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.

A dichotomous Case-Based Reasoning (CBR) model is one in which two kinds of reasoning mechanisms are employed for realizing fast and slow problem-solving as demanded by the nature of the incoming query. This is inspired by Daniel Kahneman's seminal work on the two modes of thinking observed in humans. In this paper, we present the following three directions of refinement for a dichotomous CBR model: selection of attributes for a fast thinking model based on parsimonious CBR, switching from fast to slow thinking based on constraints derived from domain knowledge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.