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A Behavioral Economics Approach to Interactive Information Retrieval

Understanding and Supporting Boundedly Rational Users

  • Book
  • © 2023

Overview

Authors:
  1. Jiqun Liu

    1. School of Library and Information Studies, University of Oklahoma, Norman, USA

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  • Introduces a behavioral economics framework for the development and evaluation of bias-aware information systems
  • Shows how an interdisciplinary approach can overcome seemingly irrational decisions not predicted by normative models
  • Intended for researchers in information retrieval and human-information interaction

Part of the book series: The Information Retrieval Series (INRE, volume 48)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Foundation

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Jiqun Liu
      Pages 3-22

    3. Formally Modeling Users in Information Retrieval

      • Jiqun Liu
      Pages 23-64

    4. From Rational Agent to Human with Bounded Rationality

      • Jiqun Liu
      Pages 65-89

  3. Beyond Rational Agents

    1. Front Matter

      Pages 91-91
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    2. Bounded Rationality in Decision-Making Under Uncertainty

      • Jiqun Liu
      Pages 93-130

    3. Back to the Fundamentals: Extend the Rational Assumptions

      • Jiqun Liu
      Pages 131-152

  4. Toward a Behavioral Economics Approach

    1. Front Matter

      Pages 153-153
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    2. Behavioral Economics in IR

      • Jiqun Liu
      Pages 155-180

    3. Implications and New Directions for IR Research and Practices

      • Jiqun Liu
      Pages 181-201

    4. Conclusion

      • Jiqun Liu
      Pages 203-207

  5. Back Matter

    Pages 209-211
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Keywords

About this book

This book brings together the insights from three different areas, Information Seeking and Retrieval, Cognitive Psychology, and Behavioral Economics, and shows how this new interdisciplinary approach can advance our knowledge about users interacting with diverse search systems, especially their seemingly irrational decisions and anomalies that could not be predicted by most normative models.

The first part “Foundation” of this book introduces the general notions and fundamentals of this new approach, as well as the main concepts, terminology and theories. The second part “Beyond Rational Agents” describes the systematic biases and cognitive limits confirmed by behavioral experiments of varying types and explains in detail how they contradict the assumptions and predictions of formal models in information retrieval (IR). The third part “Toward A Behavioral Economics Approach” first synthesizes the findings from existing preliminaryresearch on bounded rationality and behavioral economics modeling in information seeking, retrieval, and recommender system communities. Then, it discusses the implications, open questions and methodological challenges of applying the behavioral economics framework to different sub-areas of IR research and practices, such as modeling users and search sessions, developing unbiased learning to rank and adaptive recommendations algorithms, implementing bias-aware intelligent task support, as well as extending the conceptualization and evaluation on IR fairness, accountability, transparency and ethics (FATE) with the knowledge regarding both human biases and algorithmic biases.


This book introduces a behavioral economics framework to IR scientists seeking a new perspective on both fundamental and new emerging problems of IR as well as the development and evaluation of bias-aware intelligent information systems. It is especially intended for researchers working on IR and human-information interaction who want to learn about the potential offered by behavioral economics in their own research areas.

Authors and Affiliations

  • School of Library and Information Studies, University of Oklahoma, Norman, USA

    Jiqun Liu

About the author

Jiqun Liu is an Assistant Professor of Data Science and Adjunct Assistant Professor of Psychology at the University of Oklahoma (OU). His research focuses on the intersection of human-centered data science, interactive information seeking/retrieval, and cognitive psychology, and seeks to apply the knowledge learned about people interacting with information in adaptive recommendation and de-biasing, user education and intelligent nudging. He has published extensively and advised students from diverse backgrounds on these research topics, obtained grant support from varying sources, and received best paper and poster awards at leading conferences.

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