我们称什么重量？概率加权的机理模型
What are we weighting for? A mechanistic model for probability weighting
摘要：
行为经济学为人类经济行为的模式提供了标签。概率加权就是这样一个标签。它表示决策的正式模型中使用的概率（即模型参数）和从真实人的决策中推断的概率（经验估计的相同参数）之间的不匹配。推断出的概率称为“决策权重”。“这被认为是一个强有力的实验发现，决策权重高于罕见事件的概率，并且（通过标准化）低于常见事件的概率。这通常表现为认知偏见，即个人的判断错误。在这里，我们指出，同样的观察可以用不同的方式来描述：广义地说，概率加权意味着决策者对世界的不确定性比观察者更大。我们提供了一个合理的机制，使这种差异的不确定性自然出现：当决策者必须估计概率的频率在一个时间序列，而观察者知道他们的先验。这表明了另一种介绍的概率加权作为一个原则性的反应，由决策者的不确定性在观察员的模型。
英文摘要：
Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and probabilities inferred from real people's decisions (the same parameters estimated empirically). The inferred probabilities are called "decision weights." It is considered a robust experimental finding that decision weights are higher than probabilities for rare events, and (necessarily, through normalisation) lower than probabilities for common events. Typically this is presented as a cognitive bias, i.e. an error of judgement by the person. Here we point out that the same observation can be described differently: broadly speaking, probability weighting means that a decision maker has greater uncertainty about the world than the observer. We offer a plausible mechanism whereby such differences in uncertainty arise naturally: when a decision maker must estimate probabilities as frequencies in a time series while the observer knows them a priori. This suggests an alternative presentation of probability weighting as a principled response by a decision maker to uncertainties unaccounted for in an observer's model.
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