Eliciting subjective real-valued beliefs
提出一种简单且激励兼容的价格列表方法，用于引出主观实值信念的分位数，进而近似完整的主观分布，并估计均值、方差等难以直接引出的属性。
Abstract We present a simple and robustly incentive-compatible price list methodology to elicit quantiles of a subjective real-valued belief. These elicited quantiles can be employed to approximate a subject’s complete subjective distribution, and we establish that the distribution maximizing entropy while adhering to the elicited quantiles is piecewise linear. Using this approach, our methodology extends to estimating arbitrary unobserved attributes of the subjective distribution, such as mean and variance, which are otherwise challenging to elicit. We provide a proof-of-concept for our framework through an experiment involving the elicitation of participants’ beliefs regarding the mathematical abilities of their peers.