Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems

In this short paper, a critical analysis of the Neutrosophic, Pythagorean and some other novel fuzzy sets theories foundations is provided, taking into account that they actively used for the solution of the decision-making problems. The shortcomings of these theories are exposed. It is stated that the independence hypothesis, which is a cornerstone of the Neutrosophic sets theory, is not in line with common sense and therefore leads to the paradoxical results in the asymptotic limits of this theory. It is shown that the Pythagorean sets theory possesses questionable foundations, the sense of which cannot be explained reasonably. Moreover, this theory does not completely solve the declared problem. Similarly, important methodological problems of other analyzed theories are revealed. To solve the interior problems of the Atanassov’s intuitionistic fuzzy sets and to improve upon them, this being the reason most of the criticized novel sets theories were developed, an alternative approach based on extension of the intuitionistic fuzzy sets in the framework of the Dempster–Shafer theory is proposed. No propositions concerned with the improvement of the Cubic sets theory and Single-Valued Neutrosophic Offset theory were made, as their applicability was shown to be very dubious. In order to stimulate discussion, many statements are deliberately formulated in a hardline form.

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An AI view of the treatment of uncertainty

The Knowledge Engineering Review ◽

10.1017/s0269888900000795 ◽

1987 ◽

Vol 2 (2) ◽

pp. 75-97 ◽

Cited By ~ 37

Author(s):

Alessandro Saffiotti

Keyword(s):

Fuzzy Logic ◽

Complex Nature ◽

Dempster Shafer Theory ◽

Treatment Techniques ◽

Bayes Theory ◽

Theory Section ◽

Pros And Cons ◽

Fuzzy Logic Theory ◽

Inference Techniques ◽

Shafer Theory

AbstractThis paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.

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Fuzzy logic based game theory applications in multi-criteria decision making process

Journal of Intelligent & Fuzzy Systems ◽

10.3233/ifs-2012-0642 ◽

2013 ◽

Vol 25 (2) ◽

pp. 359-371 ◽

Cited By ~ 12

Author(s):

Hakan Soner Aplak ◽

Orhan Türkbey

Keyword(s):

Game Theory ◽

Decision Making ◽

Fuzzy Logic ◽

Decision Making Process ◽

Multi Criteria Decision Making

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DEVELOPMENT OF MODELS FOR DIAGNOSTIC DIABETES BASED ON FUZZY LOGIC METODS

СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ ◽

10.36622/vstu.2021.20.3.012 ◽

2021 ◽

pp. 90-95

Author(s):

Евгений Николаевич Коровин ◽

Екатерина Ивановна Новикова ◽

Олег Валерьевич Родионов

Keyword(s):

Mathematical Modeling ◽

Decision Making ◽

Information Technology ◽

Fuzzy Logic ◽

Human Body ◽

Patient Information ◽

Decision Making Process ◽

Speed Up ◽

Definition Of

В статье рассматриваются разработки методов интеллектуальной поддержки процесса диагностики сахарного диабета, а также определение его типа. В последние годы количество людей, страдающих данным заболеванием, неуклонно растет, а без своевременной диагностики эта патология может нанести огромный вред организму человека. Сахарный диабет 1 типа опасен тем, что в основном возникает у людей молодого возраста. Оперативное обнаружение диабета, а также определение его типа, поможет не только избежать возможных осложнений, но и в некоторых случаях предотвратить смерть пациента. Информационные технологии все чаще используются в различных сферах деятельности для разработки новых или совершенствования существующих методов обработки данных, особенно это можно заметить в сфере медицины. В настоящее время врач самостоятельно ставит диагноз, основываясь на результатах различных анализов, однако, для ускорения процесса принятия решения, можно воспользоваться методами математического моделирования, а именно: моделями диагностики диабета на основе нечеткой логики. Для наибольшего удобства данный способ распознавания заболевания впоследствии можно реализовать в информационно-программное обеспечение, которое сможет еще больше увеличить эффективность и скорость распознавания патологии The article discusses the issues of the incidence of diabetes in the population, in particular, the definition of its type. In recent years, the number of people suffering from this disease has been steadily growing, and without timely diagnosis, this pathology can cause enormous harm to the human body. Prompt detection of diabetes, as well as determination of its type, will help not only avoid possible complications, but also in some cases prevent the death of the patient. Information technology is increasingly being used in various fields of activity to develop new or improve existing methods of data processing, especially in the field of medicine. Currently, the doctor independently makes a diagnosis based on the results of various analyzes, however, to speed up the decision-making process, you can use the methods of mathematical modeling, namely, models of diabetes diagnostics based on fuzzy logic. For the greatest convenience, this method of disease recognition can subsequently be implemented in information software, which can further increase the efficiency and speed of pathology recognition

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Simulation of consumers decision-making process using agent-based model approach

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962319500375 ◽

2019 ◽

Vol 10 (06) ◽

pp. 1950037

Author(s):

Daniel Soto Forero ◽

Yony F. Ceballos ◽

German Sànchez Torres

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Real Data ◽

World Market ◽

Decision Making Process ◽

Agent Based ◽

Dynamic Social Network ◽

Proposed Model ◽

The World ◽

Model Approach

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network. The proposed model use theories and tools from the psychology of consumer behavior, social networks and complex dynamical systems like the Consumat framework and fuzzy logic. The model has been adjusted using real data, tested with the automobile market and it can recreate trends like those described in the world market.

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