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Valence and interactions in judicial voting.

Edward D Lee1, George T Cantwell2,3

  • 1Complexity Science Hub Vienna, Josefstædter Strasse 39, Vienna, Austria.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|February 25, 2024
PubMed
Summary
This summary is machine-generated.

Judicial court voting statistics reveal judge interactions influence decisions, not just personal bias. Our model shows peer influence is key, offering new insights beyond traditional ideological scores.

Keywords:
US Supreme Courtbiasgroup interactionsmaximum entropystatistical inferencevoting

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Area of Science:

  • Complexity science
  • Political science
  • Judicial behavior analysis

Background:

  • Traditional analyses of judicial voting statistics often assume judges' decisions are independent.
  • This assumption overlooks the significant role of peer interaction among judges.

Purpose of the Study:

  • To develop a minimal model incorporating both judge bias and peer interaction.
  • To apply this model to US Supreme Court voting data.
  • To investigate the impact of judicial interaction on decision-making.

Main Methods:

  • Developed a minimal statistical model accounting for context-dependent judge bias and peer interaction.
  • Applied the model to historical US Supreme Court voting data (1946-2021).

Main Results:

  • Found strong evidence that judge interaction is a significant factor in US Supreme Court voting across different court compositions.
  • Recovered judge biases, after accounting for interaction, differ from established ideological scores.

Conclusions:

  • Judicial decision-making is influenced by peer interactions, challenging independence assumptions.
  • Complexity science and physics-inspired modeling can enhance understanding of legal and political systems.
  • The study offers improved measures for analyzing political voting beyond simple ideology.