Логические средства когнитивной социологии
The problems of developing computer systems that perform intellectual analysis of empirical data in fields with weakly formalized knowledge are described. The JSM system for analysis of nonquantitative sociological data is presented as an example of the implementation of such a system.
In recent years there has been a growing interest in cognition within sociology and other social sciences. Within sociology this interest cuts across various topical subfields, including culture, social psychology, religion, race, and identity. Scholars within the new subfield of cognitive sociology, also referred to as the sociology of culture and cognition, are contributing to a rapidly developing body of work on how mental and social phenomena are interrelated and often interdependent. In The Oxford Handbook of Cognitive Sociology, Wayne H. Brekhus and Gabe Igantow have gathered some of the most influential scholars working in cognitive sociology to present an accessible introduction to key research areas in a diverse field. While classical sociological and newer interdisciplinary approaches have been covered separately by scholars in the past, this volume alternatively presents a broad range of cognitive sociological perspectives. The contributors discuss a range of approaches for theorizing and analyzing the "social mind," including macro-cultural approaches, interactionist approaches, and research that draws on Pierre Bourdieu's major concepts. Each chapter further investigates a variety of cognitive processes within these three approaches, such as attention and inattention, perception, automatic and deliberate cognition, cognition and social action, stereotypes, categorization, classification, judgment, symbolic boundaries, meaning-making, metaphor, embodied cognition, morality and religion, identity construction, time sequencing, and memory. A comprehensive look at cognitive sociology's main contributions and the central debates within the field, the Handbook will serve as a primary resource for social researchers, faculty, and students interested in how cognitive sociology can contribute to research within their substantive areas of focus.
In this chapter, I argue that the Durkheimian theory of the sacred is a crucial yet not fully recognized resource for cognitive sociology. It contains not only a theory of culture (which is acknowledged in contemporary sociology), but also a vision of culture-cognition relations. Thus, Durkheimian cultural sociology allows us to understand the crucial role the sacred/profane opposition plays in structuring culture, perception and thought. Based on a number of theories, I also show how another opposition – between the pure and impure modes of the sacred, allows us to explain dynamic features of the sacred and eventually provides a basic model of social change. While explicating this vision and resultant opportunities for sociological analysis I also criticize ‘cognition apart from culture’ approaches established within cognitive sociology. I argue, thus, that culture not only participates in cognition but is an intrinsic ingredient of the human mind. Culture is not a chaotic and fragmented set of elements, as some sociologists imply to a greater or lesser degree, but a system; and as such it is an inner environment for human thought and social action. This system, however, is governed not by formal logic, as some critics of the autonomy of culture presuppose, but by concrete configurations of emotionally-charged categories, created and re-created in social interactions.
This book gathers contributions presented at the 7th International Conference on Soft Methods in Probability and Statistics SMPS 2014, held in Warsaw (Poland) on September 22-24, 2014. Its aim is to present recent results illustrating new trends in intelligent data analysis. It gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics. Synergies of both fields might improve intelligent data analysis methods in terms of robustness to noise and applicability to larger datasets, while being able to efficiently obtain understandable solutions of real-world problems.
Success of any logistics enterprise in the context of digital economy progress directly depends on regular and effective innovations to the area of improving analytical applications and information systems in such actively developing fields of knowledge as strategic management, distribution networks development, and supply chain management. In an effort to ensure a sustainable economic circumstance under conditions of strong competition, the most perspective companies are increasingly focusing on the development and introduction of modern methods and tools for intelligent data analysis. The article focuses on the consideration of issues related to the use of modern simulation approaches and such components of the soft computing concept as neural networks, fuzzy logic and evolutionary computations in solving the problems of multifunctional logistics and supply chain management.