"In dem Beitrag wird Fuzzy-Logik als mögliche neue Grundlage für die Soziologie vorgestellt. Nach einer allgemeinen Einführung in die Grundlagen werden bestimmte Vorteile der Fuzzy-Logik für die Empirie am Beispiel der sog. 'Qualitativ-vergleichenden Analyse' (QCA) und für die soziologische Theorie am Beispiel der Handlungstheorie von Hartmut Esser exemplarisch vorgeführt. Insgesamt verspricht der Einsatz von Fuzzy-Logik für die Soziologie eine einfachere Modellierung komplexer sozialer Sachverhalte, als dies bisher möglich gewesen ist." (Autorenreferat)
Main description: Erstmalig werden Konzepte der Fuzzy Set-Theorie auf Problemstellungen der Agency-Theorie angewendet. Die Agency-Theorie analysiert u. a. Auftraggeber-Auftragnehmerbeziehungen, in denen Informationen ungleich verteilt sind. Es werden Verträge, Entlohnungs- bzw. Controllingsysteme entwickelt, die für den Auftraggeber vorteilhafte Ergebnisse erzielen. In diesen Situationen existieren Sachverhalte, die nur vage bzw. unscharf (fuzzy) beschreibbar sind.Der Autor bezieht diese vagen Informationen erstmals in die Analyse der Auftraggeber-Auftragnehmerbeziehungen ein und entwickelt ökonomische Modelle, welche die vorhandenen Informationsstände realitätsnah abbilden. In zwei zentralen Grundproblemen der Agency-Theorie werden die Auswirkungen von vagen Informationen analysiert. Die ermittelten Vertrags- bzw. Entlohnungssysteme zeigen, wie die vagen Informationen Entlohnungsbestandteile beeinflussen und welche zusätzlichen Aktionsmöglichkeiten sich für die Vertragspartner ergeben.Der Verfasser eröffnet mit der Fuzzy Agency-Theorie einen vielversprechenden Weg, wie neue Erkenntnisse zur Gestaltung von Entlohnungssystemen gewonnen werden können
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AbstractAny system small, medium or large can function effectively if all aspects relating to the system are considered in detail and proper planning is done to organize the various activities involved. It is always difficult to define in quantitative terms the attention needed to various aspects of the program planning for optimum utilization of input resources. Program Planning based on detailed analysis of the system is, therefore, a necessity. The utility of program planning can be increased further by application of fuzzy set theory to incorporate the qualitative aspects associated with the system.The paper deals with the application of Fuzzy Program Planning to 'Energy Conservation in Indian Cement Industry', identifying and analysing various elements and sub‐elements of the program, the key elements and the key sub‐elements based on Possibility of Interactions (POI) between them. Ranking of each sub‐element based on the POI has also been worked out and compared with rankings worked out using binary (0–1) analysis.Identification of key sub‐elements, their interaction with other sub‐elements and the ranking based on the fuzzy interaction is an extension of the work already done in the field of program planning. The superiority of Fuzzy Program Planning over the Program Planning based on binary (0–1) analysis has also been demonstrated successfully.
Tracing the development of economics since the 19th century up to the present day makes it evident that at its core there is a sequence of rather precise and mathematically sophisticated axiomatic theories. At the same time, there is always a noticeable and persistent gap between economic reality and the economic predictions derived from these theories. The main reason why economic theories have not been successful so far in modeling economic reality is the fact that these theories are formulated in terms of hard sciences characterized by their nature of preciseness. In economics, as in any complex multi-agent humanistic system, motivations, intuition, human knowledge, and human behavior, such as perception, emotions, and norms, play dominant roles. Consequently, real economic and socioeconomic world problems are too complex to be translated into classical mathematical and bivalent logic languages, solved and interpreted in the language of the real world. The traditional modeling methodology (economics deals with models of economic reality) is perhaps not relevant or at least not powerful enough to satisfy the requirements of human reasoning and decision making activities. A new much more effective modeling language is needed to capture economic reality. According to Prof. L. Zadeh, in general, fuzzy-logic-based modeling languages have a higher power of cointension than their bivalent logic-based counterparts and present the potential for playing an essential role in modeling economic, social, and political systems. The sheer complexity of causation in the economic arena mandates a fuzzy approach. We argue that many economic dynamical systems naturally become fuzzy due to the uncertain initial conditions and parameters. In this study we consider an economic system as a human centric and imperfect information-based realistic multi-agent system with fuzzy-logic-based representation of the economic agents' behavior and with imprecise constraints. We use fuzzy "if-then" language and fuzzy differential equations for modeling the economic agents. This paper looks beyond the standard assumptions of economics that all people are similarly rational and self-interested. To be able to incorporate motivation input variables of economic agents into their models, we created behavioral model of agents by using the fuzzy and Bayes-Shortliffe approaches. Nowadays, adding norms and motivations to utility function has become an issue for economists and business people. They also recognized that something is missing in standard utility function and even in standard economic theory. Everything is not as simple as just profit or utility maximization under budget constraint. They recognized that during all these years they have been ignoring the most important thing: the norms and motivations that actually directed the human nature of the decision makers. It is obvious that these norms and motivations cannot be captured by statistical analysis, there are deeper and more subtle uncertain relationships between well-being and its determinants. We suggest the fuzzy graph-based approach to utility function construction. The proposed approach is consistent with the behavioral and uncertainty paradigms of the decision makers. Here we put the emphasis on mathematical background rather than real case analysis. The suggested fuzzy approaches to economic problem-solving are applied to fuzzy decision making in an oligopolistic economy.
This paper describes two computer-aided design (CAD) tools for automatic synthesis of fuzzy logic-based inference systems. The tools share a common architecture for efficient hardware implementation of fuzzy modules, but are based on two different design strategies. One of them is focused on the generation of standard VHDL code, which can be later implemented on a reconfigurable device [field-programmable gate array (FPGA)] or as an application-specific integrated circuit (ASIC). The other one uses the Matlab/Simulink environment and tools for development of digital signal processing (DSP) systems on Xilinx's FPGAs. Both tools are included in the last version of Xfuzzy, which is a specific environment for designing complex fuzzy systems, and they provide interfaces to commercial VHDL synthesis and verification tools, as well as to conventional FPGA development environments. As demonstrated by the included design example, the proposed development strategies speed up the stages of description, synthesis, and functional verification of embedded fuzzy inference systems. ; This work was partially funded by Spanish Ministerio de Economía y Competitividad under the Project TEC2011-24319 and by Junta de Andalucía under the Project P08-TIC-03674 (both with support from FEDER), and by the European Community through the MOBY-DIC Project FP7-INFSO-ICT-248858 (www.mobydic-project.eu). P. Brox is supported under the post-doctoral program "Juan de la Cierva" from the Spanish Government. ; This work was partially funded by Spanish Ministerio de Economía y Competitividad under the Project TEC2011-24319 and by Junta de Andalucía under the Project P08-TIC-03674 (both with support from FEDER), and by the European Community through the MOBY-DIC Project FP7-INFSO-ICT-248858 (www.mobydic-project.eu). P. Brox is supported under the post-doctoral program "Juan de la Cierva" from the Spanish Government. ; Peer Reviewed
[EN] Different types of fuzzy uniformities have been introduced in the literature standing out the notions due to Hutton, Hohle and Lowen. The main purpose of this paper is to study several methods to endow a fuzzy metric space (X, M, *), in the sense of George and Veeramani, with a probabilistic uniformity and with a Hutton [0, 1](-quasi)-uniformity. We will show the functorial behavior of these constructions as well as its relation with respect to Lowen's functors and Katsaras's functors, which establish a relationship between the categories of probabilistic uniformities and Hutton [0, 1](-quasi)-uniformities with the category of classical uniformities respectively. Furthermore, we also study the fuzzy topologies associated with these fuzzy uniformities. (C) 2017 Elsevier B.V. All rights reserved. ; The first named author acknowledges the support of the grants MTM2015-63608-P (MINECO/FEDER, UE) and IT974-16 (Basque Government).The second named author is supported by the grant MTM2015-64373-P (MINECO/FEDER, UE). ; Gutierrez Garcia, J.; Rodríguez López, J.; Romaguera Bonilla, S. (2018). On fuzzy uniformities induced by a fuzzy metric space. Fuzzy Sets and Systems. 330:52-78. https://doi.org/10.1016/j.fss.2017.05.001 ; S ; 52 ; 78 ; 330
"The well-known distinction between soft and hard science cuts a sharp line of demarcation between hard and soft facts of scientific studies. Physics deal with precise hard facts characteristically whereas social sciences are confronted with imprecise soft social facts because social facts are notoriously vague, interpretative facts of meaning. Therefore Fuzzy logic seems to fit perfectly the needs of social scientist that look for mathematical precise models to deal with vague, imprecise data [52]. In this contribution we discuss the usefulness of Fuzzy logic for social sciences in general, and especially sociology. In a first step we summarize some fundamentals of 'fuzzy thinking' for social scientist. This will lead to the discussion of the need of fuzzy thinking in action theory, systems theory, modernization theory and empirical research.We discuss the advantage of fuzzy thinking for action theory and social systems theory at length whereas the discussion of fuzzy thinking in modernization theory and empirical research falls short. Modernization theory and empirical research just function as further examples for the need and usefulness of fuzzy thinking." (excerpt)
