Suchergebnisse

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- A Note on the Efficient Reading of This Book -- Acknowledgments -- Part I Economic Theory as Physics -- Introduction -- 1 Physics and Economics-A Theory Comparo -- 2 Physics and Economics-A Performance Comparo -- 3 Immortal Constants -- 4 Mitigations? -- Part I-The Gist -- Part II Branches of Economic Theory-Differential Disappointments -- 5 General Economic Equilibrium Is Neither -- 6 Macroeconomics: Theorem-Seeking, Forecasting Failure -- 7 Finance: It Works-but How?

This paper considers the actual problem of correlation of economics with other sciences, and namely with social sciences. The evolution of processes to solve the provisioning problem takes place in a social context. As a result, the economy is a subsystem and is interrelated with a variety of other social subsystems. These subsystems include economic, political, religious, social, geographic, demographic, legal, and moral systems. From the ancient times economics was treated as part of philosophy and religion.

This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.

Over the last two decades there has been a great deal of research into nonlinear dynamic models in economics, finance and the social sciences. This book contains twenty papers that range over very recent applications in these areas. Topics covered include structural change and economic growth, disequilibrium dynamics and economic policy as well as models with boundedly rational agents. The book illustrates some of the most recent research tools in this area and will be of interest to economists working in economic dynamics and to mathematicians interested in seeing ideas from nonlinear dynamics and complexity theory applied to the economic sciences.

The paper discusses measurement, primarily in economics, from both analytical and historical perspectives. The historical section traces the commitment to ordinalism on the part of economic theorists from the doctrinal disputes between classical economics and marginalism, through the struggle of orthodox economics against socialism down to the cold-war alliance between mathematical social science and anti-communist ideology. In economics the commitment to ordinalism led to the separation of theory from the quantitative measures that are computed in practice: price and quantity indexes, consumer surplus and real national product. The commitment to ordinality entered political science, via Arrow's 'impossibility theorem', effectively merging it with economics, and ensuring its sterility. How can a field that has as its central result the impossibility of democracy contribute to the design of democratic institutions? The analytical part of the paper deals with the quantitative measures mentioned above. I begin with the conceptual clarification that what these measures try to achieve is a restoration of the money metric that is lost when prices are variable. I conclude that there is only one measure that can be embedded in a satisfactory economic theory, free from unreasonable restrictions. It is the Törnqvist index as an approximation to its theoretical counterpart the Divisia index. The statistical agencies have at various times produced different measures for real national product and its components, as well as related concepts. I argue that all of these are flawed and that a single deflator should be used for the aggregate and the components. Ideally this should be a chained Törnqvist price index defined on aggregate consumption. The social sciences are split. The economic approach is abstract, focused on the assumption of rational and informed behavior, and tends to the political right. The sociological approach is empirical, stresses the non-rational aspects of human behavior and tends to the political left. I argue that the split is due to the fact that the empirical and theoretical traditions were never joined in the social sciences as they were in the natural sciences. I also argue that measurement can potentially help in healing this split.

Over the last two decades there has been a great deal of research into nonlinear dynamic models in economics, finance and the social sciences. This book contains twenty papers that range over very recent applications in these areas. Topics covered include structural change and economic growth, disequilibrium dynamics and economic policy as well as models with boundedly rational agents. The book illustrates some of the most recent research tools in this area and will be of interest to economists working in economic dynamics and to mathematicians interested in seeing ideas from nonlinear dynamics and complexity theory applied to the economic sciences.

Economics is far more versatile than its critics believe. It is a method of analysis and not just a field of study. The method is to combine two core assumptions-individual optimization and equilibrium-with various sets of specific assumptions adapted to different fields of application. The method is applicable not only to the market system but also the social and political environment within which this system is embedded. Social and psychological insights can be encapsulated in the specification of interdependent preferences, which hold the key to modelling all kinds of institutional behavior in rational terms.

Nietzsche distinguished between two forces in art: Apollonian, which represents order and reason, and Dionysian, which represents chaos and energy. An ideal work of art combines these two characteristics in a believable, relatable balance. Economists, Ward argues, have operated for too long under the assumption that their work reflects scientific, Apollonian principals when these simply do not or cannot apply: "constants" in economics stand in for variables, mathematical equations represent the simplified ideal rather than the complex reality, and the core scientific principal of replication is all but ignored. In Dionysian Economics, Ward encourages economists to reintegrate the standard rigor of the scientific method into their work while embracing the fact that their prime indicators come from notoriously chaotic and changeable human beings. Rather than emphasizing its shortfalls compared to an extremely Apollonian science, such as physics, economics can aspire to the standards of a science that accounts for considerable Dionysian variation, such as biology. The book proposes that economists get closer to their dynamic objects of study, that they avoid the temptation to wish away dynamic complexity by using simplifying assumptions, and that they recognize the desire to take risks as fundamentally human.