By Norman Ehrentreich

ISBN-10: 3540738789

ISBN-13: 9783540738787

ISBN-10: 3540738797

ISBN-13: 9783540738794

From the reviews:

"The ebook below evaluate is a crucial and cautious examine of a few of the problems keen on the workings of the SFI inventory marketplace. … for my part, Ehrentreich’s publication is a superb connection with either the educational, and empirical literature in finance." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 1141, 2008)

"Norman Ehrentreich used to be one of many bold few to tackle the version, and he has summarized his paintings and findings during this first-class e-book. … it truly is beneficial primer for someone attracted to getting began within the quarter of agent-based finance. … it really is crucial examining for a person attracted to the dynamics of the SFI industry specifically, yet I additionally suggest it for others as an invaluable source on agent-based monetary marketplace layout as well." (Blake LeBaron, magazine of man-made Societies and Social Simulation, Vol. 12 (2), March, 2009)

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Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited

From the reviews:"The e-book less than overview is a crucial and cautious learn of a few of the problems fascinated about the workings of the SFI inventory marketplace. … for my part, Ehrentreich’s ebook is a wonderful connection with either the educational, and empirical literature in finance. " (Krzysztof Piasecki, Zentralblatt MATH, Vol.

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Additional info for Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited

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However, since computer simulations can only evolve in discrete time steps, the discrete versions of any formula will usually be given. In a more detailed analysis, Blume and Easley show on the one hand that market selection favors profit maximizing firms. The long-run behavior of evolutionary market models, however, is not consistent with equilibrium models based on the profit-maximization hypothesis [43]. This equation is named after two evolutionary biologists, Ronald A. Fisher from Great Britain, and Manfred Eigen from Germany.

3 Rationality-Based Learning Models 31 in different economic fields and, therefore, might be classified accordingly. While Bayesian and least-square learning dominate in macroeconomics, game theorists seem to prefer fictitious play, replicator dynamics, and other adaptive learning models. Experimental economists tend to employ reinforcement learning, fictitious play, and indexlearning direction theory learning direction theory, while evolutionary algorithms and genetic programming are prominent in agent-based computational models.

In the canonical GA, the offspring replace their parents, and simple one point crossover is used. parent 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 parent 2 0 1 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 offspring 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 offspring 2 crossover point crossover point Fig. 2. Example of one-point crossover with a random break point after the fifth bit. In one point crossover, a break point between 1 and L−1 is randomly chosen. Contiguous bit segments that begin or end at the break point are exchanged between the two parents.

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Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited by Norman Ehrentreich


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