Titlebook: Artificial Economics; Agent-Based Methods M. Beckmann,H. P. Künzi,Olivier Brandouy Conference proceedings 2006 Springer-Verlag Berlin Heid

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Complex Dynamics, Financial Fragility and Stylized Facts

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Stock Price Dynamics in Artificial Multi-Agent Stock Markets

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Time Series Properties from an Artificial Stock Market with a Walrasian Auctioneerthese shortcomings, i.e. the auctioneer builds a causality between wealth of each agent and the arising price function of the risky asset, and the auctioneer iterates toward the equilibrium. The Santa Fe artificial stock market has been criticized because the mutation operator for producing new trad

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Learning in Continuous Double Auction Market switching between the alternative strategies. We examine the emergence or not of Nash equilibriums, with a bottom-up approach..Our results confirm that although market efficiency is an ecological property, an it is robust against intelligence agents, convergence and volatility depend on the learnin

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Pablo Branas-Garza,Iván Barredathese shortcomings, i.e. the auctioneer builds a causality between wealth of each agent and the arising price function of the risky asset, and the auctioneer iterates toward the equilibrium. The Santa Fe artificial stock market has been criticized because the mutation operator for producing new trad

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Time Series Properties from an Artificial Stock Market with a Walrasian Auctioneersed on the Santa Fe artificial stock market (SF-ASM, see e.g. [1], [2],[3],[4],[5]). The model is purposely simple in order to show that a parsimonious nonlinear framework with an equilibrium model can replicate typical stock market phenomena including phases of speculative bubbles and market crashe

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