A Behavioral Economics Approach to Interactive Information Retrieval: Understanding and Supporting Boundedly Rational Users

This book combines insights from Information Seeking and Retrieval, Cognitive Psychology, and Behavioral Economics to advance our understanding of users interacting with search systems. It highlights the biases and cognitive limits that contradict traditional models and proposes a behavioral economics approach to IR research and practices. The book is intended for researchers in IR and human-information interaction who want to explore the potential of behavioral economics in their work.

Format: Hardback
Length: 211 pages
Publication date: 18 February 2023
Publisher: Springer International Publishing AG

This groundbreaking book brings together insights from three distinct fields: Information Seeking and Retrieval, Cognitive Psychology, and Behavioral Economics, to explore how this interdisciplinary approach can enhance our understanding of users' interactions with diverse search systems, particularly their seemingly irrational decisions and anomalies that defy conventional normative models. The first part, titled "Foundation," delves into the fundamental concepts, terminology, and theories that underpin this novel approach. It introduces the main ideas and principles that guide this interdisciplinary endeavor. The second part, "Beyond Rational Agents," takes a deep dive into the systematic biases and cognitive limitations revealed through behavioral experiments of various types. It meticulously explains how these biases and limitations contradict the assumptions and predictions of traditional information retrieval (IR) models. The third part, "Toward A Behavioral Economics Approach," synthesizes the findings from existing preliminary research on bounded rationality and behavioral economics modeling in the fields of information seeking, retrieval, and recommender systems. It then discusses the implications, open questions, and methodological challenges of applying the behavioral economics framework to diverse sub-areas of IR research and practices, including user modeling, learning to rank algorithms, adaptive recommendations, bias-aware intelligent task support, and extending the conceptualization and evaluation of IR fairness, accountability, transparency, and ethics (FATE) with insights into both human biases and algorithmic biases. This book serves as a valuable resource for information retrieval (IR) scientists seeking a fresh perspective on both fundamental and emerging problems within the field, as well as those interested in exploring innovative solutions to address the complexities of user behavior in search systems.

Weight: 512g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031232282
Edition number: 1st ed. 2023