Abstract
While most models of human choice are linear to ease interpretation, it is not clear whether linear models are good models of human decision making. And while prior studies have investigated how task conditions and group characteristics, such as personality or socio-demographic background, influence human decisions, no prior works have investigated how to use less personal information for choice prediction. We propose a deep learning model based on self-attention and cross-attention to model human decision making which takes into account both subject-specific information and task conditions. We show that our model can consistently predict human decisions more accurately than linear models and other baseline models while remaining interpretable. In addition, although a larger amount of subject specific information will generally lead to more accurate choice prediction, collecting more surveys to gather subject background information is a burden to subjects, as well as costly and time-consuming. To address this, we introduce a training scheme that reduces the number of surveys that must be collected in order to achieve more accurate predictions.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Availability of data and materials
– The Gambling dataset [36] that was used in this work is available in the github repository, https://github.com/TRI-MAC/SEU. The answers to personalization questionnaires for each user is available on Open Science Framework at https://osf.io/mpzbx/. – The PHEV dataset [37] that was used in this work is available from Toyota Motor Corporation but restrictions apply to the availability of these data, which were used under license for the current work, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Toyota Motor Corporation.
Change history
20 February 2024
A Correction to this paper has been published: https://doi.org/10.1007/s10472-024-09934-w
References
Stumpf, S.A., Dunbar, R.L.: The effects of personality type on choices made in strategic decision situations. Decis. Sci. 22(5), 1047–1072 (1991)
Lauriola, M., Levin, I.P.: Personality traits and risky decision-making in a controlled experimental task: An exploratory study. Pers. Individ. Differ. 31(2), 215–226 (2001)
El Othman, R., El Othman, R., Hallit, R., Obeid, S., Hallit, S.: Personality traits, emotional intelligence and decision-making styles in lebanese universities medical students. BMC Psychol. 8, 1–14 (2020)
Yechiam, E.: Robust consistency of choice switching in decisions from experience. Judgm. Decis. Mak. 15(1), 74–81 (2020)
Gosling, S.D., Rentfrow, P.J., Swann, W.B., Jr.: A very brief measure of the big-five personality domains. J. Res. Pers. 37(6), 504–528 (2003)
Baumsteiger, R., Siegel, J.T.: Measuring prosociality: The development of a prosocial behavioral intentions scale. J. Pers. Assess. 101(3), 305–314 (2019)
Stanovich, K.E., West, R.F.: Reasoning independently of prior belief and individual differences in actively open-minded thinking. J. Educ. Psychol. 89(2), 342 (1997)
Becker, A., Deckers, T., Dohmen, T., Falk, A., Kosse, F.: The relationship between economic preferences and psychological personality measures. Annu. Rev. Econ. 4(1), 453–478 (2012)
Johnson, M., Schuster, M., Le, Q.V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G., et al.: Google’s multilingual neural machine translation system: Enabling zero-shot translation. Trans. Assoc. Comput. Linguist. 5, 339–351 (2017)
McFadden, D., et al.: Conditional logit analysis of qualitative choice behavior. https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf
McFadden, D., Train, K.: Mixed mnl models for discrete response. J. Appl. Econom. 15(5), 447–470 (2000)
Bower, A., Balzano, L.: Preference modeling with context-dependent salient features. In: Proceedings of the International Conference on Machine Learning, pp. 1067–1077 (2020). PMLR
Seshadri, A., Peysakhovich, A., Ugander, J.: Discovering context effects from raw choice data. In: Proceedings of the International Conference on Machine Learning, pp. 5660–5669 (2019). PMLR
Rosenfeld, N., Oshiba, K., Singer, Y.: Predicting choice with set-dependent aggregation. In: Proceedings of the International Conference on Machine Learning, pp. 8220–8229 (2020). PMLR
Green, P.E., Srinivasan, V.: Conjoint analysis in marketing: New developments with implications for research and practice. J. Mark. 54(4), 3–19 (1990). https://doi.org/10.1177/002224299005400402
Orme, B.K.: Getting started with conjoint analysis: strategies for product design and pricing research (2006)
Toubia, O., Evgeniou, T., Hauser, J., et al.: Optimization-based and machine-learning methods for conjoint analysis: Estimation and question design. Conjoint Measurement: Methods and Applications 12, 231–258 (2007)
Chapelle, O., Harchaoui, Z.: A machine learning approach to conjoint analysis. Adv. Neural Inf. Process. 17, 257–264 (2005)
Agrawal, M., Peterson, J.C., Griffiths, T.L.: Scaling up psychology via scientific regret minimization. Proc. Natl. Acad. Sci. 117(16), 8825–8835 (2020)
Peterson, J.C., Bourgin, D.D., Agrawal, M., Reichman, D., Griffiths, T.L.: Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372(6547), 1209–1214 (2021)
Bourgin, D.D., Peterson, J.C., Reichman, D., Russell, S.J., Griffiths, T.L.: Cognitive model priors for predicting human decisions. In: International Conference on Machine Learning, pp. 5133–5141 (2019). PMLR
van Cranenburgh, S., Alwosheel, A.: An artificial neural network based approach to investigate travellers’ decision rules. Transp. Res. Part C Emerg. 98, 152–166 (2019)
Barseghyan, L., Molinari, F., Thirkettle, M.: Discrete choice under risk with limited consideration. Am. Econ. Rev. 111(6), 1972–2006 (2021)
Han, Y., Pereira, F.C., Ben-Akiva, M., Zegras, C.: A neural-embedded choice model: Tastenet-mnl modeling taste heterogeneity with flexibility and interpretability (2020). arXiv:2002.00922
Nam, D., Cho, J.: Deep neural network design for modeling individual-level travel mode choice behavior. Sustainability 12(18), 7481 (2020)
Steiner, M., Helm, R., Hüttl-Maack, V.: A customer-based approach for selecting attributes and levels for preference measurement and new product development. Int. J. Prod. Dev. 21(4), 233–266 (2016)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2013) arXiv:1312.6114v10 [stat.ML]
Loreggia, A., Mattei, N., Rossi, F., Venable, K.B.: Cpm etric: Deep siamese networks for metric learning on structured preferences. In: International Joint Conference on Artificial Intelligence, pp. 217–234 (2019). Springer
Pfannschmidt, K., Gupta, P., Haddenhorst, B., Hüllermeier, E.: Learning context-dependent choice functions. Int. J. Approx. Reason. 140, 116–155 (2022)
Sifringer, B., Lurkin, V., Alahi, A.: Enhancing discrete choice models with representation learning. Transp. Res. B: Methodol. 140, 236–261 (2020)
Zhang, Y., Chen, F., Hakimi, S., Harinen, T., Filipowicz, A., Chen, Y.-Y., Iliev, R., Arechiga, N., Murakami, K., Lyons, K., Wu, C., Klenk, M.: Conjointnet: Enhancing conjoint analysis for preference prediction with representation learning. IJCAI M-PREF (2022)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Hao, Y., Dong, L., Wei, F., Xu, K.: Self-attention attribution: Interpreting information interactions inside transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 12963–12971 (2021)
Janocha, K., Czarnecki, W.M.: On loss functions for deep neural networks in classification (2017). arXiv:1702.05659
Iliev, R.: Social expected utility: Indifference to others can influence risk preferences. PsyArXiv (2022)
Yamada, R., Filipowicz, A., Boloor, M., Hogan, C., Toyoda, H.: Mixed fleet of bevs and phevs can meet transportation carbon emission targets without exceeding battery supply constraints. In preparation
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4-2 1(4), 1–4 (2015)
Arik, S.Ö., Pfister, T.: Tabnet: Attentive interpretable tabular learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6679–6687 (2021)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. In: Proceedings of ICLR (2014)
Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems 31 (2018)
Hilliard, A., Kazim, E., Bitsakis, T., Leutner, F.: Measuring personality through images: validating a forced-choice image-based assessment of the big five personality traits. J. Intell. 10(1), 12 (2022)
Dai, Y., Jayaratne, M., Jayatilleke, B.: Explainable personality prediction using answers to open-ended interview questions. Front. Psychol. 13, 2386 (2022)
Acknowledgements
We would like to thank Kate Sieck for her generous support of and enthusiasm for this work. We also would like to thank Candice Hogan for her support and encouragement, especially as we began to investigate these ideas.
Funding
No funds, grants, or other support was received.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflicts of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Chen, F., Zhang, Y., Nguyen, M. et al. Personalized choice prediction with less user information. Ann Math Artif Intell (2024). https://doi.org/10.1007/s10472-024-09927-9
Accepted:
Published:
DOI: https://doi.org/10.1007/s10472-024-09927-9