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Advancing Social Simulation: The First World Congress
Agent-based modeling and social simulation have emerged as both developments of and challenges to the social sciences. The developments include agent-based computational economics and investigations of theoretical sociological concepts using formal simulation techniques. Among the challenges are the development of qualitative modeling techniques, implementation of agent-based models to investigate phenomena for which conventional economic, social, and organizational models have no face validity, and the application of physical modeling techniques to social processes. Bringing together diverse approaches to social simulation and research agendas.
Learning in Economic Systems with Expectations Feedback
Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system.
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.
Agent-Based Models of Energy Investment Decisions
This book demonstrates how bounded rational decision models can be standardized and parameterized by socio-economic data. Focusing on private energy technology investment decisions, the author shows how different representative agents can be constructed using search rules, analysis tools and decision strategies. Diffusion curves for energy technologies such as solar collectors, boilers and efficiency upgrades for buildings are calculated. Further, the model is extended to study the impact of firms’ competition on technology diffusion. The modeling approach presented in this book may serve as a template for applications in other domain.
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.
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.
Complex decision making : Theory and practice
The increasingly complex environment of today's world, characterized by technological innovation and global communication, generates myriads of possible and actual interactions while limited physical and intellectual resources severely impinge on decision makers, be it in the public or private domains. At the core of the decision-making process is the need for quality information that allows the decision maker to better assess the impact of decisions in terms of outcomes, nonlinear feedback processes and time delays on the performance of the complex system invoked.
An Introduction to continuous-time stochastic processes : Theory, models, and applications to finance, biology, and medicine
This book is introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required. Key topics covered include: * Interacting particles and agent-based models: from polymers to ants * Population dynamics: from birth and death processes to epidemics * Financial market models: the non-arbitrage principle * Contingent claim valuation models: the risk-neutral valuation theory * Risk analysis in insurance
Agent-Based Modeling Meets Gaming Simulation
Provides a good example of the diverse scope and standard of research achieved in simulation and gaming today. The theme of the special session at ISAGA2003 was Agent-Based Modeling Meets Gaming Simulation. Nowadays, agent-based simulation is becoming very popular for modeling and solving complex social phenomena. It is also used to arrive at practical solutions to social problems. At the same time, however, the validity of simulation does not exist in the magni?cence of the model. R. Axelrod stresses the simplicity of the agent-based simulation model through the “Keep it simple, stupid” (KISS) principle: As an ideal, simple modeling is essential.
