Do Non-Choice Data Reveal Preferences?

非选择数据是否揭示偏好？

• 批准号: 1156263
• 负责人: Douglas Bernheim
• 金额: $ 29.36万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1156263&HistoricalAwards=false
• 关键词: Do; Non; Choice; Data; Reveal

This award funds the development of a new method for inferring the choices people will make in possible situations from a variety of 'non-choice' data. Many practical problems in microeconomics call for the researcher to predict the distribution of households' choices in not-yet-observed situations. Economists prefer to draw such inferences from actual choices in a closely related domain. However, this approach is sometimes problematic in practice because of the practical limitations of choice data. For example, we may not have data about closely related choices, or we may have serious concerns about uncontrolled factors, selections, and the endogeneity of opportunity sets.The new method uses non-choice data. This method goes beyond current 'non-choice' methods such as the use of survey responses to include possible measures of passive physiological and neurological responses that have been widely studied in behavioral science. The new approach has several features beyond the use of non-choice data. The researchers focus on non-choice responses that are likely to have context-independent meanings over large collections of choice problems. The researchers treat the decision problem as the unit of observation, and they treat the problem as one of optimal statistical prediction. Finally, they want to determine whether and when these new methods are better than the standard method using imperfect choice data.Broader impacts are substantial. The research could result in a new valuation method that could be used in addition to or instead of such methods as contingent valuation. The result would be improvements in how microeconomics is used to analyze a broad array of practical questions.

该奖项资助开发一种新方法，用于从各种“非选择”数据中推断人们在可能情况下的选择。 微观经济学中的许多实际问题要求研究者在尚未观察到的情况下预测家庭选择的分布。 经济学家更喜欢从密切相关领域的实际选择中得出这样的推论。 然而，由于选择数据的实际限制，这种方法在实践中有时会出现问题。 例如，我们可能没有关于密切相关的选择的数据，或者我们可能对不受控制的因素、选择和机会集的内隐性有严重的担忧。 这种方法超越了当前的“非选择”方法，例如使用调查响应，包括行为科学中已广泛研究的被动生理和神经反应的可能测量方法。 除了使用非选择数据外，新方法还有几个特点。 研究人员专注于非选择反应，这些反应可能在大量的选择问题中具有独立于上下文的意义。 研究人员将决策问题视为观察单元，并将其视为最优统计预测问题之一。 最后，他们希望确定这些新方法是否以及何时优于使用不完美选择数据的标准方法。 这项研究可能会产生一种新的估值方法，可用于补充或取代或有估值等方法。 其结果将是改进如何使用微观经济学来分析广泛的实际问题。