Modeling Evaluation Methods for Eyewitness Identification

目击者识别的建模评估方法

• 批准号: 2017046
• 负责人: Yueran Yang
• 金额: $ 29.48万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017046&HistoricalAwards=false
• 关键词: Modeling; Evaluation; Methods; Eyewitness; Identification; Modeling; Evaluation; Methods; Eyewitness; Identification

Eyewitness identification plays an important role in administering justice in the criminal justice system. Yet, eyewitnesses make mistakes that can lead to grave legal consequences, with innocent suspects who are wrongfully convicted and guilty suspects who escape punishment and continue to harm society. Because of the complexity of eyewitness responses and associated legal consequences, it is critical to develop effective methods for evaluating eyewitness performance. This project will incorporate a modeling approach to develop effective evaluation methods for eyewitness identification.Employing a mathematical modeling approach, this project will advance and apply an expected cost model to integrate police investigation practices before, during, and after conducting an eyewitness identification. By incorporating meta-analytical data and mathematical modeling, the project’s expected cost model will provide an integrative framework for rigorously analyzing eyewitness performance and for making useful predictions under a broad range of circumstances. This research will analyze the implications of the evaluation methods commonly used in eyewitness research in order to build toward scientific consensus for analyzing eyewitness data and assessing identification procedures. This project will also develop computational and visualization tools to enhance proper use of the expected cost model and other evaluation methods. Findings will improve understanding of eyewitness performance, produce scientific evidence for optimal legal practices, and promote the scientific use of mathematical modeling approaches in eyewitness research. All of these outcomes are crucial in developing scientific understanding of eyewitness identification and improving identification practices to advance justice.This project is jointly funded by the Law and Science Program in the Directorate for Social, Behavioral & Economic Sciences (SBE) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

目击者识别在管理刑事司法系统中的正义方面起着重要作用。然而，目击者会犯错，可能导致严重的法律后果，而无辜的嫌疑人被错误定罪和有罪的嫌疑犯，他们逃脱了惩罚并继续损害社会。由于目击者的反应和相关法律后果的复杂性，开发评估目击者表现的有效方法至关重要。该项目将结合一种建模方法来开发目击者识别的有效评估方法。就业数学建模方法，该项目将推进并采用预期的成本模型来整合警察调查惯例，然后进行目击者识别。通过合并的荟萃分析数据和数学建模，该项目的预期成本模型将为严格分析目击者的性能和在广泛情况下做出有用的预测提供一个集成的框架。这项研究将分析目击者研究中常用的评估方法的含义，以朝着分析目击者数据和评估识别程序的科学共识建立。该项目还将开发计算和可视化工具，以增强预期成本模型和其他评估方法的正确使用。发现将提高对目击者表现的理解，为最佳法律实践提供科学证据，并促进目击者研究中数学建模方法的科学使用。所有这些结果对于建立对目击者识别的科学理解和改善识别实践以提高正义至关重要。该项目是由社会，行为和经济科学局（SBE）的法律和科学计划共同资助的。更广泛的影响审查标准。