Building Artificial Economies: From Aggregate Data to Experimental Microstructure. A Methodological Survey

Gianfranco Giulioni (), Paola D’Orazio (), Edgardo Bucciarelli () and Marcello Silvestri ()
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Gianfranco Giulioni: Pedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” University
Paola D’Orazio: Pedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” University
Edgardo Bucciarelli: Pedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” University
Marcello Silvestri: Pedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” University
Authors registered in the RePEc Author Service: Paola D'Orazio

A chapter in Advances in Artificial Economics, 2015, pp 69-78 from Springer

Abstract: Abstract This paper suggests a methodological appraisal of the main improvements witnessed by the methodology based on the interplay between Experimental Economics (EE) and Agent-based Computational Economics (ACE) in the last 5–6 years. EE and ACE proved to be “natural allies” in that they complement each other: EE helps ACE in dealing with its “degree of freedom” problem and ACE helps EE in controlling and providing benchmarks for experimental subjects’ behavior. The paper discusses the role Evolutionary Computation plays in this bidirectional relationship.

Keywords: Genetic Algorithm; Artificial Agent; Experimental Economic; Macroeconomic Model; Heterogeneous Agent (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-319-09578-3_6

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DOI: 10.1007/978-3-319-09578-3_6

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