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This paper is a continuation of our paper10,11 and formulates a fuzzy team decision problem of type 2. The concept of fuzzy sets of type 2 is introduced to formulate the team decision processes which contain fuzzy‐fuzzy states, fuzzy‐fuzzy information functions, fuzzy‐fuzzy information signals, fuzzy‐fuzzy decision functions and fuzzy‐fuzzy actions. After some definitions of fuzzy‐fuzzy relations and fuzzy‐fuzzy mappings, a model of fuzzy team decision of type 2 is proposed.

Focuses on the applications of fuzzy set theory as a tool for the construction of multi‐criteria decision functions from specifications expressed in natural language. Starting with the ability to represent individual criteria satisfactions in terms of membership of fuzzy subsets, shows how different types of linguistic specifications are implemented. Consideration is given to the representation of trade‐offs between criteria, quantifier‐guided aggregations, conditioned criteria and possibilistically qualified criteria.

The claim that super intelligent machines constitute a major existential risk was recently defended in Nick Bostrom’s book Superintelligence and forms the basis of the sub-discipline AI risk. The purpose of this paper is to critically assess the philosophical assumptions that are of importance to the argument that AI could pose an existential risk and if so, the character of that risk.

This paper distinguishes between “intelligence” or the cognitive capacity of an individual and “techne”, a more general ability to solve problems using, for example, technological artifacts. While human intelligence has not changed much over historical time, human techne has improved considerably. Moreover, the fact that human techne has more variance across individuals than human intelligence suggests that if machine techne were to surpass human techne, the transition is likely going to be prolonged rather than explosive.

Some constraints for the intelligence explosion scenario are presented that imply that AI could be controlled by human organizations.

If true, this argument suggests that efforts should focus on devising strategies to control AI rather strategies that assume that such control is impossible.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The general problem to be considered in this paper is as follows: Given a general system defined by m attributes, find a meaningful partition of systems’ behaviors. Using an order preserving mapping from a space of m attributes into an m‐dimensional Euclidean space, a partitioning criterion of systems' behaviors is defined. Then, its mathematical properties are studied, on the basis of the usual hyperplane separation theorem and of the existence theorem of ton Neumann and Morgenstern's utility function.

The real estate industry has rapidly changed due to technological advances across residential and commercial real estate from the perspective of occupiers, investors, and service providers. Owners and buyers of properties have access to increasing information in the marketplace, including access to residential real estate platforms such as Zillow. Automated appraisals and artificial intelligence (AI) in the mortgage application process speed up home buying. Commercial real estate uses fintech to source deals, perform due diligence, and execute property management requests. This chapter includes a practitioner's view of the current and future information data needs, processes, and point solutions in the evolving technology landscape, including how tools such as ChatGPT apply. It concludes that the real estate fintech revolution has only begun, as data gaps in the real estate market require resolution before yielding better process automation and as the business model of real estate service providers shifts to strategic advisory roles.

The entropy as a measure of diversity has been used by ecologists to characterize a community by its stability process. After the introduction of the concept of a fuzzy subset by Zadeh (1965), many definitions of entropies were given emphasizing the subjectivity in evaluations. A pioneering work which relates the classical meaning of entropy (Shannon index) with the modern fuzzy theory was due to De Luca and Termini (1972); Knopfmacher (1975) formulated a generalization of the axiomatics given by De Luca and Termini; Batle and Trillas (1979) obtained a result which is essentially analogous to Knopfmacher’s by considering a finite fuzzy measure space and the Sugeno’s integrals; and Trillas and Riera (1978) introduced the concept of fuzzy algebraic entropics. Analyses the continuity properties for these fuzzy entropies and establishes conditions which guarantee the convergence E(f_n) → E(f), where (f_n) is a sequence of fuzzy sets and E is an entropy.

We investigated a fuzzy programming problem in which the constraints are a fuzzy subset over the alternatives and the objective is in the form of a linear ordering. A fuzzy subset is developed which reflects the objects ranking information in terms of grades of membership of the constraints. These two fuzzy subsets then are combined via intersection operation to form a fuzzy decision function.

In this paper the asymptotic and globally asymptotic stability of the zero solution of systems with impulses is investigated. For this purpose piecewise continuous auxiliary functions are used which are an analogue to Lyapunov's functions. The theorem of Marachkov on the asymptotic stability of systems without impulses is generalized. The results obtained are formulated in four theorems.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.