Hostname: page-component-6587cd75c8-vfwnz Total loading time: 0 Render date: 2025-04-24T10:04:49.476Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  14 March 2025

Michael D. Lee Open the ORCID record for Michael D. Lee [Opens in a new window]  and
Sara Chong
Michael D. Lee*
Affiliation:
Department of Cognitive Sciences, University of California Irvine, Irvine, CA 92697-5100, USA
Sara Chong
Affiliation:
Department of Cognitive Sciences, University of California Irvine, Irvine, CA 92697-5100, USA
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

Optimal stoppingCognitive modelingChanging environmentsStrategic decision makingD9D90C91
Type
Original Paper
Information
Experimental Economics , Volume 27 , Issue 4: Special Issue in Honor of Amnon Rapoport, Part 1 , September 2024 , pp. 854 - 873
Copyright
Copyright © The Author(s), under exclusive licence to Economic Science Association 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Baumann, C, Gershman, SJ, Singmann, H, & von Helversen, B. (2020). A linear threshold model for optimal stopping behavior. Proceedings of the National Academy of Science, 117, 23, 12750–12755. 10.31234/osf.io/epd2x.CrossRefGoogle ScholarPubMed
Baumann, C, Schlegelmilch, R, & von Helversen, B. (2023). Adaptive behavior in optimal sequential search. Journal of Experimental Psychology: General, 152, 3, 657–672. 10.1037/xge0001287.CrossRefGoogle ScholarPubMed
Bearden, JN, Rapoport, A, & Murphy, RO. (2006). Sequential observation and selection with rank-dependent payoffs: An experimental study. Management Science, 52, 9, 1437–1449. 10.1287/mnsc.1060.0535.CrossRefGoogle Scholar
Bilotkach, V, Gaggero, AA, & Piga, CA. (2015). Airline pricing under different market conditions: Evidence from European low-cost carriers. Tourism Management, 47, 152–163. 10.1016/j.tourman.2014.09.015.CrossRefGoogle Scholar
Bonner, SE, Hastie, R, Sprinkle, GB, & Young, SM. (2000). A review of the effects of financial incentives on performance in laboratory tasks: Implications for management accounting. Journal of management accounting research, 12, 1, 19–64. 10.2308/jmar.2000.12.1.19.CrossRefGoogle Scholar
Camerer, CF, & Hogarth, RM. (1999). The effects of financial incentives in experiments: A review and capital-labor-production framework. Journal of Risk and Uncertainty, 19, 7–42. 10.1007/978-94-017-1406-8_2.CrossRefGoogle Scholar
Campbell, J., & Lee, M. D. (2006). The effect of feedback and financial reward on human performance solving ‘secretary’ problems. In Sun, R. (Ed.), Proceedings of the 28th Annual conference of the cognitive science society (pp. 1068–1073). Erlbaum.Google Scholar
Ferguson, TS. (1989). Who solved the secretary problem?. Statistical Science, 4, 3, 282–296. 10.1214/ss/1177012493.Google Scholar
Gilbert, JP, & Mosteller, F. (1966). Recognizing the maximum of a sequence. American Statistical Association Journal, 61, 313, 35–73. 10.1007/978-0-387-44956-2_22.CrossRefGoogle Scholar
Goldstein, DG, McAfee, RP, Suri, S, & Wright, JR. (2020). Learning when to stop searching. Management Science, 66, 3, 1375–1394. 10.1287/mnsc.2018.3245.CrossRefGoogle Scholar
Guan, H., Lee, M. D., & Silva, A. (2014). Threshold models of human decision making on optimal stopping problems in different environments. In Bello, P., Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th annual conference of the cognitive science society (pp. 553–558). Cognitive Science Society.Google Scholar
Guan, M, & Lee, MD. (2018). The effect of goals and environments on human performance in optimal stopping problems. Decision, 5, 4, 339–361. 10.1037/dec0000081.CrossRefGoogle Scholar
Hertwig, R, & Ortmann, A. (2001). Experimental practices in economics: A methodological challenge for psychologists?. Behavioral and Brain Sciences, 24, 3, 383–403. 10.1017/s0140525x01004149.CrossRefGoogle ScholarPubMed
Kuss, M, Jäkel, F, & Wichmann, FA. (2005). Bayesian inference for psychometric functions. Journal of Vision, 5, 5, 478–492. 10.1167/5.5.8.CrossRefGoogle ScholarPubMed
Lee, MD. (2006). A hierarchical Bayesian model of human decision making on an optimal stopping problem. Cognitive Science, 30, 3, 555–580. 10.1207/s15516709cog0000_69.CrossRefGoogle Scholar
Lee, M. D. (2018). Bayesian methods in cognitive modeling. In Wixted, J. & Wagenmakers, E.-J. (Eds.), The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience. Volume 5: Methodology chapter 2 (4th edn, pp. 37–84). Wiley.Google Scholar
Lee, MD, & Courey, KA. (2021). Modeling optimal stopping in changing environments: A case study in mate selection. Computational Brain & Behavior, 4, 1–17. 10.1007/s42113-020-00085-9.CrossRefGoogle Scholar
Lee, MD, Criss, AH, Devezer, B, Donkin, C, Etz, A, Leite, FP, Matzke, D, Rouder, JN, Trueblood, JS, White, CN, & Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain & Behavior, 2, 141–153. 10.31234/osf.io/dmfhk.CrossRefGoogle Scholar
Lee, MD, Gluck, KA, & Walsh, MM. (2019). Understanding the complexity of simple decisions: Modeling multiple behaviors and switching strategies. Decision, 6, 4, 335–368. 10.1037/dec0000105.CrossRefGoogle Scholar
Lee, MD, & Vanpaemel, W. (2018). Determining informative priors for cognitive models. Psychonomic Bulletin & Review, 25, 114–127. 10.3758/s13423-017-1238-3.CrossRefGoogle ScholarPubMed
McCartney, S. (2014). The best day to buy airline tickets. The Wall Street Journal. https://www.wsj.com/articles/the-best-day-to-buy-airline-tickets-1413999377.Google Scholar
Pinker, S. (1997). How the mind works, W. W. Norton & Co..Google Scholar
Pitt, MA, Myung, IJ, & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 3, 472–491. 10.1037/0033-295x.109.3.472.CrossRefGoogle Scholar
Plummer, M. (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In Hornik, K., Leisch, F., & Zeileis, A. (Eds.), Proceedings of the 3rd international workshop on Distributed Statistical Computing (DSC 2003), vol. 124, (pp. 123 Computational Brain & Behavior 1–10). Vienna, Austria.Google Scholar
Seale, DA, & Rapoport, A. (1997). Sequential decision making with relative ranks: An experimental investigation of the “Secretary Problem”. Organizational Behavior and Human Decision Processes, 69, 3, 221–236. 10.1006/obhd.1997.2683.CrossRefGoogle Scholar
Seale, DA, & Rapoport, A. (2000). Optimal stopping behavior with relative ranks. Journal of Behavioral Decision Making, 13, 4, 391–411. 10.1002/1099-0771.3.0.CO;2-I>CrossRefGoogle Scholar
Shapira, Z, & Venezia, I. (1981). Optional stopping on nonstationary series. Organizational Behavior and Human Performance, 27, 1, 32–49. 10.1016/0030-5073(81)90037-4.CrossRefGoogle Scholar
Villarreal, M, Etz, A, & Lee, MD. (2023). Evaluating the complexity and falsifiability of psychological models. Psychological Review, 130, 4, 853–872. 10.1037/rev0000421.CrossRefGoogle ScholarPubMed