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A Statistical Equilibrium Representation of Markets as Complex Networks

Author

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  • Leonardo Bargigli

    (DISEI, Università degli Studi di Firenze)

  • Andrea Lionetto
  • Stefano Viaggiu

    (DISEI, Università degli Studi di Firenze)

Abstract

We represent an exchange economy in terms of statistical ensembles for complex networks by introducing the concept of market configuration. In this way, starting from economic reasoning, we obtain a sound interpretation of the typical network variables in terms of thermodynamic quantities together with a strong consistency with microeconomic theory, and in particular with Walrasian general equilibrium theory. In our formalism, naturally arises the interpretation of the temperature T as a quantification of economic disequilibrium, which can indeed coexist with statistical equilibrium.

Suggested Citation

  • Leonardo Bargigli & Andrea Lionetto & Stefano Viaggiu, 2013. "A Statistical Equilibrium Representation of Markets as Complex Networks," Working Papers - Economics wp2013_23.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    • Handle: RePEc:frz:wpaper:wp2013_23.rdf
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    References listed on IDEAS

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    1. Garibaldi,Ubaldo & Scalas,Enrico, 2010. "Finitary Probabilistic Methods in Econophysics," Cambridge Books, Cambridge University Press, number 9780521515597, January.
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    Cited by:

    1. George Judge, 2016. "Econometric Information Recovery in Behavioral Networks," Econometrics, MDPI, vol. 4(3), pages 1-11, September.
    2. Merza, Ádám & London, András & Kiss, István Márton & Pelle, Anita & Dombi, József & Németh, Tamás, 2016. "A világkereskedelem hálózatelméleti vizsgálatának lehetőségeiről [The scope for analysis of world trade through network theory]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 79-98.
    3. George Judge, 2018. "Micro-Macro Connected Stochastic Dynamic Economic Behavior Systems," Econometrics, MDPI, vol. 6(4), pages 1-14, December.
    4. Tiziano Squartini & Enrico Ser-Giacomi & Diego Garlaschelli & George Judge, 2015. "Information Recovery in Behavioral Networks," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    5. George Judge, 2015. "Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems," Econometrics, MDPI, vol. 3(1), pages 1-10, February.

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