Collaborative Research: Meta-Analysis of Empirical Estimates of Loss-Aversion

合作研究：损失厌恶实证估计的荟萃分析

• 批准号: 1757288
• 负责人: Colin Camerer
• 金额: $ 5.58万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1757288&HistoricalAwards=false
• 关键词: Collaborative; Research; Meta; Analysis; Empirical

Losses hurt more than gains feel good. This principle, known as "loss-aversion", is a foundational principle in behavioral economics, which uses psychology to improve economic prediction and advice. Loss aversion is applied to understand several policy issues, including international trade and stock market trades. It has also been used as a tool by governments and businesses to design "nudges" to help people make better decisions. The value of "loss aversion" depends on specific events and circumstances in which it is applied. As a result, several calculated values of loss aversion exist, creating confusion and uncertainty for its use in research and policy. This proposed research will collect all calculated values of loss aversion, categorize and summarize, and placed them in a central repository for easy reference. The results of this research activity will improve the usefulness of loss aversion in research and policy development. This will establish the US as the global leader in loss aversion research and application as this will be the only such study in the world. Aversion to losses is measured by a number "lambda", which is thought to be about 1.5-2 times higher than the value of gains. This range of values is based on prominent examples, but there are actually several hundred published studies measuring "lambda". Our proposed meta-analysis involves finding and identifying every paper that has a statistical estimate of "lambda", recording all those estimates, then summarizing their results. Scientists can then see for the first time whether the assumed range of "lambda"=1.5 to 2 is correct. The proposed research will also measure how much "lambda" varies for different groups of people and based on differences in scientific methods (e.g., controlled lab experiments vs. inferences from everyday decisions). This evidence can help behavioral insights teams customize nudges for particular groups of people, using more precise assumptions about the appropriate value of "lambda". The meta-analysis also illustrates how to cumulate scientific knowledge effectively, to overcome possible concerns about the reproducibility of science. This research also contributes to the efforts to increase reproducibility of scientific research.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.

损失的伤害比收获的感觉更好。这一原则被称为“损失规避”，是行为经济学的基本原则，它利用心理学来改善经济预测和建议。损失厌恶被应用于理解几个政策问题，包括国际贸易和股票市场交易。 它也被政府和企业用作设计“助推”的工具，以帮助人们做出更好的决策。 “损失规避”的价值取决于具体的事件和应用它的情况。 因此，存在几个计算值的损失厌恶，造成混乱和不确定性，其用于研究和政策。 本研究将收集所有损失厌恶的计算值，分类和总结，并将它们放在一个中央存储库中，以便于参考。 这项研究活动的结果将提高避免损失在研究和政策制定中的作用。 这将使美国成为损失规避研究和应用的全球领导者，因为这将是世界上唯一的此类研究。 对损失的厌恶是用一个数字“lambda”来衡量的，这个数字被认为是收益值的1.5-2倍。这个数值范围是基于突出的例子，但实际上有几百个已发表的测量“lambda”的研究。 我们提出的荟萃分析包括找到并识别每一篇具有统计估计“lambda”的论文，记录所有这些估计，然后总结他们的结果。然后，科学家们可以第一次看到“lambda”=1.5到2的假设范围是否正确。拟议的研究还将测量不同人群的“λ”变化有多大，并基于科学方法的差异（例如，对照实验室实验vs.来自日常决策的推论）。 这些证据可以帮助行为洞察团队为特定人群定制轻推，使用关于“lambda”适当值的更精确假设。元分析还说明了如何有效地积累科学知识，以克服对科学可重复性的可能担忧。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。