Thinking Fast and Slow: A CBR Perspective

Srashti Kaurav, Devi Ganesan, Deepak Padmanabhan, Sutanu Chakraborti

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

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In a path-breaking work, Kahneman characterized human cognition as a result of two modes of operation, Fast Thinking and Slow Thinking. Fast thinking involves quick, intuitive decision making and slow thinking is deliberative conscious reasoning. In this paper, for the first time, we draw parallels between this dichotomous model of human cognition and decision making in Case-based Reasoning (CBR). We observe that fast thinking can be operationalized computationally as the fast decision making by a trained machine learning model, or a parsimonious CBR system that uses few attributes. On the other hand, a full-fledged CBR system may be seen as similar to the slow thinking process. We operationalize such computational models of fast and slow thinking and switching strategies, as Models 1 and 2. Further, we explore the adaptation process in CBR as a slow thinking manifestation, leading to Model 3. Through an extensive set of experiments on real-world datasets, we show that such realizations of fast and slow thinking are useful in practice, leading to improved accuracies in decision-making tasks.
Original languageEnglish
Title of host publicationThe International FLAIRS Conference Proceedings, 34.
Publication statusEarly online date - 18 Apr 2021
Event34th Florida Artificial Intelligence Research Society Conference -
Duration: 17 May 202119 May 2021


Conference34th Florida Artificial Intelligence Research Society Conference
Abbreviated titleFLAIRS
Internet address


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