This book explores the dynamic processes in economic systems, concentrating on the extraction and use of the natural resources required to meet economic needs. Sections cover methods for dynamic modeling in economics, microeconomic models of firms, modeling optimal use of both nonrenewable and renewable resources, and chaos in economic models. This book does not require a substantial background in mathematics or computer science.
This book explores the dynamic processes in economic systems, concentrating on the extraction and use of the natural resources required to meet economic needs. Sections cover methods for dynamic modeling in economics, microeconomic models of firms, modeling optimal use of both nonrenewable and renewable resources, and chaos in economic models. This book does not require a substantial background in mathematics or computer science.
This title brings together frontier research on complex economic systems, heterogeneous interacting agents, bounded rationality, and nonlinear dynamics in economics. The book contains the proceedings of the CEF2015 (21st Computing in Economics in Finance), held 20-22 June 2015 in Taipei, Taiwan, and addresses some of the important driving forces for various emergent properties in economies, when viewed as complex systems. The breakthroughs reported in this book are a result of an interdisciplinary approach and simulation remains the unifying theme for these papers as they deal with a wide range of topics in economics. The text is a valuable addition to the efforts in promoting the complex systems view in economic science. The computational experiments reported in the book are both transparent and replicable. Complex System Modeling and Simulation in Economics and Finance is useful for graduate courses of complex systems, with particular focus on economics and finance. At the same time it serves as a good overview for researchers who are interested in the topic.
This book will enable students to use a computer as a tool to analyse, model and solve economic and business problems. The book will: develop an understanding of the methods and problems involved in the design and construction of computer-based models of economic systems; explain the features and use of a spreadsheet package (focusing on Quattro-Pro for Windows); and critically examine the design, construction and use of spreadsheet-based models in several areas of economics and business. The book integrates material by concentrating on practical issues of model-building and problem-solving, and illustrates the use of spreadsheet software throughout. Case studies and examples can be worked through on-screen once the underlying theory has been assimilated.
Models in Microeconomic Theory covers basic models in current microeconomic theory. Part I (Chapters 1-7) presents models of an economic agent, discussing abstract models of preferences, choice, and decision making under uncertainty, before turning to models of the consumer, the producer, and monopoly. Part II (Chapters 8-14) introduces the concept of equilibrium, beginning, unconventionally, with the models of the jungle and an economy with indivisible goods, and continuing with models of an exchange economy, equilibrium with rational expectations, and an economy with asymmetric information. Part III (Chapters 15-16) provides an introduction to game theory, covering strategic and extensive games and the concepts of Nash equilibrium and subgame perfect equilibrium. Part IV (Chapters 17-20) gives a taste of the topics of mechanism design, matching, the axiomatic analysis of economic systems, and social choice. The book focuses on the concepts of model and equilibrium. It states models and results precisely, and provides proofs for all results. It uses only elementary mathematics (with almost no calculus), although many of the proofs involve sustained logical arguments. It includes about 150 exercises. With its formal but accessible style, this textbook is designed for undergraduate students of microeconomics at intermediate and advanced levels.
This book at once introduces the entire area of adaptive modeling and precisely defines a new decision-making procedure for developing and using models of large, complex socioeconomic systems.
The approach to many problems in economic analysis has changed drastically with the development and dissemination of new and more efficient computational techniques. Computational Economic Systems: Models, Methods & Econometrics presents a selection of papers illustrating the use of new computational methods and computing techniques to solve economic problems. Part I of the volume consists of papers which focus on modelling economic systems, presenting computational methods to investigate the evolution of behavior of economic agents, techniques to solve complex inventory models on a parallel computer and an original approach for the construction and solution of multicriteria models involving logical conditions. Contributions to Part II concern new computational approaches to economic problems. We find an application of wavelets to outlier detection. New estimation algorithms are presented, one concerning seemingly related regression models, a second one on nonlinear rational expectation models and a third one dealing with switching GARCH estimation. Three contributions contain original approaches for the solution of nonlinear rational expectation models.
Model Building is the most fruitful area of economics, designed to solve real-world problems using all available methods such as mathematical, computational and analytical, without distinction. Wherever necessary, we should not be reluctant to develop new techniques, whether mathematical or computational. That is the philosophy of this volume. The volume is divided into three distinct parts: Methods, Theory and Applications. The Methods section is in turn subdivided into Mathematical Programming and Econometrics and Adaptive Control System, which are widely used in econometric analysis. The impacts of fiscal policy in a regime with independent monetary authority and dynamic models of environmental taxation are considered. In the section on "Modelling Business Organization," a model of a Japanese organization is presented. Furthermore, a model suitable for an efficient budget management of a health service unit by applying goal programming method is analyzed, taking into account various socio-economic factors. This is followed by a section on "Modelling National Economies," in which macroeconometric models for the EU member countries are analyzed, to find instruments that stabilize inflation with coordinated action.
Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.
