Preference Uncertainty, Preference Learning, and Paired Comparison Experiments
配对比较实验的结果表明，随着受访者在一系列二元选择中逐步推进，其偏好一致性增强，这可通过随机效用模型中误差项所衡量的估值分布方差降低来体现。方差显著减小与允许偏好学习的偏好不确定性模型一致，说明受访者在比较中积累经验后能更好地区分选项，从而获得更可靠的估值。
Results from paired comparison experiments suggest that as respondents progress through a sequence of binary choices they become more consistent, apparently fine-tuning their preferences. Consistency may be indicated by the variance of the estimated valuation distribution measured by the error term in the random utility model. A significant reduction in the variance is shown to be consistent with a model of preference uncertainty allowing for preference learning. Respondents become more adept at discriminating among items as they gain experience considering and comparing them, suggesting that methods allowing for such experience may obtain more well founded values. <i></i>