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：我们提出一种新的方法来量化经济模式的限制性,其依据是模型与模拟假设数据集的相近程度。 数据集是从满足某些依赖应用的内容限制的分布中随机抽取的(比如人们更喜欢金钱而不是更少 ) 。 几乎所有假设数据都适合的模型并不具有限制性。 为了说明我们的方法,我们评估了两种广泛使用的行为模式 — — 累积前景理论和Poisson Cognitive 等级模型 — — 的限制性,并解释了限制如何揭示了对这两种模式的新洞察力。