Abstract
Our book brings together the multidisciplinary insights, methods, and empirical findings related to bounded rationality and human biases in decision-making and presents a behavioral economics research agenda under which a series of specific research questions, new directions, and methodological challenges can be further investigated by students and researchers in future IR studies. In this final chapter, we summarize the contents of previous chapters and discuss the contributions, practical implications, and related new directions under our behavioral economics research approach to IR problems. We hope that this book can serve as a useful starting point for studying bias-aware IR and motivate students and researchers from diverse backgrounds to further explore and advance the science and technology on supporting boundedly rational people interacting with information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Text Retrieval Conference (TREC): https://trec.nist.gov
- 2.
A new research community emerged and is growing rapidly around the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT): https://facctconference.org/
References
Agosto, D. E. (2002). Bounded rationality and satisficing in young people’s web-based decision making. Journal of the American Society for Information Science and Technology, 53(1), 16–27. https://doi.org/10.1002/asi.10024
Azzopardi, L. (2021). Cognitive biases in search: A review and reflection of cognitive biases in information retrieval. In Proceedings of the 2021 ACM SIGIR conference on human information interaction and retrieval (pp. 27–37). ACM. https://doi.org/10.1145/3406522.3446023
Barbosa, N. M., & Chen, M. (2019). Rehumanized crowdsourcing: A labeling framework addressing bias and ethics in machine learning. In Proceedings of the 2019 ACM SIGCHI conference on human factors in computing systems (pp. 1–12). ACM. https://doi.org/10.1145/3290605.3300773
Dingler, T., Tag, B., Karapanos, E., Kise, K., & Dengel, A. (2020). Workshop on detection and design for cognitive biases in people and computing systems. In Extended abstracts of the 2020 ACM SIGCHI conference on human factors in computing systems (pp. 1–6). ACM. https://doi.org/10.1145/3334480.3375159
Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021). A checklist to combat cognitive biases in crowdsourcing. In Proceedings of the AAAI conference on human computation and crowdsourcing (Vol. 9, pp. 48–59) https://ojs.aaai.org/index.php/HCOMP/article/view/18939
Ge, Y., Zhao, S., Zhou, H., Pei, C., Sun, F., Ou, W., & Zhang, Y. (2020). Understanding echo chambers in e-commerce recommender systems. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 2261–2270). https://doi.org/10.1145/3397271.3401431
Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143. https://doi.org/10.1111/j.1756-8765.2008.01006.x
Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475. https://doi.org/10.1257/000282803322655392
Kelly, D. (2009). Methods for evaluating interactive information retrieval systems with users. Foundations and Trends in Information Retrieval, 3(1–2), 1–224. https://doi.org/10.1561/1500000012
Lee, M. K., & Rich, K. (2021). Who is included in human perceptions of AI? Trust and perceived fairness around healthcare AI and cultural mistrust. In Proceedings of the 2021 ACM SIGCHI conference on human factors in computing systems (pp. 1–14). ACM. https://doi.org/10.1145/3411764.3445570
Liu, J., & Han, F. (2020). Investigating reference dependence effects on user search interaction and satisfaction: A behavioral economics perspective. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1141–1150). ACM. https://doi.org/10.1145/3397271.3401085
Liu, J., & Shah, C. (2019). Interactive IR user study design, evaluation, and reporting. Synthesis Lectures on Information Concepts, Retrieval, and Services, 11(2), i–93. https://doi.org/10.2200/S00923ED1V01Y201905ICR067
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1–35. https://doi.org/10.1145/3457607
Saab, F., Elhajj, I. H., Kayssi, A., & Chehab, A. (2019). Modelling cognitive bias in crowdsourcing systems. Cognitive Systems Research, 58, 1–18. https://doi.org/10.1016/j.cogsys.2019.04.004
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST special publication, 1270.
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Taniguchi, H., Sato, H., & Shirakawa, T. (2018). A machine learning model with human cognitive biases capable of learning from small and biased datasets. Scientific Reports, 8(1), 1–13. https://doi.org/10.1038/s41598-018-25679-z
Thaler, R. H. (2016). Behavioral economics: Past, present, and future. American Economic Review, 106(7), 1577–1600. https://doi.org/10.1257/aer.106.7.1577
Weber, R. A., & Camerer, C. F. (2006). “Behavioral experiments” in economics. Experimental Economics, 9(3), 187–192. https://doi.org/10.1007/s10683-006-9121-5
Xu, Z., Han, Y., Zhang, Y., & Ai, Q. (2020). E-commerce recommendation with weighted expected utility. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 1695–1704). ACM. https://doi.org/10.1145/3340531.3411993
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Liu, J. (2023). Conclusion. In: A Behavioral Economics Approach to Interactive Information Retrieval. The Information Retrieval Series, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-031-23229-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-23229-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23228-2
Online ISBN: 978-3-031-23229-9
eBook Packages: Computer ScienceComputer Science (R0)