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Date of Graduation
5-6-2021
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Department of Graduate Psychology
Advisor(s)
Bernice Marcopulos
Kethera Fogler
S. Jeanne Horst
Sharon Kelley
Abstract
An overwhelming majority of criminal cases in the United States utilize plea bargaining (90-95%). A plea bargain is an agreement between a criminal defendant and a prosecuting attorney where the defendant agrees to plead guilty, or nolo contendre (no contest), to one or more charges to reduce or drop other charges. The decision to accept a plea bargain must be made by the defendant, so a defendant’s ability to make or communicate competent choices regarding a plea bargain is important. However, defendant decision-making in plea bargaining is not sufficiently prevalent in plea bargaining or decision-making literature. While factors such as strength of evidence and attorney recommendation have been explored in defendant plea bargain decision-making, the same cannot be said of several cognitive biases that have been shown to affect decision-making in defendants. The current study is exploring the role of two such biases, temporal discounting and loss aversion, on defendant decision-making in plea bargaining. Participants in the study were presented with a vignette that details a plea-bargaining scenario and manipulates either temporal discounting or loss aversion, and were asked whether or not they would accept the plea bargain. It was found that more participants accepted a plea bargain when the consequence of doing so was distal rather than proximal, showing that temporal discounting had an effect, but that loss aversion had no effect on plea bargain decision-making. These findings can be used to inform various actors within the criminal justice system on how to frame plea offers in order to not be manipulative towards defendants.
Recommended Citation
Patel, Anisha, "The effect of temporal discounting and loss aversion on mock plea bargain decision-making" (2021). Masters Theses, 2020-current. 86.
https://commons.lib.jmu.edu/masters202029/86
