Artificial Economics: Agent-Based Methods in Finance, Game by Philippe Mathieu, Bruno Beaufils, Olivier Brandouy

By Philippe Mathieu, Bruno Beaufils, Olivier Brandouy

Agent-based Computational Economics (ACE) is a brand new self-discipline of economics, principally grounded on innovations like evolution, auto-organisation and emergence: it intensively makes use of computing device simulations in addition to man made intelligence, as a rule in line with multi-agents platforms. the aim of this ebook is to offer an up-to date view of the medical construction within the fields of Agent-based Computational Economics (mainly in marketplace Finance and online game Theory). in accordance with communications given at AE'2005 (Lille, USTL, France), this publication deals a large landscape of modern advances in ACE (both theoretical and methodological) that would curiosity teachers in addition to practitioners.

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Additional info for Artificial Economics: Agent-Based Methods in Finance, Game Theory and Their Applications (Lecture Notes in Economics and Mathematical Systems)

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Economic Letters, A5{\)'A()2>-\\\, May 1994. 14. A. OrlQan. Le pouvoir de la finance. 1999. 15. G. B. H. Holland, B. LeBaron, and P. Tayler. Artificial economic life : A simple model of a stockmarket. Physica D, 15:264-21A, 1994. 16. A. Samuelson. Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, (6):41-49, 1965. il Simulations of artificial stock markets were considered as early as 1964 [20] and multi-agent ones were introduced as early as 1989 [10]. Starting the early 90's [18, 13, 21], collaborations of economists and physicists produced increasingly realistic simulation platforms.

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).

1 Market Representation Each agent has a stock of m rules which describe market states and tell him which decision to take. A rule is composed of three subparts: the first one describes a specific market state (called condition part of the rule). The second part describes the decision to take withing this specific context: bid a share, offer a share or do nothing (called action part. The last part represents the current evaluation of the rule's adequateness in market activity (called^brc^). One have to keep in mind that each agent possess his own stock of rule that is, it is hardly possible that two agents are exactly similar.

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