By Norman Ehrentreich

From the reviews:

"The ebook less than assessment is a vital and cautious examine of a few of the problems keen on the workings of the SFI inventory industry. … for my part, Ehrentreich’s ebook is a wonderful 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 very good booklet. … it really is worthy primer for a person drawn to getting all started within the zone of agent-based finance. … it's crucial interpreting for someone drawn to the dynamics of the SFI marketplace specifically, yet I additionally suggest it for others as an invaluable source on agent-based monetary industry layout as well." (Blake LeBaron, magazine of synthetic Societies and Social Simulation, Vol. 12 (2), March, 2009)

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

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However, I follow Langlois in defining rationality in a situational context [231, 233]. 6 It “insists that agents act not optimally but merely reasonably under the circumstances” [231, p. 693]. ” It always requires the use of all our cognitive and computational capabilities—sometimes even beyond that—to perform simple tasks for which simple heuristics would suffice. In this respect, procedural or situational rationality, to be defined below, are more economical concepts of rationality. A survey about violations of the basic postulates of economic rationality can be found in [64].

By now, there have been many different variants of GAs developed, some of which are are discussed in [112]. Most of them descended from the so-called canonical GA developed by Holland [182] and described in [154]. 1. BEGIN randomly create initial population P0 WHILE NOT stopping condition DO t := t + 1 Evaluation of Pt−1 Selection from Pt−1 and Reproduction into Pt Recombination (Crossover) on Pt Mutation on Pt END END Fig. 1. Basic structure of the canonical GA. The canonical GA is a non-overlapping populations approach in the sense that the offspring generation created through selective reproduction, mutation, and crossover completely replaces the parent generation.

The two most used sampling algorithms are roulette wheel (RW) and stochastic universal sampling (SUS). There are numerous selection schemes. 7) ΠRW (i) = N j=1 Φ(j) N being the population size. Since fitness proportionate selection requires positive fitness values, it is sometimes necessary to add a positive 22 23 ticular, genetic drift, as a means of moving a population across valleys in the fitness surface [442]. GAs with state dependent functions are analyzed in more detail in [89]. Riechmann [356] also points out that the term “strategy” is used differently in game theory and in the theory of economic GA learning.

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