{"title":"“d score” for type-2 fuzzy number incorporating the interaction between fast thinking and slow thinking","authors":"Kun Yu , Yuanzhen Xu , Xiaohan Yu , Bin Zhu","doi":"10.1016/j.fss.2025.109362","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>d</em> score of T2FN to reveal their interaction mechanism, where an interaction coefficient <em>θ</em> 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 <em>θ</em>. 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.</div></div>","PeriodicalId":55130,"journal":{"name":"Fuzzy Sets and Systems","volume":"510 ","pages":"Article 109362"},"PeriodicalIF":3.2000,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fuzzy Sets and Systems","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165011425001010","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies.
In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.