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This 1997 book presents developments in nonlinear economic dynamics along with related research from other fields, including mathematics, statistics, biology, and physics.
Business transactions and partnerships across borders have become easier than ever due to globalization and global digital connectivity. As part of this shift in the business sphere, managers, executives, and strategists across industries must acclimate themselves with the challenges and opportunities for conducting business globally. International Business: Concepts, Methodologies, Tools, and Applications presents the latest research innovations focusing on cross-cultural communications and training, international relations, multinational enterprises, outsourcing, international business strategies, and competitive advantage in the global marketplace. This publication is an exhaustive multi-volume work essential to academic and corporate libraries who serve researchers, scholars, business executives and professionals, and graduate-level business students.
This collection illustrates how nonlinear methods can provide new insight into existing political questions. Politics is often characterized by unexpected consequences, sensitivity to small changes, non-equilibrium dynamics, the emergence of patterns, and sudden changes in outcomes. These are all attributes of nonlinear processes. Bringing together a variety of recent nonlinear modeling approaches, Political Complexity explores what happens when political actors operate in a dynamic and complex social environment. The contributions to this collection are organized in terms of three branches within non-linear theory: spatial nonlinearity, temporal nonlinearity, and functional nonlinearity. The chapters advance beyond analogy towards developing rigorous nonlinear models capable of empirical verification. Contributions to this volume cover the areas of landscape theory, computational modeling, time series analysis, cross-sectional analysis, dynamic game theory, duration models, neural networks, and hidden Markov models. They address such questions as: Is international cooperation necessary for effective economic sanctions? Is it possible to predict alliance configurations in the international system? Is a bureaucratic agency harder to remove as time goes on? Is it possible to predict which international crises will result in war and which will avoid conflict? Is decentralization in a federal system always beneficial? The contributors are David Bearce, Scott Bennett, Chris Brooks, Daniel Carpenter, Melvin Hinich, Ken Kollman, Susanne Lohmann, Walter Mebane, John Miller, Robert E. Molyneaux, Scott Page, Philip Schrodt, and Langche Zeng. This book will be of interest to a broad group of political scientists, ranging from those who employ nonlinear methods to those curious to see what it is about. Scholars in other social science disciplines will find the new methodologies insightful for their own substantive work. Diana Richards is Associate Professor of Political Science, University of Minnesota.
Brock, Hsieh, and LeBaron show how the principles of chaos theory can be applied to such areas of economics and finance as the changing structure of stock returns and nonlinearity in foreign exchange.
This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.
Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.
Modelling and Forecasting Financial Data brings together a coherent and accessible set of chapters on recent research results on this topic. To make such methods readily useful in practice, the contributors to this volume have agreed to make available to readers upon request all computer programs used to implement the methods discussed in their respective chapters. Modelling and Forecasting Financial Data is a valuable resource for researchers and graduate students studying complex systems in finance, biology, and physics, as well as those applying such methods to nonlinear time series analysis and signal processing.