A quantitative analysis of heuristics and discounting models of intertemporal choice

跨期选择的启发式和贴现模型的定量分析

• 批准号: 1062045
• 负责人: Michel Regenwetter
• 金额: $ 47.91万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062045&HistoricalAwards=false
• 关键词: quantitative; analysis; heuristics; discounting; models

The Discounted Utility model has, historically, been regarded as the appropriate model of how people make choices between intertemporal prospects (e.g., between receiving some amount of money today versus that amount of money plus interest at some future date.) However, there is abundant evidence that actual behavior systematically departs from the predictions of the model. A variety of alternatives have been proposed in the literature. Some of these depart from Discounted Utility's assumption of exponential discounted while others assume that intertemporal choices are made based on the application of simple decision heuristics. In this research project, the PI (in close cooperation with Dr. Jeffrey R. Steven's lab at the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development, Germany) will develop full-fledged probabilistic specifications of competing theories for intertemporal choice and apply quantitative methods immune from aggregation artificacts to test their relative veracity. Intertemporal choices underlie many of society's most pressing decisions, from collective decisions such as global climate change and the war on obesity to more personal decisions such as consuming alcohol and investing in retirement plans. Virtually all decisions we make have a temporal component. This makes intertemporal choice relevant across a broad range of disciplines (e.g., economics, psychology, biology, neuroscience, finance, medicine, environmental science). Despite the extensive interest in this topic, there still is a critical gap in our knowledge of how individuals make intertemporal choices. The research proposed in this application is significant because it will allow us to investigate the different cognitive processes that individuals use when making intertemporal choices.  Equally significant is the question of interindividual differences. Rather than dismissing variation as noise in the data, we expect to show strong qualitative differences among different people. In terms of broader impacts, this may  allow simple ``nudges'' to help people make better decisions by customizing individual decision scenarios to focus on the long-term benefits of a healthy lifestyle, a secure financial future, and environmental stewardship. The scientific findings will impact future training of basic and applied decision scientists. By eliminating major sources of artifacts, the project can help improve all levels of societal decision making in the future.

从历史上看，贴现效用模型一直被认为是人们如何在跨期前景之间做出选择的适当模型（例如，在今天收到一定数额的钱与在未来某个日期收到这笔钱加利息之间）。有证据表明，实际行为系统地偏离了模型的预测，其中一些偏离了贴现效用的指数贴现假设，而另一些则假设基于跨期选择。在这个研究项目中，PI（与德国马克斯普朗克人类发展研究所适应性行为和认知中心的 Jeffrey R. Steven 博士的实验室密切合作）将开发成熟的简单决策启发法。跨期选择的竞争理论的概率规范，并应用不受聚合伪影影响的定量方法来测试其相对准确性。跨期选择是社会许多最紧迫决策（集体决策）的基础。从全球气候变化和肥胖战争到饮酒和投资退休计划等更个人的决定，我们所做的几乎所有决定都具有时间成分，这使得跨期选择与广泛的学科相关（例如，经济学、尽管人们对这一主题产生了广泛的兴趣，但我们对个人如何做出跨期选择的了解仍然存在重大差距，因为它将允许我们进行跨期选择。我们调查个体在做出跨期选择时使用的不同认知过程同样重要，而不是将差异视为数据中的噪音，我们希望在更广泛的影响方面显示出强烈的质量差异。简单的“推动”，通过定制个人决策场景来帮助人们做出更好的决策，重点关注健康生活方式、安全的财务未来和环境管理的长期利益。科学发现将影响未来的基础和应用培训。决策科学家。作为文物的主要来源，该项目可以帮助改善未来各个层面的社会决策。

项目成果

Predicting similarity judgments in intertemporal choice with machine learning

通过机器学习预测跨期选择中的相似性判断

• DOI: 10.3758/s13423-017-1398-1
• 发表时间: 2018
• 期刊: Psychonomic Bulletin & Review
• 影响因子: 3.5
• 作者: Stevens, Jeffrey R.;Soh, Leen-Kiat
• 通讯作者: Soh, Leen-Kiat