Modeling Complexity In Economic And Social Systems
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Modeling Complexity in Economic and Social Systems
Economics and the social sciences are, in fact, the ?hard? sciences, as Herbert Simon argued, because the complexity of the problems dealt with cannot simply be reduced to analytically solvable models or decomposed into separate subprocesses. Nevertheless, in recent years, the emerging interdisciplinary ?sciences of complexity? have provided new methods and tools for tackling these problems, ranging from complex data analysis to sophisticated computer simulations. In particular, advanced methods developed in the natural sciences have recently also been applied to social and economic problems.The twenty-one chapters of this book reflect this modern development from various modeling perspectives (such as agent-based models, evolutionary game theory, reinforcement learning and neural network techniques, time series analysis, non-equilibrium macroscopic dynamics) and for a broad range of socio-economic applications (market dynamics, technological evolution, spatial dynamics and economic growth, decision processes, and agent societies). They jointly demonstrate a shift of perspective in economics and the social sciences that is allowing a new outlook in this field to emerge.
Modeling Complexity In Economic And Social Systems
Economics and the social sciences are, in fact, the “hard” sciences, as Herbert Simon argued, because the complexity of the problems dealt with cannot simply be reduced to analytically solvable models or decomposed into separate subprocesses. Nevertheless, in recent years, the emerging interdisciplinary “sciences of complexity” have provided new methods and tools for tackling these problems, ranging from complex data analysis to sophisticated computer simulations. In particular, advanced methods developed in the natural sciences have recently also been applied to social and economic problems.The twenty-one chapters of this book reflect this modern development from various modeling perspectives (such as agent-based models, evolutionary game theory, reinforcement learning and neural network techniques, time series analysis, non-equilibrium macroscopic dynamics) and for a broad range of socio-economic applications (market dynamics, technological evolution, spatial dynamics and economic growth, decision processes, and agent societies). They jointly demonstrate a shift of perspective in economics and the social sciences that is allowing a new outlook in this field to emerge.
Economics as an Agent-Based Complex System
Author: H. Deguchi
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-06-28
In agent-based modeling the focus is very much on agent-based simulation, as simulation is a very important tool for agent-based modeling. We also use agent-based simulation in this book with a stress on the mathematical foundation of agent-based modeling. We introduce two original mathematical frameworks, a theory of SLD (Social Learning Dynamics) and an axiomatic theory of economic exchange (Exchange Algebra) among agents. Exchange algebra gives bottom-up reconstruction of SNA (System of National Accountings). SLD provides the concept of indirect control of socio-economic systems to manage structural change and its stability. We also compare agent-based simulation with gaming simulation and investigate the epistemological foundation of agent-based modeling.