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

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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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Notes

  1. 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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  1. Department of Economics and Finance, University of Rome Tor Vergata, Rome, Italy

    Annalisa Fabretti

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  1. Annalisa Fabretti
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Correspondence to Annalisa Fabretti.

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Fabretti, A. Markov chain analysis in agent-based model calibration by classical and simulated minimum distance. Knowl Inf Syst 61, 259–276 (2019). https://doi.org/10.1007/s10115-018-1258-y

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