts the incidence of words related to each issue that factors into the decision. The system then assigns relative importance to each such issue according to its share of relevant words, and it can deduce the strength of each justice's feelings on an issue by aggregating the analyses of multiple opinions.
The model also can identify the high court's swing justices by examining the degree of variance of each justice's "ideal point" across multiple issues.
The Virginia Tech researchers say knowledge gleaned from the model can describe the decision process for an individual case, as well as predicting justices' decisions in future cases with 79.46-percent accuracy.
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