Prince: An improved method for measuring incentivized preferences
提出Prince激励系统，结合直接匹配与选择列表的优势，透明地测量偏好，避免策略行为，适用于风险与模糊决策，实证支持禀赋效应和线性效用。
Abstract This paper introduces the Prince incentive system for measuring preferences. Prince combines the tractability of direct matching, allowing for the precise and direct elicitation of indifference values, with the clarity and validity of choice lists. It makes incentive compatibility completely transparent to subjects, avoiding the opaqueness of the Becker-DeGroot-Marschak mechanism. It can be used for adaptive experiments while avoiding any possibility of strategic behavior by subjects. To illustrate Prince’s wide applicability, we investigate preference reversals, the discrepancy between willingness to pay and willingness to accept, and the major components of decision making under uncertainty: utilities, subjective beliefs, and ambiguity attitudes. Prince allows for measuring utility under risk and ambiguity in a tractable and incentive-compatible manner even if expected utility is violated. Our empirical findings support modern behavioral views, e.g., confirming the endowment effect and showing that utility is closer to linear than classically thought. In a comparative study, Prince gives better results than a classical implementation of the random incentive system.