Using Neurometric Data To Measure Economic Values in Private and Social Exchange Situations

使用神经测量数据衡量私人和社会交换情况下的经济价值

• 批准号: 0850840
• 负责人: Colin Camerer
• 金额: $ 67.2万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850840&HistoricalAwards=false
• 关键词: Using; Neurometric; Data; Measure; Economic

"This award is funded under the American Recovery and Reinvestment Act of 2009(Public Law 111-5)."Firms, governments, and scientists are constantly striving to figure out what goods and services people want most and quantify their worth. The main technique used to do this in economics is to statistically analyze data on what people have bought before, in order to guess how purchases might change if people have more to spend, if prices rise or fall, or if similar products are introduced. However, this method is of limited use in forecasting the value of brand new products, and goods that are not traded in markets (particularly public goods which benefit everyone, such as clean air.)When people are choosing, a complex cognitive and biological process underlies those choices. The proposed research uses empirical tools from cognitive neuroscience to measure aspects of these processes when experimental subjects make actual choices of consumer goods. Measures will include brain imaging, eyetracking of visual attention, and speed of responses. The goal is to use these measures to infer what people value, in order to understand the neural foundation of choice and to predict actual choices more accurately than other measures can.

“这项奖励是根据2009年美国复苏和再投资法案（公法111-5）资助的。”企业、政府和科学家一直在努力弄清楚人们最想要的商品和服务是什么，并量化它们的价值。在经济学中，这样做的主要技术是对人们之前购买的数据进行统计分析，以便猜测如果人们有更多的钱可以花，价格上涨或下跌，或者引入类似的产品，购买行为会如何变化。然而，这种方法在预测全新产品和未在市场上交易的商品（特别是有利于每个人的公共产品，如清洁空气）的价值方面用途有限。当人们做出选择时，一个复杂的认知和生物过程是这些选择的基础。该研究使用认知神经科学的经验工具来测量实验对象在实际选择消费品时这些过程的各个方面。测试将包括大脑成像、视觉注意力的眼球追踪和反应速度。我们的目标是使用这些方法来推断人们的价值，从而理解选择的神经基础，并比其他方法更准确地预测实际的选择。