Chasing Tails: How Do People Respond to Wait Time Distributions?

TL;DR

This study investigates how people evaluate different wait time distributions, revealing preferences for long-right tails and the importance of tail information, with implications for service design and communication strategies.

Contribution

It uncovers that decision-makers prefer long-right tail distributions over spike-shaped tails, challenging traditional utility models and highlighting the role of tail information in choices.

Findings

People prefer distributions with long-right tails over spike-shaped tails.

CVaR utility models predict preferences for long-right tail distributions.

Decision-makers seek information about right-tail outcomes.

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.