Agent-based Computational Economics (ACE) is a brand new self-discipline of economics, mostly grounded on thoughts like evolution, auto-organisation and emergence: it intensively makes use of laptop simulations in addition to synthetic intelligence, regularly in line with multi-agents platforms. the aim of this booklet is to offer an up-to date view of the clinical creation within the fields of Agent-based Computational Economics (mainly in marketplace Finance and video game Theory). in line with 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 may curiosity lecturers in addition to practitioners.
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Extra resources for Artificial Economics: Agent-Based Methods in Finance, Game Theory and Their Applications (Lecture Notes in Economics and Mathematical Systems, Volume 564)
Table 4 reports prices deviations during this critical event. Here the standard deviation is greater than the one observed on the complete sample. A bubble is hence characterized by a great deviation between the stock price and its fundamental value during a long time range. This typical dynamic, obtained with 75% of speculators and 25% of fundamentalists, can be found with other sets of parameters as long as speculator agents proportion is great (> 70%). In the speculative regime (when speculators compose the main part of population), we obtain a highly volatile price dynamic with bubbles and crashes.
K agents must be parasitic on the intelligent agents to trade and to obtain profit. If all traders in the market are K agents no trade will take place. Although learning and the convergence to the Nash Equilibrium have been widely studied (Kirman ), there are few applications to the analysis of learning strategies in a CDA market (Walsh et al ). Walsh et al  found two Nash equilibria points when these three types agents (GD, K and ZIP) rival each other in a CDA market and their agents strategies are fixed by the modeller.
3. Market dynamics with fundamentalist agents The first step to test if those motions are somewhat consistent with what happens in the real stock markets consist in testing whether they are driven by non-stationary processes or not. g. ADF). Both fundamental values and prices have to be random walks if we want to qualify the simulations realistic since the immense part of academic researchs attest such motions for modem, real stock market dynamics. In the following tests, the null hypothesis is time seriepresents one unit root (HQ) while the alternative is time serie has no unit root (Hi).
Artificial Economics: Agent-Based Methods in Finance, Game Theory and Their Applications (Lecture Notes in Economics and Mathematical Systems, Volume 564)