We use a series of pre-registered, incentive-compatible online experiments to investigate how people evaluate and choose among different waiting time distributions. Our main findings are threefold. First, consistent with prior literature, people show an aversion to both longer expected waits and higher variance. Second, and more surprisingly, moment-based utility models fail to capture preferences when distributions have thick-right tails: indeed, decision-makers strongly prefer distributions with long-right tails (where probability mass is more evenly distributed over a larger support set) relative to tails that exhibit a spike near the maximum possible value, even when controlling for mean, variance, and higher moments. Conditional Value at Risk (CVaR) utility models commonly used in portfolio theory predict these choices well. Third, when given a choice, decision-makers overwhelmingly seek information about right-tail outcomes. These results have practical implications for service operations: (1) service designs that create a spike in long waiting times (such as priority or dedicated queue designs) may be particularly aversive; (2) when informativeness is the goal, providers should prioritize sharing right-tail probabilities or percentiles; and (3) to increase service uptake, providers can strategically disclose (or withhold) distributional information depending on right-tail shape.

翻译：我们通过一系列预先注册、激励相容的在线实验，研究人们如何评估和选择不同的等待时间分布。我们的主要发现有三点。首先，与先前文献一致，人们对较长的预期等待时间和较高的方差均表现出厌恶。其次，更令人惊讶的是，当分布具有厚右尾时，基于矩的效用模型无法捕捉偏好：事实上，即使控制了均值、方差及更高阶矩，决策者仍强烈偏好具有长右尾的分布（即概率质量在更大的支撑集上更均匀分布），而非在最大可能值附近出现尖峰的尾部。投资组合理论中常用的条件风险价值（CVaR）效用模型能很好地预测这些选择。第三，当面临选择时，决策者绝大多数会寻求关于右尾结果的信息。这些结果对服务运营具有实际意义：（1）导致长等待时间出现尖峰的服务设计（如优先级或专用队列设计）可能特别令人厌恶；（2）若以信息有效性为目标，服务提供者应优先分享右尾概率或百分位数；（3）为提高服务采纳率，提供者可根据右尾形状策略性地披露（或隐瞒）分布信息。