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.

本文介绍了测量偏好的王子激励系统。Prince 结合了直接匹配的易处理性，允许精确和直接地引出无差异值，以及选择列表的清晰度和有效性。它使激励兼容性对主体完全透明，避免了 Becker-DeGroot-Marschak 机制的不透明性。它可以用于适应性实验，同时避免受试者的任何策略行为的可能性。为了说明普林斯的广泛适用性，我们调查了偏好逆转、支付意愿和接受意愿之间的差异，以及不确定性下决策的主要组成部分：效用、主观信念和模糊态度。即使违反了预期效用，Prince 也允许以易于处理和激励兼容的方式在风险和模糊性下衡量效用。我们的实证研究结果支持现代行为观点，例如，确认禀赋效应并表明效用比经典思想更接近线性。在比较研究中，普林斯给出了比随机激励系统的经典实施更好的结果。