What is it about?
Prosecutors sometimes threaten defendants with disproportionately severe punishment to force them to waive their right to trial and accept plea deals. But does this unethical charging practice change how defendants interpret their case circumstances? Across three experiments, this paper shows that threatening severe punishment makes mock defendants advised by mock attorneys willing to serve longer sentences and regard the plea deal as fairer and more lenient without altering their beliefs about the strength of the evidence or the severity of their crime. Despite their expertise, real defense attorneys were not less susceptible to the influence of prosecutorial charging practices. Thus, to coerce plea decisions, prosecutors need only raise the severity of their charges and sentencing recommendations.
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Why is it important?
First, the current work demonstrates that unethical charging practices by prosecutors make both novice defendants and expert attorneys willing to accept more severe sentences despite not thinking that their trial prospects are more dire. Second, though most prior work examines individual-level plea decision-making, this experiment is the first to examine group-level decision-making in an immersive role-playing simulation involving mock defendants and attorneys, which is important because attorneys are known to influence defendant decision-making. In sum, this work critiques the immense discretionary power of prosecutors by providing an evidence-based argument for charging with restraint and with justice—not coercion—in mind.
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This page is a summary of: Charged up and anchored down: A test of two pathways to judgmental and decisional anchoring biases in plea negotiations., Psychology Public Policy and Law, May 2023, American Psychological Association (APA),
DOI: 10.1037/law0000390.
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OSF project page
The present studies were approved by the institutional review board at John Jay College of Criminal Justice. In keeping with best science practices, all hypotheses, sample sizes, inclusion and exclusion criteria, materials, procedures, data preparation, and code for statistical analyses in R were preregistered and are publicly available on the Open Science Framework (OSF) at osf.io/h6ktg.
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