Chasing Tails: How Do People Respond to Wait Time Distributions?
Evgeny Kagan, Kyle Hyndman, Andrew Davis
TL;DR
The paper investigates how people evaluate waiting-time distributions using preregistered, incentive-compatible online experiments. It shows that while mean and variance influence willingness to wait, the right-tail shape of the distribution—particularly thick versus long-right tails—drives decisions far beyond first-moment risk. Tail-based utility models, especially those incorporating CVaR and percentile information, better explain choices than traditional moment-based models and imply distinct service-design tradeoffs (pooled vs. dedicated queues) depending on tail characteristics. Participants also show a strong preference for right-tail information, and simplified tail information effectively informs decisions, with disclosure generally improving uptake for favorable tails but potentially reducing demand for unfavorable tails. Overall, accounting for tail shape alters predictions of customer behavior and offers concrete guidelines for communicating wait times and designing service systems.
Abstract
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.