Search under uncertainty: Cognitive biases and heuristics : a tutorial on testing, mitigating and accounting for cognitive biases in search experiments

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Liu, Jiqun and Azzopardi, Leif; (2024) Search under uncertainty: Cognitive biases and heuristics : a tutorial on testing, mitigating and accounting for cognitive biases in search experiments. In: SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery (ACM), USA, pp. 3013-3016. ISBN 979-8-4007-0431-4 (https://doi.org/10.1145/3626772.3661382)

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Abstract

Understanding how people interact with search interfaces is core to the field of Interactive Information Retrieval (IIR). While various models have been proposed (e.g., Belkin's ASK, Berry picking, Everyday-life information seeking, Information foraging theory, Economic theory, etc.), they have largely ignored the impact of cognitive biases on search behaviour and performance. A growing body of empirical work exploring how people's cognitive biases influence search and judgments, has led to the development of new models of search that draw upon Behavioural Economics and Psychology. This full day tutorial will provide a starting point for researchers seeking to learn more about information seeking, search and retrieval under uncertainty. The tutorial will be structured into three parts. First, we will provide an introduction of the biases and heuristics program put forward by Tversky and Kahneman [60] (1974) 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,[5, 40] before doing a deep dive into several specific examples and the impact of biases on different types of decisions (e.g., health/medical, financial). The third part will focus on a discussion of the practical implication regarding the design and evaluation human-centered IR systems in the light of cognitive biases - where participants will undertake some hands-on exercises.

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