Evidence of Herding and Stubbornness in Jury Deliberations

Abstract

We explore how the mechanics of collective decision-making, especially of jury deliberation, can be inferred from macroscopic statistics. We first hypothesize that the dynamics of competing opinions can leave a "fingerprint" in the joint distribution of final votes and time to reach a decision. We probe this hypothesis by modeling jury datasets from different states collected in different years and identifying which of the models best explains opinion dynamics in juries. In our best-fit model, individual jurors have a "herding" tendency to adopt the majority opinion of the jury, but as the amount of time they have held their current opinion increases, so too does their resistance to changing their opinion (what we call "increasing stubbornness"). By contrast, other models without increasing stubbornness, or without herding, create poorer fits to data. Our findings suggest that both stubbornness and herding play an important role in collective decision-making.

9 pages, 13 figures (5 figures in the main text, and 8 figures in the SI). Watermarks removed in version 2 and two very minor typos fixed

References in corpus (7)

• Power-law distributions in empirical data
• Voter Models on Heterogeneous Networks
• Scaling and universality in proportional elections
• Competing opinions and stubbornness: connecting models to data
• Human collective intelligence as distributed Bayesian inference
• Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
• Solution of the Voter Model by Spectral Analysis

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