Agent-Based Modeling: The Santa Fe Institute Artificial - download pdf or read online

By Norman Ehrentreich

ISBN-10: 3540738789

ISBN-13: 9783540738787

This booklet reconciles the lifestyles of technical buying and selling with the effective marketplace speculation. through examining a well known agent-based version, the Santa Fe Institute man made inventory industry (SFI-ASM), it unearths that after selective forces are susceptible, monetary evolution can't ensure that in simple terms the fittest buying and selling ideas will live to tell the tale. Its major contribution lies within the program of ordinary effects from inhabitants genetics that have largely been ignored within the agent-based group.

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Additional resources for Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited (Lecture Notes in Economics and Mathematical Systems)

Example text

3) and is also closely related to the concept of “self-organized criticality” [20]. 5 Some Methodological Aspects of Agent-Based Simulations The ability to study high-dimensional complex adaptive systems in economics through agent-based simulations, however, comes at a cost. Agent-based simulations not only require inductive methods on the level of the agents, but also require inductive reasoning on the level of the modeler. Unlike most analytical models, an agent-based simulation does not produce theorems and existence proofs.

13]. Celebrating the fiftieth anniversary of Milton Friedman’s ‘The methodology of positive economics’, the 2003 annual meeting of the Allied Social Science Associations devoted a complete symposium to it. , [353, 273], are contained in a special issue of the Journal of Economic Methodology. 22 3 The Concept of Minimal Rationality axioms require a complete, continuous, and transitive preference ordering which should be invariant to framing and independent of irrelevant alternatives. Finally, process rationality in economics implies the maximization of an objective function subject to some constraints.

In period 0, agents typically assign to each hypothesis h an equal probability p(h, 0). If better information is available, these so-called prior probabilities might differ from each other. , h∈H p(h, t) = 1 must hold for all periods t. As agents learn about new evidence in the next period, they update their initial subjective probabilities associated with each hypothesis according to p(h, t + 1) = P (e(t)|h) · p(h, t) . 1) Through this equation, an observed event increases the probability of that hypothesis for which the occurrence of this event is most likely.

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Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited (Lecture Notes in Economics and Mathematical Systems) by Norman Ehrentreich


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