We propose a new way to quantify the restrictiveness of an economic model, based on how well the model fits simulated, hypothetical data sets. The data sets are drawn at random from a distribution that satisfies some application-dependent content restrictions (such as that people prefer more money to less). Models that can fit almost all hypothetical data well are not restrictive. To illustrate our approach, we evaluate the restrictiveness of two widely-used behavioral models, Cumulative Prospect Theory and the Poisson Cognitive Hierarchy Model, and explain how restrictiveness reveals new insights about them.

翻译：我们提出了一种量化经济模型约束性的新方法，该方法基于模型对模拟假设数据集的拟合效果。这些数据集是从满足某些应用相关的内容约束（例如，人们偏好更多而非更少的货币）的分布中随机抽取的。能够良好拟合几乎所有假设数据的模型并不具备约束性。为阐释我们的方法，我们评估了两种广泛使用的行为模型——累积前景理论与泊松认知层级模型的约束性，并解释了约束性如何揭示关于它们的新见解。