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Assessing multiple prior models of behaviour under ambiguity

机译:评估歧义下的多个先验行为模型

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The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors and those not involving multiple priors. This paper provides an experimental investigation into the first set. Using an appropriate experimental interface we examine the fitted and predictive power of the various theories. We first estimate subject-by-subject, and then estimate and predict using a mixture model over the contending theories. The individual estimates suggest that 24% of our 149 subjects have behaviour consistent with Expected Utility, 56% with the Smooth Model, 11% with Rank Dependent Expected Utility and 9% with the Alpha Model; these figures are close to the mixing proportions obtained from the mixture estimates where the respective posterior probabilities of each of them being of the various types are 25%, 50%, 20% and 5%; and using the predictions 22%, 53%, 22% and 3%. The Smooth model appears the best.
机译:近来在歧义下行为的理论模型可分为两类:涉及多个先验的模型和不涉及多个先验的模型。本文提供了对第一组的实验研究。使用适当的实验接口,我们检查了各种理论的拟合和预测能力。我们首先对每个主题进行估算,然后在竞争的理论上使用混合模型进行估算和预测。个体估计表明,我们149名受试者中有24%的行为与预期效用一致,平滑模型为56%,等级相关的预期效用为11%,阿尔法模型为9%。这些数字接近于从混合估计得出的混合比例,其中每个混合估计的后验概率分别为25%,50%,20%和5%;并使用预测22%,53%,22%和3%。平滑模型看起来最好。

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