An Overview of the New Frontiers of Economic Complexity

  • Matthieu Cristelli
  • Andrea Tacchella
  • Luciano Pietronero
Part of the New Economic Windows book series (NEW)

Abstract

The fundamental idea developed throughout this short overview on Economic Complexity is that a revolution of the revolution of Economics is needed to turn this field into a mature discipline. The first revolution of Economic Complexity (Bouchaud in Nature 455:1181, 2008) led to a conceptual paradigm shift and agent-based models have shown, from a qualitative point of view, the crucial role played by concepts like agent heterogeneity and herding behavior to understand the non-trivial features of financial time series. The second revolution must lead the paradigm shift from a conceptual and qualitative level to a quantitative and effective description of economic systems. This can be achieved through the introduction of new metrics and quantitative methods in Social Sciences (Economics, Finance, opinion dynamics, etc.). In fact, the concept of metrics is usually neglected by mainstream theories of Economy and Finance. Only in that way Economic Complexity can concretely affect the thinking of Economic mainstream and, in this sense, become a mature discipline. The large availability of datasets (the so-called Big Data Science) has recently revealed new promising path towards such perspectives and, as an example, we briefly discuss how archival data about export flows can be turned into a concrete tool to assess the competitiveness of countries and the complexity of products.

References

  1. 1.
    Bouchaud J-P (2008) Economics needs a scientific revolution. Nature 455:1181 ADSCrossRefGoogle Scholar
  2. 2.
    Tacchella A, Cristelli M, Caldarelli G, Gabrielli A, Pietronero L (2012) A new metrics for countries’ fitness and products’ complexity. Sci Rep Nat 2:723 ADSGoogle Scholar
  3. 3.
    Bouchaud J-P, Kockelkoren J, Potters M (2004) Random walks, liquidity molasses and critical response in financial markets. Quant Finance 6:115 MathSciNetCrossRefGoogle Scholar
  4. 4.
    Lillo F, Farmer J (2004) The long memory of the efficient market. Stud Nonlinear Dyn Econom 8:1226 Google Scholar
  5. 5.
    Farmer JD, Gerig A, Lillo F, Mike S (2006) Market efficiency and the long-memory of supply and demand: is price impact variable and permanent or fixed and temporary. Quant Finance 6:107 MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Bouchaud J-P, Farmer JD, Lillo F (2008) How markets slowly digest changes in supply and demand. Elsevier/Academic Press, Amsterdam Google Scholar
  7. 7.
    Chakraborti A, Toke IM, Patriarca M, Abergel F (2011) Econophysics review, II: agent-based models. Quant Finance 11:1013–1041. doi:10.1080/14697688.2010.539249 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bouchaud J-P, Mezard M, Potters M (2002) Statistical properties of stock order books: empirical results and models. Quant Finance 2:251 CrossRefGoogle Scholar
  9. 9.
    Potters M, Bouchaud J-P (2003) More statistical properties of order books and price impact. Physica A 324:133 ADSCrossRefMATHGoogle Scholar
  10. 10.
    Wyart M, Bouchaud J-P, Kockelkoren J, Potters M, Vettorazzo M (2008) Relation between bid-ask spread, impact and volatility in order-driven markets. Quant Finance 8:41 CrossRefMATHGoogle Scholar
  11. 11.
    Lillo F, Farmer JD, Mantegna RN (2002) Single curve collapse of price impact function for the New York stock exchange. arXiv:cond-mat/0207428
  12. 12.
    Farmer JD, Gillemot L, Lillo F, Mike S, Sen A (2004) What really causes large price changes? Quant Finance 4:383 CrossRefGoogle Scholar
  13. 13.
    Lillo F, Farmer JD, Mantegna RN (2003) Master curve for price-impact function. Nature 421:129 ADSCrossRefGoogle Scholar
  14. 14.
    Weber P, Rosenow B (2006) Large stock price changes: volume and liquidity. Quant Finance 6:7 MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Cristelli M, Zaccaria A, Pietronero L (2011) Critical overview of agent-based models for economics. In: Proceedings of the school of physics E. Fermi, course CLXXVI, 2010, Varenna Google Scholar
  16. 16.
    Alfi V, Pietronero L, Zaccaria A (2009) Self-organization for the stylized facts and finite-size effects in a financial-market model. Europhys Lett 86:58003 ADSCrossRefGoogle Scholar
  17. 17.
    Alfi V, Cristelli M, Pietronero L, Zaccaria A (2009) Minimal agent based model for financial markets, I: origin and self-organization of stylized facts. Eur Phys J B 67:385 ADSCrossRefMATHGoogle Scholar
  18. 18.
    Alfi V, Cristelli M, Pietronero L, Zaccaria A (2009) Minimal agent based model for financial markets, II: statistical properties of the linear and multiplicative dynamics. Eur Phys J B 67:399 ADSCrossRefMATHGoogle Scholar
  19. 19.
    Alfi V, Cristelli M, Pietronero L, Zaccaria A (2009) Mechanisms of self-organization and finite size effects in a minimal agent based model. J Stat Mech P03016 Google Scholar
  20. 20.
    Samanidou E, Zschischang E, Stauffer D, Lux T (2007) Microscopic models of financial markets. Rep Prog Phys 70 Google Scholar
  21. 21.
    Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397:498 ADSCrossRefGoogle Scholar
  22. 22.
    Giardina I, Bouchaud J-P (2003) Bubbles, crashes and intemittency in agent based market. Eur Phys J B 31:421 MathSciNetADSCrossRefGoogle Scholar
  23. 23.
    Caldarelli G, Marsili M, Zhang Y-C (1997) A prototype model of stock exchange. Europhys Lett 40:479 ADSCrossRefGoogle Scholar
  24. 24.
    Battiston S, Puliga M, Kaushik R, Tasca P, Caldarelli G. DebtRank: too central to fail? Financial networks, the FED and systemic risk. Nature 2:541 Google Scholar
  25. 25.
    Golub T (2010) Counterpoint: data first. Nature 464:679 ADSCrossRefGoogle Scholar
  26. 26.
    Evans J, Rzhetsky A (2010) Machine science. Science 329:399 CrossRefGoogle Scholar
  27. 27.
    Lazer D et al. (2009) Life in the network: the coming age of computational social science. Science 323:5915 CrossRefGoogle Scholar
  28. 28.
    Gonzalez M, Hidalgo C, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453:479 CrossRefGoogle Scholar
  29. 29.
    Choi H, Varian H (2009) Predicting the present with Google trends. Technical report Google Scholar
  30. 30.
    Bordino I, Battiston S, Caldarelli G, Matthieu M, Ukkonen A, Weber I (2011) PLoS ONE 7(10):e47278. doi:10.1371/journal.pone.0047278 Google Scholar
  31. 31.
    Hidalgo C, Klinger B, Barabási AL, Hausmann R (2007) The product space conditions the development of nations. Science 317:482–487 ADSCrossRefGoogle Scholar
  32. 32.
    Hidalgo C, Hausmann R (2009) The building blocks of economic complexity. Proc Natl Acad Sci USA 106:10570–10575 ADSCrossRefGoogle Scholar
  33. 33.
    Caldarelli G, Cristelli M, Gabrielli A, Pietronero L, Scala A et al. (2012) A network analysis of countries’ export flows: firm grounds for the building blocks of the economy. PLoS ONE 7(10):e47278. doi:10.1371/journal.pone.0047278 ADSCrossRefGoogle Scholar
  34. 34.
    Gaulier G, Zignago S (2010) Baci: international trade database at the product-level. http://www.cepii.fr/anglaisgraph/workpap/pdf/2010/wp2010-23.pdf
  35. 35.
    Smith A (1776) The wealth of nations. Strahan and Cadell, London Google Scholar
  36. 36.
    Ricardo D (1817) On the principles of political economy and taxation. Murray, Sydney Google Scholar
  37. 37.
    Romer PM (1990) Endogenous technological change. J Polit Econ 98:71–102 CrossRefGoogle Scholar
  38. 38.
    Grossman GM, Helpman E (1991) Quality ladders in the theory of growth. Rev Econ Stud 58:43–61 CrossRefGoogle Scholar
  39. 39.
    Flam H, Flanders MJ (1991) Heckscher-Ohlin trade theory. MIT Press, Cambridge Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Matthieu Cristelli
    • 1
  • Andrea Tacchella
    • 2
  • Luciano Pietronero
    • 2
    • 3
  1. 1.ISC-CNRSapienza UniversityRomeItaly
  2. 2.Sapienza UniversityRomeItaly
  3. 3.ISC-CNRRomeItaly

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