Анализ времени достижения консенсуса в рамках деятельности ТК
If you want to make beautiful music,
you must play the black and the white notes together.
Richard M. Nixon, the 37th President of USA
The problem of studying the achievement of consensus in social groups is related to the complexity of organizing such a study, especially for large groups, with more than five participants. In connection with the above, it is advisable to investigate the phenomenon of consensus in large social groups, using the modeling methodology.
The article presents the results of statistical modeling describing the dependence of the time to reach consensus on the number and authoritarianism of a social group members using two mathematical models of consensus achievement in a group based on the model proposed by DeGroot and model of the cellular automaton.
The main problems of attaining consensus under the settings of the proposed model during the development of consensus standards in technical committees on standardization were analyzed. It is shown that an increase in the number of social group members and their authoritarianism has an adverse impact on the time for reaching consensus and increases the disunity of the group.
A model of the cellular automaton modeling the achievement of consensus within the negotiation process has been were studied: the initial discrepancy between the opinions of the members of the group and the space of opinions of the members of the group. In particular, it is shown that if initially the views of the members of the group are radically different, then the process of reaching consensus will be as long as possible if one of the participants is absolutely authoritative. If initially the views of the members of the group are close, then the process of reaching consensus will also be as long as possible if both members of the group are absolutely compromise.
This book concentrates on in-depth explanation of a few methods to address core issues, rather than presentation of a multitude of methods that are popular among the scientists. An added value of this edition is that I am trying to address two features of the brave new world that materialized after the first edition was written in 2010. These features are the emergence of “Data science” and changes in student cognitive skills in the process of global digitalization. The birth of Data science gives me more opportunities in delineating the field of data analysis. An overwhelming majority of both theoreticians and practition-ers are inclined to consider the notions of ‘data analysis” (DA) and “machine learning” (ML) as synonymous. There are, however, at least two differences between the two. First comes the difference in perspectives. ML is to equip computers with methods and rules to see through regularities of the environment - and behave accordingly. DA is to enhance conceptual understanding. These goals are not inconsistent indeed, which explains a huge overlap between DA and ML. However, there are situations in which these perspectives are not consistent. Regarding the current students’ cognitive habits, I came to the conclusion that they prefer to immediately get into the “thick of it”. Therefore, I streamlined the presentation of multidimensional methods. These methods are now organized in four Chapters, one of which presents correlation learning (Chapter 3). Three other Chapters present summarization methods both quantitative (Chapter 2) and categorical (Chapters 4 and 5). Chapter 4 relates to finding and characterizing partitions by using K-means clustering and its extensions. Chapter 5 relates to hierarchical and separative cluster structures. Using encoder-decoder data recovery approach brings forth a number of mathematically proven interrelations between methods that are used for addressing such practical issues as the analysis of mixed scale data, data standardization, the number of clusters, cluster interpretation, etc. An obvious bias towards summarization against correlation can be explained, first, by the fact that most texts in the field are biased in the opposite direction, and, second, by my personal preferences. Categorical summarization, that is, clustering is considered not just a method of DA but rather a model of classification as a concept in knowledge engineering. Also, in this edition, I somewhat relaxed the “presentation/formulation/computation” narrative struc-ture, which was omnipresent in the first edition, to be able do things in one go. Chapter 1 presents the author’s view on the DA mainstream, or core, as well as on a few Data science issues in general. Specifically, I bring forward novel material on the role of DA, including its successes and pitfalls (Section 1.4), and classification as a special form of knowledge (Section 1.5). Overall, my goal is to show the reader that Data science is not a well-formed part of knowledge yet but rather a piece of science-in-the-making.
This Chapter is devoted to the study of the emergence and development of the international regulation of social security, its modern scope and specifics, as well as basic international principles, rights and freedoms in this sphere.
The article explores the procedural aspect of constructing structural and logical typologies with the aim of creating the innovation index - workers attitudes guiding innovation and innovation -related behavior at workplace.
This paper presents a preliminary analysis of hotel room prices in several European cities based on the data from Booking.com website. The main question raised in the study is whether early booking is advantageous indeed, and if so, how early should it be? First a script was developed to download more than 600 thousand hotel offers for reservations from 25 March 2013 to 17 March 2014. Then an attempt to discover more details concerning the early booking effect was made via basic statistics, graphical data representation and hedonic pricing analysis. It was revealed that making reservations in advance can be really gainful, although more data and research are needed to measure the exact numbers, as they depend on at least seasonality and city.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.