Bounded rationality and limited data sets
提出一种方法，在有限数据下理解有限理性理论的经验内容，改编经典显示偏好方法以揭示新信息，并应用于多种理论，指出先前测试的陷阱。
Bounded rationality theories are typically characterized over exhaustive data sets. We develop a methodology to understand the empirical content of such theories with limited data, adapting the classic revealed‐preference approach to new forms of revealed information. We apply our approach to an array of theories, illustrating its versatility. We identify theories and data sets testable in the same elegant way as rationality, and theories and data sets where testing is more challenging. We show that previous attempts to test consistency of limited data with bounded rationality theories are subject to a conceptual pitfall that may lead to false conclusions that the data are consistent with the theory.