Abstract
Following previous discussions on extending rational assumptions and formal user models, this chapter introduces the research progress on factors associated with human bounded rationality, especially cognitive and perceptual biases, in IR and other closely related fields, including information seeking and recommender systems. By explaining and synthesizing the findings and methods extracted from empirical research on bounded rational IR, we hope to (1) clarify the existing progress and achievements that the research community has already made toward developing and applying intelligent bias-aware search support and, more importantly, (2) identify existing gaps, open challenges, and unsolved problems that may require further investigations. In addition, the knowledge learned from previous studies that leverage the theories from behavioral economics in user modeling, re-ranking, and simulation-based evaluation of algorithm performances can inform the development of our behavioral economics research agenda on bias-aware user modeling, search system design, and evaluation.
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Acknowledgment
The in situ search expectation study discussed in this chapter (Sect. 6.4) is supported by the National Science Foundation (NSF) Award IIS-2106152. Any opinions, findings, and conclusions or recommendations expressed in this work are those of the author and do not necessarily reflect those of the sponsor.
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Liu, J. (2023). Behavioral Economics in IR. 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_6
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