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Liquidity, markets and trading in action : An interdisciplinary perspective
This book addresses four standard business school subjects: microeconomics, macroeconomics, finance and information systems as they relate to trading, liquidity, and market structure. It provides a detailed examination of the impact of trading costs and other impediments of trading that the authors call “frictions”. It also presents an interactive simulation model of equity market trading, TraderEx, that enables students to implement trading decisions in different market scenarios and structures. Addressing these topics shines a bright light on how a real-world financial market operates, and the simulation provides students with an experiential learning opportunity that is informative and fun.
Complexity hints for economic policy
This volume extends the complexity approach to economics. It provides some alternative pattern generators, which can supplement existing approaches by providing an alternative way of finding patterns than be obtained by the traditional scientific approach.
Complexity and Artificial Markets
In recent years, agent-based simulation has become a widely accepted tool when dealing with complexity in economics and other social sciences. The contributions presented in this book apply agent-based methods to derive results from complex models related to market mechanisms, evolution, decision making, and information economics. In addition, the applicability of agent-based methods to complex problems in economics is discussed from a methodological perspective. The papers presented in this collection combine approaches from economics, finance, computer science, natural sciences, philosophy, and cognitive sciences.
CGE models and capital income tax reforms : The case of a dual income tax for Germany
The book suggests a novel way how the effects of tax reforms especially in the field of capital income taxation can be measured by means of dynamic computable general equilibrium (CGE) models.
Artificial economics : Agent-based methods in finance, game theory and their applications
The purpose of this book is to give an up-to date view of the scientific production in the fields of Agent-based Computational Economics (mainly in Market Finance and Game Theory). Based on communications given at AE'2005 (Lille, USTL, France), this book offers a wide panorama of recent advances in ACE (both theoretical and methodological) that will interest academics as well as practitioners.
Applied Research in Uncertainty Modeling and Analysis
For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on theory, the authors use real-world examples to demonstrate the strength of the chosen methodology. Applied Research in Uncertainty Modeling and Analysis concentrates on general aspects of uncertainty, modeling, and methods, and focuses on various applications, included Biomedical Engineering, Chemical Engineering, Structural Engineering, and Transportation Engineering.
Applied Econometrics with R
This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research.
Applications of simulation methods in environmental and resource economics
Simulation methods are revolutionizing the practice of applied economic analysis. This volume collects eighteen chapters written by leading researchers from prestigious research institutions the world over. The common denominator of the papers is their relevance for applied research in environmental and resource economics. The topics range from discrete choice modeling with heterogeneity of preferences, to Bayesian estimation, to Monte Carlo experiments, to structural estimation of Kuhn-Tucker demand systems, to evaluation of simulation noise in maximum simulated likelihood estimates, to dynamic natural resource modeling. Empirical cases are used to show the practical use and the results brought forth by the different methods.
An Introduction to Efficiency and Productivity Analysis
It is designed to be a "first port of call" for people wishing to study efficiency and productivity analysis. The book provides an accessible introduction to the four principal methods involved: econometric estimation of average response models; index numbers; data envelopment analysis (DEA); and stochastic firontier analysis (SFA). For each method, we provide a detailed introduction to the basic concepts, give some simple numerical examples, discuss some of the more important extensions to the basic methods, and provide references for further reading. In addition, we provide a number of detailed empirical applications using real-world data.
Agent-based modeling : The Santa Fe Institute artificial stock market model revisited
An excellent reference to both the learning, and empirical literature in finance." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 1141, 2008) "Norman Ehrentreich was one of the daring few to take on the model, and he has summarized his work and findings in this excellent book. … It is useful primer for anyone interested in getting started in the area of agent-based finance. … It is essential reading for anyone interested in the dynamics of the SFI market in particular, but I also recommend it for others as a useful resource on agent-based financial market design as well." (Blake LeBaron, Journal of Artificial Societies and Social Simulation, Vol. 12 (2), March, 2009)
Agent-based computational modelling : Applications in demography, social, economic and environmental sciences
The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.
Advances in mathematical economics ; Vol. 8
The series is designed to bring together those mathematicians who were seriously interested in getting new challenging stimuli from economic theories with those economists who are seeking for effective mathematical tools for their researchers.
Adaptive Information Systems and Modelling in Economics and Management Science
Learning and adaption are key features of "real economies". Studying interesting real phenomena like innovation, industry evolution or the role of expectation formulation in financial markets thus necessitates novel methods of data analysis and modelling. This title covers statistical models of heterogeneity, artificial consumer markets, models of adaptive expectation formulation in financial markets and agent-based models of industry evolution, product diversification and energy markets. The joint findings are presented in a manner that is interesting both for readers with a background in economics/management and mathematics and statistics and also for non-expert readers because it allows them to grasp the ideas of modern management science. This book thus provides a unique integrated toolbox for building realistic agent-based models of learning and adaption in a variety of settings based on sound data analysis.
Landscape Performance Modeling Using Rhino and Grasshopper
A guidebook for landscape architects to learn the fundamental practices and use of the computational software Rhino 3D and the plugin Grasshopper for parametric modeling, landscape inventory, and performative analysis. This process visually connects intangible and abstract information with physical and spatial relationships to signify the impact ecological, climate, and cultural factors have on landscape performance and decision making.
Architectural scale models in the digital age : Design, representation and manufacturing
Complex geometric forms generated using virtual media can be tested and validated only by means of physical models, which also make it possible to assess their practical application. The complexity of contemporary architectural design requires the mastery of new methods of producing scale models, which opens a new chapter in the field of modeling, and is the focus of this book. Along with the traditional methods that provide the basis for modeling, this book presents the principles of digital NURBS modeling, parametric modeling, digital modeling support, and model creation, complete with a number of tutorials, practical advice and examples found in architectural practice today.
Alvar Aalto and the future of architecture
Exposes dialogue between history, theory, design, construction, technology, and sensory experience by means of digital simulations that enhance the assessment and values of our material choices. It offers a critical look to the past to inspire the future. This new edition looks to Alvar Aalto as the primary protagonist for channeling discussions related to these topics. Architects like ALA, Shigeru Ban, 3XN, Peter Zumthor, and others also play the role of contemporary guides in this review. The work of Aalto and selected contemporary architects, along with computer modeling software, showcase the importance of comprehensive design.
Mathematical Modeling of Complex Biological Systems : A Kinetic Theory Approach
This book describes the evolution of several socio-biological systems using mathematical kinetic theory. Specifically, it deals with modeling and simulations of biological systems—comprised of large populations of interacting cells—whose dynamics follow the rules of mechanics as well as rules governed by their own ability to organize movement and biological functions. The authors propose a new biological model for the analysis of competition between cells of an aggressive host and cells of a corresponding immune system.Because the microscopic description of a biological system is far more complex than that of a physical system of inert matter, a higher level of analysis is needed to deal with such complexity. Mathematical models using kinetic theory may represent a way to deal with such complexity, allowing for an understanding of phenomena of nonequilibrium statistical mechanics not described by the traditional macroscopic approach. The proposed models are related to the generalized Boltzmann equation and describe the population dynamics of several interacting elements (kinetic population models).The particular models proposed by the authors are based on a framework related to a system of integro-differential equations, defining the evolution of the distribution function over the microscopic state of each element in a given system. Macroscopic information on the behavior of the system is obtained from suitable moments of the distribution function over the microscopic states of the elements involved. The book follows a classical research approach applied to modeling real systems, linking the observation of biological phenomena, collection of experimental data, modeling, and computational simulations to validate the proposed models. Qualitative analysis techniques are used to identify the prediction ability of specific models.
Mathematical methods and modelling in hydrocarbon exploration and production
Hydrocarbon exploration and production incorporate great technology challenges for the oil and gas industry. In order to meet the world's future demand for oil and gas, further technological advance is needed, which in turn requires research across multiple disciplines, including mathematics, geophysics, geology, petroleum engineering, signal processing, and computer science. This book addresses important aspects and fundamental concepts in hydrocarbon exploration and production. Moreover, new developments and recent advances in the relevant research areas are discussed, whereby special emphasis is placed on mathematical methods and modelling. The book reflects the multi-disciplinary character of the hydrocarbon production workflow, ranging from seismic data imaging, seismic analysis and interpretation and geological model building, to numerical reservoir simulation. Various challenges concerning the production workflow are discussed in detail.
Math Everywhere : Deterministic and Stochastic Modelling in Biomedicine, Economics and Industry
These proceedings are reporting on the conference ''Math Everywhere", a successful event celebrating a leading scientist, promoting ideas he pursued and sharing the open atmosphere he is known for. The areas of the contributions are the following , Deterministic and Stochastic Systems. Mathematical Problems in Biology, Medicine and Ecology, Mathematical Problems in Industry and Economics.
Materials Issues for Generation IV Systems ; Status, Open Questions and Challenges
Global warming, shortage of low-cost oil resources and the increasing demand for energy are currently controlling the world's economic expansion while often opposing desires for sustainable and peaceful development. In this context, atomic energy satisfactorily fulfills the criteria of low carbon gas production and high overall yield. However, in the absence of industrial fast-breeders the use of nuclear fuel is not optimal, and the production of high activity waste materials is at a maximum. These are the principal reasons for the development of a new, fourth generation of nuclear reactors, minimizing the undesirable side-effects of current nuclear energy production technology while increasing yields by increasing operation temperatures and opening the way for the industrial production of hydrogen through the decomposition of water.
