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By Thomas Stümpert, Detlef Seese, Malte Sunderkötter (auth.), M. Beckmann, H. P. Künzi, Prof. Dr. G. Fandel, Prof. Dr. W. Trockel, A. Basile, A. Drexl, H. Dawid, K. Inderfurth, W. Kürsten, U. Schittko, Prof. Philippe Mathieu, Dr. Bruno Beaufils, Prof. Olivie

ISBN-10: 3540285474

ISBN-13: 9783540285472

ISBN-10: 3540285784

ISBN-13: 9783540285786

Agent-based Computational Economics (ACE) is a brand new self-discipline of economics, principally grounded on strategies like evolution, auto-organisation and emergence: it intensively makes use of computing device simulations in addition to synthetic intelligence, normally in accordance with multi-agents platforms. the aim of this booklet is to offer an up-to date view of the clinical construction within the fields of Agent-based Computational Economics (mainly in marketplace Finance and video game Theory). in response to communications given at AE'2005 (Lille, USTL, France), this e-book bargains a large landscape of modern advances in ACE (both theoretical and methodological) that would curiosity lecturers in addition to practitioners.

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Additional info for Artificial Economics: Agent-Based Methods in Finance, Game Theory and Their Applications

Example text

Ecological learning and social learning beat individual intelligent learning. 2 intelligent Agents in the CDA Market Although the Zero Intelligence agents may achieve high market efficiency, ZI agents perform poorly when they are competing with agents with learning capacity (Tesauro and Das [12]). It seems intuitively reasonable that one should endorse the agents in a CDA with both, intelligence and adaptation (social learning). In a series of subsequent papers non zero intelligent agents have been proposed to analyze the performance of the CDA market: ZIP agents, GD agents, etc.

We hope this unbiased way of capturing the adaptive evolution of real subjects behaviour may lead to a new kind of behavioural economics experiments with a high degree of reliability, analysability and reproducibility. 28 Gilles Daniel et al. 1 Introduction In the last decade, generic stylized facts were reproduced with very simple agents by a wide range of models [3, 12, 14, 6, 16, 8]. By the very nature of their generic properties, those models teach us little on real particular effects taking place as result of real particular conditions within the market.

Thus the task of the present generation of models is to describe and explain the observed collective market phenomena in terms of the actual behaviour of the individuals. For a long while, classical economics assumed individuals were homogeneous and behaved rationally. Thus it was not necessary to study real people behaviour since (presumably) there is only one way to be rational. Even after the conditions of rationality and homogeneity were relaxed, many models did it by postulating arbitrary departures not necessarily based on actual experiments.

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Artificial Economics: Agent-Based Methods in Finance, Game Theory and Their Applications by Thomas Stümpert, Detlef Seese, Malte Sunderkötter (auth.), M. Beckmann, H. P. Künzi, Prof. Dr. G. Fandel, Prof. Dr. W. Trockel, A. Basile, A. Drexl, H. Dawid, K. Inderfurth, W. Kürsten, U. Schittko, Prof. Philippe Mathieu, Dr. Bruno Beaufils, Prof. Olivie


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