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Applied analysis of international relations began to form at MGIMO-University in the 1970s. This kind of research always attracted considerable interest of the Ministry of Foreign Affairs of the USSR, and other executive institutions of the government and received their support. The Ministry of Foreign Affairs initiated the creation of a special unit at MGIMO - the Problem Research Laboratory of Systems Analysis in International Relations. The Laboratory was using system analysis and quantitative methods to produce scientific information for decision-makers to make "more informed decisions in the field of international relations in order to reduce the level of uncertainty in the assessment of the expected impact of these decisions". In 2004, the successor to the Problem Laboratory - Center for International Studies - was transformed into a Research Coordination Council for International Studies, which in 2009 handed its functions to the Institute of International Studies. In comparison with previous periods the Institute of International Studies has significantly increased of research for the Ministry of International Affairs. It has also moved functionally outside its institutional boundaries and produces unclassified research for public offer. It also serves as a place for vivid public discussions among IR specialists. There's also an international recognition of the Institute of International Studies. The "Go to think tanks" international ranking produced annually at the University of Pennsylvania has put MGIMO-University on the 10th place in the category of university based think tanks.

The paper describes the application to longitudinal research of a method of data collection which combines quantitative and qualitative material. It is claimed that Group Feedback Analysis (GFA) helps to validate data collected by various methods and produces additional ethnographic material and insight into the process under investigation. When the people who supply the data are experienced in the subject matter, as is the case with managers and other employees in organizational research, GFA makes it possible for them to help with the interpretation of the data instead of leaving it entirely to the researcher. The method, which has previously been used in cross-sectional research, is shown to have utility in a longitudinal design where it can facilitate organizational learning. It can also contribute to the refinement of a theoretical model.

"Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture, environmental sciences, and chemistry. General introductions to multiway data types, methods, and estimation procedures are provided in addition to detailed explanations and advice for readers who would like to learn more about applying multiway methods. Using carefully laid out examples and engaging applications, the book begins with an introductory chapter that serves as a general overview of multiway analysis, including the types of problems it can address. Next, the process of setting up, carrying out, and evaluating multiway analyses is discussed along with commonly-encountered issues, such as preprocessing, missing data, model and dimensionality selection, postprocessing, transformation, as well as robustness and stability issues"--Provided by publisher

Benefit-cost analysis reduces all of the impacts of a proposed policy change to a common unit of measurement. It is used in a wide variety of fields including agriculture, life and health, transportation and the environment. In this single volume the editors, both leading scholars in their field, present a judicious selection of previously published papers indispensable to the study of applied benefit-cost analysis. The comprehensive collection is an essential resource to scholars, researchers and policymakers alike

The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authenticated to employ in forecasting the stock index volatility appropriately.