TY - JOUR
T1 - “d score” for type-2 fuzzy number incorporating the interaction between fast thinking and slow thinking
AU - Yu, Kun
AU - Xu, Yuanzhen
AU - Yu, Xiaohan
AU - Zhu, Bin
N1 - Publisher Copyright:
© 2025 Elsevier B.V.
PY - 2025/6/15
Y1 - 2025/6/15
N2 - Modeling human thinking process is essential in decision making. According to dual-process theory, fast thinking and slow thinking in our brains shape our judgments and decisions. Type-2 fuzzy number (T2FN) models the shaping process with these two thinking patterns. However, how to model the interaction between them is a mystery, which can be referred as interactive thinking. In this paper, we propose a d score of T2FN to reveal their interaction mechanism, where an interaction coefficient θ is used to measure the interaction. This score describes fast and slow thinking patterns and the transformation process between them, and how the interaction helps decision makers make better decisions. We propose two optimization models to solve for the coefficient θ. With the collected data based on questionnaires, we find a dominant role of interactive thinking in practice. In particular, it is most common that fast thinking and slow thinking play equally important roles in forming our judgments. Moreover, our results suggest that fast thinking tends to correspond with questions that contain complex information, while slow thinking tends to correspond with simple information.
AB - Modeling human thinking process is essential in decision making. According to dual-process theory, fast thinking and slow thinking in our brains shape our judgments and decisions. Type-2 fuzzy number (T2FN) models the shaping process with these two thinking patterns. However, how to model the interaction between them is a mystery, which can be referred as interactive thinking. In this paper, we propose a d score of T2FN to reveal their interaction mechanism, where an interaction coefficient θ is used to measure the interaction. This score describes fast and slow thinking patterns and the transformation process between them, and how the interaction helps decision makers make better decisions. We propose two optimization models to solve for the coefficient θ. With the collected data based on questionnaires, we find a dominant role of interactive thinking in practice. In particular, it is most common that fast thinking and slow thinking play equally important roles in forming our judgments. Moreover, our results suggest that fast thinking tends to correspond with questions that contain complex information, while slow thinking tends to correspond with simple information.
KW - Fast thinking
KW - Interaction coefficient
KW - Slow thinking
KW - Type-2 fuzzy number
KW - d score
UR - http://www.scopus.com/inward/record.url?scp=86000610469&partnerID=8YFLogxK
U2 - 10.1016/j.fss.2025.109362
DO - 10.1016/j.fss.2025.109362
M3 - Article
AN - SCOPUS:86000610469
SN - 0165-0114
VL - 510
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
M1 - 109362
ER -