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

Speaker: Prof Kyle Hyndman

Affiliation: UT Dallas

Zoom: https://uqz.zoom.us/j/82603079317

Abstract: 

Using a series of pre-registered, incentive-compatible online experiments, we 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: decision-makers strongly prefer distributions with longer tails (where probability mass is more evenly distributed) 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 functions 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 maximum informativeness is the goal, providers should prioritize sharing right-tail probabilities or percentiles; and (3) to increase service uptake, providers can strategically disclose distributional information for long-tailed waits and withhold it for thick-tailed waits.