Archetypes and Prototypes of Decisions under Uncertainty

本项目通过一系列的决策实验收集数据，以帮助理解不确定性条件下的选择。该项目还开发了工具，用于在不确定情况下对选择进行理论分析，并混合使用参数和非参数计量经济学技术，用新数据检验理论。该项目的理论和实验方面提供了对风险偏好和实现风险偏好的选择的更丰富的理解。实验为在允许对个人行为模式的一般表征的环境中的不确定性下的选择提供了积极的解释。先前的数据表明，尽管在选择行为上存在相当大的异质性，但参与者的行为方式确实是一致的，因为选择最大化了一组一致的偏好。然而，受试者也使用可识别的常见启发式(经验法则)来做出决定。这两种不同的行为反映了两种不同的理性：实体理性和程序理性。新的理论将这两种不同的观点结合成一个单一的模型，研究团队将使用新的理论来开发一种方法来识别人们何时做出决策时使用程序理性或实体理性。了解人们如何在危险情况下做出决策是一个重要的跨学科研究问题，对公共政策也有重要的影响。研究人员正在开发的实验平台和分析技术将对研究界广泛有用。

This project uses a series of decision making experiments to gather data for understanding choice under uncertainty. The project also develops tools for the theoretical analysis of choice under uncertainty and a blend of parametric and non-parametric econometric techniques for testing the theory with the new data. The theoretical and experimental aspects of the project provide a richer understanding of risk preferences and the choices that implement them.The experiments provide a positive account of choice under uncertainty in an environment that allows for a general characterization of the patterns of individual behavior. Previous data show that although there is considerable heterogeneity in choce behavior, participants do behave in a way that is consistent in the sense that choices maximize a consistent set of preferences. However, subjects also use identifiable common heuristics (rules of thumb) to make decisions. These two different kinds of behavior reflect two different kinds of rationality: substantive rationality and procedural rationality. The new theory combines these two different ideas into a single model and the research team will use the new theory to develop a method to identify when people make decisions using procedural or substantive rationality.Understanding how people make decisions in risky situations is an important and interdisciplinary research question that also has important implications for public policy. The experimental platform and the analytical techniques that the researchers are developing will be broadly useful to the research community.