Inferring models of opinion dynamics from aggregated jury data. Burghardt, K. A. R. & William AND Girvan, M. PLOS ONE, 14(7):1-15, Public Library of Science, 07, 2019. ![Inferring models of opinion dynamics from aggregated jury data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant’s guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.
@article{Burghardt2018Jury,
author = {Burghardt, Keith AND Rand, William AND Girvan, Michelle},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Inferring models of opinion dynamics from aggregated jury data},
year = {2019},
month = {07},
volume = {14},
url = {https://doi.org/10.1371/journal.pone.0218312},
pages = {1-15},
abstract = {Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant’s guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.},
number = {7},
doi = {10.1371/journal.pone.0218312}
}
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