Fundamentals of the fuzzy logic-based generalized theory of decisions

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Everyday decision-making and choices in complex human-centric systems often involve imperfect information. A significant limitation of current decision theories is their inability to address this imperfection and to model vague preferences. The concept of non-numerical probabilities in decision-making has historical roots, including Keynes’s analysis of uncertainty. There is a pressing need to advance decision theories that accommodate perception-based imperfect information articulated in natural language (NL). New decision models should utilize human-centric computational schemes rather than binary logic to process NL-described information. The development of these theories is now feasible due to enhanced computational power, enabling the handling of complex computations involving imprecise and partially true information. This work lays the groundwork for a novel decision theory that incorporates imperfect decision-relevant information concerning the environment and decision-maker behavior. It synthesizes fuzzy sets theory with perception-based information and probability theory. The content is self-contained, systematically presenting decision theory with imperfect information suitable for educational systems. This resource will benefit university and college educators and students, as well as managers and specialists across various sectors, including business, economics, production, and social fields.