Search under Uncertainty: Cognitive Biases and Heuristics - Tutorial on Modeling Search Interaction using Behavioral Economics

Leif Azzopardi, Jiqun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Modeling how people interact with search interfaces is core to the field of Interactive Information Retrieval. While various models have been proposed ranging from conceptual (e.g., Belkin's ASK[12], Berry picking[11], Everyday-life information seeking, etc.) to theoretical (e.g., Information foraging theory[50], Economic theory[4], etc.), more recently there has been a body of working explore how people's biases and the heuristics that they take influence how they search. This has led to the development of new models of the search process drawing upon Behavioural Economics and Psychology. This half day tutorial will provide a starting point for researchers seeking to learn more about information searching under uncertainty. The tutorial will be structured into two parts. First, we will provide an introduction of the biases and heuristics program put forward by Tversky and Kahneman [59] which assumes that people are not always rational. The second part of the tutorial will provide an overview of the types and space of biases in search [6, 42], before doing a deep dive into several specific examples and the impact of biases on different types of decisions (e.g., health/medical, financial etc.). The tutorial will wrap up with a discussion of some of the practical implication for how we can better design and evaluate IR systems in the light of cognitive biases.

Original languageEnglish
Title of host publicationCHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval
EditorsPaul Clough, Morgan Harvey, Frank Hopfgartner
PublisherAssociation for Computing Machinery (ACM)
Pages427-430
Number of pages4
ISBN (Electronic)9798400704345
DOIs
Publication statusPublished - 10 Mar 2024

Keywords

  • Cognitive Bias
  • Evaluation
  • Information Retrieval
  • Retrieval Strategies
  • Search Behaviour
  • User Models
  • Web Search

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