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
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Klaus Schredelseker • Florian Hauser
Complexity and Artificial Markets
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Dr. Florian Hauser University of Innsbruck Institute for Banking and Finance Universitätsstr. 15 6020 Innsbruck Austria florian.hauser@ uibk.ac.at
Dr. Klaus Schredelseker University of Innsbruck Institute for Banking and Finance Universitätsstr. 15 6020 Innsbruck Austria [email protected]
ISBN 978-3-540-70553-6
e-ISBN 978-3-540-70556-7
DOI 10.1007/978-3-540-70556-7 Lecture Notes in Economics and Mathematical Systems ISSN 0075-8442 Library of Congress Control Number: 2008930214 © 2008 Springer-Verlag Berlin Heidelberg A X. Copyright © 2008 Schredelseker and Hauser. All rights reserved. Typeset with LT E The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Production: le-tex Jelonek, Schmidt & Vöckler GbR, Leipzig Cover design: WMX Design GmbH, Heidelberg Printed on acid-free paper 987654321 springer.com
Preface
In 2000, when Levy, Levy, and Solomon published their book Microscopic Simulation of Financial Markets, Harry Markowitz noted in the blurb that numerical simulations point “us towards the future of financial economics. If we restrict ourselves to models which can be solved analytically, we will be modeling for our mutual entertainment, not to maximize explanatory or predictive power.” At that time most economists were quite sceptical about the new techniques and thus a statement like this was encouraging for the Artificial Economics community. Since 2000, things have changed tremendously. Agent-based modeling, computer simulations, and artificial economics have become broadly accepted tools in social sciences by now. For a large number of problems they are the only reliable techniques to arrive at nontrivial results. Neoclassical economics is usually split up into a micro and a macro analysis, the first dealing with the individual decision-maker (consumer, firm, investor etc.), and the second with economic aggregates such as aggregate demand and aggregate supply (labor, consumption, capital, etc.). The link, if there is any, between both levels is the representative agent, that is the assumption that either all agents are of the same type or that they act in such a way that the sum of their choices is mathematically equivalent to the decisions of identical, prototypical individuals. In such a world neither the problem of imperfect rationality nor the problem of disparate and diverse information can be addressed; the latter is not even the case if you allow for only two disparate levels of information, let us say informed and non-informed individuals. What happens in the real world is an outcome of the interaction of numerous individuals, each of whom may have different preferences, different information levels and different attitudes. A system with a set of autonomous decision-makers (agents) who individua
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