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Big Data: Opportunity and Mith. Luciano Pietronero, Institute of Complex Systems

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Data Driven Economy - May 18th 2018. Data Driven Innovation 2018. Dipartimento di ingegneria, Università degli Studi Roma Tre

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Big Data: Opportunity and Mith. Luciano Pietronero, Institute of Complex Systems

  1. 1. ECONOMIC FITNESSAND COMPLEXITY Collaborators: G. Chiarotti1,2, G. Cimini1,2, M. Cristelli1,2,5, R. Di Clemente1,2, A. Gabrielli1,2,3,D. Mazzilli,1, A. Napoletano1,2,, L. Napolitano1,2,, E. Pugliese1,2,5, F. Saracco1,2, F. Sylos Labini2,4 , T. Squartini1,2, A.Tacchella1,2,5, A. Zaccaria1,2,5 , M. Cader5, T. Chu5, S. Mishra5, K. Rosner,5, A. Volkov,5, Y. Yao5 [1] ”Sapienza” University of Rome, Italy; [2] Institute for Complex Systems, CNR, Rome, Italy; [3] London Institute for Mathematical Sciences, UK; [4] Centro Fermi, Rome, [5] IFC World Bank Group, Washington Data Driven Innovation (Economy) Univ. Roma 3, May 18, 2018 Luciano Pietronero1,2,3,5 New WEBpage: http://www.economic-fitness.com
  2. 2. SHANGHAI 1990 - 2010 Economic Growth defines the quality of life of people BUT “growth is devilinglishly hard to predict” The Economist, Jan 9th 2016
  3. 3. THE FANTASTIC GROWTH OF CHINA 1980-2017 AND ???
  4. 4. https://www.quora.com/profile/Godfree-Roberts ECONOMIC PREDICTIONS ABOUT CHINA: 1990. The Economist. China's economy has come to a halt. 1996. The Economist. China's economy will face a hard landing 1998. The Economist: China's economy entering a dangerous period of sluggish growth. 1999. Bank of Canada: Likelihood of a hard landing for the Chinese economy. 2000. Chicago Tribune: China currency move nails hard landing risk coffin. 2001. Wilbanks, Smith & Thomas: A hard landing in China. 2002. Westchester University: China Anxiously Seeks a Soft Economic Landing 2003. New York Times: Banking crisis imperils China 2004. The Economist: The great fall of China? 2005. Nouriel Roubini: The Risk of a Hard Landing in China 2006. International Economy: Can China Achieve a Soft Landing? 2007. TIME: Is China's Economy Overheating? Can China avoid a hard landing? 2008. Forbes: Hard Landing In China? 2009. Fortune: China's hard landing. China must find a way to recover. 2010: Nouriel Roubini: Hard landing coming in China. 2011: Business Insider: A Chinese Hard Landing May Be Closer Than You Think 2012: American Interest: Dismal Economic News from China: A Hard Landing 2013: Zero Hedge: A Hard Landing In China 2014. CNBC: A hard landing in China. 2015. Forbes: Congratulations, You Got Yourself A Chinese Hard Landing. 2016. The Economist: Hard landing looms for China 2017. National Interest: Is China's Economy Going To Crash?
  5. 5. BBC NEWS 17 July 2017 From the section Business China's second quarter growth beats expectations at 6.9% NB: Latest update October 2017: 6.8% Problems of the standard analysis: • Collection of huge amount of data with 100-200 arbitrary parameters • Linear regressions etc.
  6. 6. FORECASTING IN THE ERA OF BIG DATA • MORE DATA ALWAYS LEAD TO BETTER FORECASTING ?
  7. 7. FORECASTING IN THE ERA OF BIG DATA • MORE DATA ALWAYS LEAD TO BETTER FORECASTING ? NOT REALLY • MORE DATA CAN GIVE MORE INFORMATION BUT UNAVOIDABLY LEAD TO MORE NOISE • LARGE DIMENSION OF DATA IS PROBLEMATIC (Poincaré problem in dynamical systems) • SIGNAL TO NOISE RATIO IS THE KEY POINT • EDUCATION + POLLUTION = ???
  8. 8. • LET’S TRY A RADICALLY NEW STRATEGY: FROM 100 TO ZERO PARAMETERS • HIERARCHICAL ORGANIZATION OF DATA FOCUS ON THE MOST INFORMATIVE DATA WITH GOOD SIGNAL TO NOISE RATIO THREE PLATFORMS PLUS THEIR INTERRELATIONS • ECONOMIC FITNESS: COMTRADE DATA ON PRODUCTS + SERVICES FROM WORLD BANK ALSO POSSIBLE: LABOUR DATA • TECHNOLOGICAL FITNESS: PATSAT DATA ON PATENTS AND RELATIVE TECHNOLOGIES • SCIENTIFIC FITNESS: SCIVAL: SCIENTIFIC ACTIVITY AND RELATION TO PATENTS BOTTOM UP DATA MINING. NO IDEOLOGY. GLASS BOX CLEAR TO ALL CITIZENS. RESULTS CAN BE REPRODUCED (SCIENTIFIC)
  9. 9. Monetary measures Metrics for intangibles NEW INFORMATION M. Cristelli, A. Tacchella, L. Pietronero, The Heterogenous Dynamics of Economic Complexity (2015) M. Cristelli, A. Tacchella, L. Pietronero, Economic Complexity: Measuring the Intangibles (ebook) (GDP, GDPpc, etc)
  10. 10. SPECIALIZATION VS. DIVERSIFICATION DATADRIVENAPPROACH: • Math. Problem: minimal elements to have a triangular matrix Complex Hierarchical structure, nestdness etc. • For sectors and companies the situation evolves towards specialization Nested (Triangular Structure) Evidence for leading role of diversification with respect to competitive advantage (specialization) • Globalization • Ecosystems • Evolvability • Adaptation From Qualitative to Quantitative
  11. 11. This leads to the Fitness of countries (intangibles) and the Complexity of products with an iterative for the bipartite country-product network but also for the Technology and Science Networks (Non linear and different from Google) Fitness Complexity Nestedness-Triangularity is a general property of all the data For Products, Patents and Technologies and Scientific Activity Bipartite Nested Networks: FITNESS ALGORITHM
  12. 12. Fitness Complexity • EACH PROBLEM REQUIRES SPECIFIC CONCEPTS AND ALGORITHMS • NETWORK (links rather than sites) IS A KEY CONCEPT BUT THERE WILL BE NO COOK BOOK • THERE IS LIFE AFTER GOOGLE PAGE RANK (which works independently on the specific network structure, i.e. communities) • The FITNESS ALGORITHM is inspired to biosystems; economy as a branch of biology. Best algorithm for ecosystems (M. Munoz et al Sci Rep. 2014) • Recently rediscovered by Hidalgo et al as ECI+ Bipartite Nested Networks: FITNESS ALGORITHM
  13. 13. Bloomberg News November 2015: Goldman's BRIC era ends as Fund folds after years of losses Goldman Sachs 2001: BRIC countries will dominate the World economy for the next 50 years
  14. 14. 1995
  15. 15. ECONOMIC DYNAMICS IS HETEROGENEOUS 1995 - 2010

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