Strategies people use buying airline tickets: a cognitive modeling analysis of optimal stopping in a changing environment

We study how people solve the optimal stopping problem of buying an airline ticket. Over a set of problems, people were given 12 opportunities to buy a ticket ranging from 12 months before travel to 1 day before. The distributions from which prices were sampled changed over time, following patterns observed in industry analysis of flight ticket pricing. We characterize the optimal decision process in terms of a set of thresholds that set the maximum purchase price for each time point. In a behavioral analysis, we find that the average price people pay is above the optimal, that there is little evidence people learn over the sequence of problems, but that there are likely significant individual differences in the way people make decisions. In a model-based analysis, we propose a set of nine possible decision strategies, based on how purchasing probabilities change according to time and the price of the ticket. Using Bayesian latent-mixture methods, we infer the strategies used by the participants, finding that some use purely time-based strategies, while others also attend to the price of the tickets. We conclude by noting the limitations in the strategies as accounts of people’s decision making, highlighting the need to consider sequential effects and other context effects on purchasing behavior.