Proceedings of Philisophy of Law International Symposium "Rationality in Law" (Buenos Aires, 5-7 May, 2014)
Into the Red explores the emergence of a credit card market in post-Soviet Russia during the formative period from 1988 to 2007. In her analysis, Alya Guseva locates the dynamics of market building in the social structure, specifically the creative use of social networks. Until now, network scholars have overlooked the role that networks play in facilitating exchange in mass markets because they have exclusively focused on firm-to-firm or person-to-person ties. Into the Reddemonstrates how networks that combine individuals and organizations help to build markets for mass consumption. The book is situated on the cutting edge of emerging interdisciplinary research, linking multiple layers of analysis with institutional evolution. Using an intricate framework, Guseva chronicles both the creation of a credit card market and the making of a mass consumer. These processes are placed in the context of the ongoing restructuring in postcommunist Russia and the expansion of Western markets and ideologies through the rest of the world.
The proceedings of the conference "Rationality in Action: Intentions, Interpretations and Interactions". The project has been carried out as part of the HSE Program of Fundamental Studies.
Supply chain management is rather new scientific field that reflects the concept of integrated business planning. This concept should be experts and practitioners in logistics and strategic management. Today, integrated planning to become a reality thanks to the development of information technology and computer technology. At the same time to achieve a competitive advantage is not enough high-speed, low-cost data transfer process. In order to effectively apply information technology tools necessary to develop a quantitative analysis of the effectiveness of supply chain management. The mam element of this tool are optimization models that reveal the complex interactions, the wave and the synergies that arise in supply chain management. In this article we consider one of the classes of such models - the so-called dynamic models of conveyor systems, processing of applications.
Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.