Three-way Decision Models of Cognitive Computing in Pythagorean Fuzzy Environments

Loss functions, commonly believed to be the cost of cognitive computing, are a key element in decision-making, and three-way decisions can be regarded as a cognitive computing method that seeks to minimize the overall risks involved in the decision-making process. Recently, many studies on loss functions have been conducted based on fuzzy sets, intuitionistic fuzzy sets, and interval intuitionistic fuzzy sets. However, most of these studies draw conclusions based on two descriptions, which may fail to capture the whole picture of decision-making. In this paper, in order to improve the accuracy of decision-making, we propose loss functions based on three descriptions, adding a hesitation description to the Pythagorean fuzzy environment. Then, we redefine the expected loss functions, which allow people to make a decision with more uncertainty. Subsequently, on the basis of the Bayesian minimum risk decision theory, four strategies for dealing with expected losses are proposed, and three-way decision models are established. Finally, group decision models are discussed. Three-way decision models of real value loss functions and Pythagorean fuzzy loss functions based on three descriptions are proposed, and data analyses of different parameters show the feasibility of the three-way decision models.