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Knowledge and Information Systems

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03.09.2018 | Regular Paper

Markov chain analysis in agent-based model calibration by classical and simulated minimum distance

verfasst von: Annalisa Fabretti

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2019

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Abstract

Agent-based models are nowadays widely used; however, their calibration on real data still remains an open issue which prevents to exploit completely their potentiality. Rarely such a kind of models can be studied analytically; more often they are studied by simulation. Among the problems encountered in ABM calibration, the choice of the criteria to fit can appear arbitrary. Markov chain analysis can come through to identify a standard procedure able to face this issue. Indeed, Izquierdo et al. (J Artif Soc Soc Simul 12(16):1–6, 2009) show that many computer simulation models can be represented as Markov chains. Exploiting such an idea classical minimum distance and its simulated counterpart, i.e., simulated minimum distance, are discussed theoretically and applied to Kirman model, which can be reformulated as a Markov chain. Comparison with approximate Bayesian computation is also addressed.

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The author has studied deeply this point performing analytically and statistical analysis that are not reported here, but that can be provided on request.

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Titel: Markov chain analysis in agent-based model calibration by classical and simulated minimum distance
verfasst von: Annalisa Fabretti
Publikationsdatum: 03.09.2018
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 1/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-018-1258-y

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