Our thinking process is averse to change. We as humans avoid uncertainty, and therefore we seek constant confirmation of our thoughts and we try to keep our perceptions to remain the same. These behaviours may be attributed to phenomena known as Cognitive Biases, which are systematic patterns of deviation from norm, leading to irrationality. Knowing how cognitive biases affect and influence mental processes represents a challenging task for researchers. Cognitive biases’ field is broad enough not to be understood and analysed in a single picture, leading to the need of dividing it into single smaller problems to manage. Several studies have been conducted with the purpose of analysing and mapping the cognitive biases’ literature, the majority of which are qualitative mapping studies, based on subjective and not scientific methods. The aim of this work is to analyse the cognitive biases’ literature by means of text-mining techniques, in an efficient, solid, and replicable approach. The purpose is also to provide researchers and industrial users with an instrument to “navigate” the field, both to enrich and enlarge it with new knowledge and to solve practical and real problems linked to cognitive biases. The proposed approach is based on a methodology which exploits the combination of three steps of analysis: a descriptive analysis for the identification of patterns of information and their evolution, an evaluative analysis for “co-citation” exploration, and a text-based analysis for the analysis of words (cognitive biases), co-occurrences, and frequencies. While the analyses are still running, preliminary results about the descriptive analysis have been obtained, showing paper’s distribution among the subject areas, paper’s publication growth over time and their geographical distribution. More comprehensive considerations will be drawn from the evaluative and the text-based analyses in the upcoming work.

Cognitive Biases: A Text-Mining Driven Scientific Literature Approach

Sale, Valentina;Martini, Antonella;Chiarello, Filippo
2021-01-01

Abstract

Our thinking process is averse to change. We as humans avoid uncertainty, and therefore we seek constant confirmation of our thoughts and we try to keep our perceptions to remain the same. These behaviours may be attributed to phenomena known as Cognitive Biases, which are systematic patterns of deviation from norm, leading to irrationality. Knowing how cognitive biases affect and influence mental processes represents a challenging task for researchers. Cognitive biases’ field is broad enough not to be understood and analysed in a single picture, leading to the need of dividing it into single smaller problems to manage. Several studies have been conducted with the purpose of analysing and mapping the cognitive biases’ literature, the majority of which are qualitative mapping studies, based on subjective and not scientific methods. The aim of this work is to analyse the cognitive biases’ literature by means of text-mining techniques, in an efficient, solid, and replicable approach. The purpose is also to provide researchers and industrial users with an instrument to “navigate” the field, both to enrich and enlarge it with new knowledge and to solve practical and real problems linked to cognitive biases. The proposed approach is based on a methodology which exploits the combination of three steps of analysis: a descriptive analysis for the identification of patterns of information and their evolution, an evaluative analysis for “co-citation” exploration, and a text-based analysis for the analysis of words (cognitive biases), co-occurrences, and frequencies. While the analyses are still running, preliminary results about the descriptive analysis have been obtained, showing paper’s distribution among the subject areas, paper’s publication growth over time and their geographical distribution. More comprehensive considerations will be drawn from the evaluative and the text-based analyses in the upcoming work.
2021
978-605-68816-8-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1102349
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