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PLOS ONE
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M. Bardoscia, S. Battiston, F. Caccioli, G. Caldarelli
PLOS ONE
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F. Lillo, P. Barucca
Computational Management Science
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Random close packing fractions of lognormal distributions of hard spheres
R. Farr
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How much can we influence the rate of innovation?
T. Fink, M. Reeves
Sub. to
Science Advances
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Photonic Maxwell’s Demon
M. Vidrighin, O. Dahlsten, M. Barbieri, M. Kim, V. Vedral, I. Walmsley
Physical Review Letters
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The mise en scene of memristive networks: effective memory, dynamics and learning
F. Caravelli
International Journal of Parallel, Emergent and Distributed Systems
98 views
Bootstrapping topology and systemic risk of complex network using the fitness model
N. Musmeci, S. Battiston, G. Caldarelli, M. Puliga, A. Gabrielli
Journal of Statistical Physics
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Serendipity and strategy in rapid innovation
T. Fink, M. Reeves, R. Palma, R. Farr
Nature Communications
194 views
Entanglement typicality
O. Dahlsten, C. Lupo, S. Mancini, A. Serafini
Journal of Physics A
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Searching for Great Strategies
T. Fink, P. Ghemawat, M. Reeves
Strategy Science
78 views
Low-temperature behaviour of social and economic networks
D. Garlaschelli, S. Ahnert, T. Fink, G. Caldarelli
Entropy
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How predictable is technological progress?
D. Farmer, F. Lafond
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76 papers
The heterogeneous dynamics of economic complexity
M. Cristelli, A. Tacchella, L. Pietronero
PLOS ONE
10, 1371 (2015)
What will be the growth of the Gross Domestic Product (GDP) or the competitiveness of China, United States, and Vietnam in the next 3, 5 or 10 years? Despite this kind of questions has a large societal impact and an extreme value for economic policy making, providing a scientific basis for economic predictability is still a very challenging problem. Recent results of a new branch—Economic Complexity—have set the basis for a framework to approach such a challenge and to provide new perspectives to cast economic prediction into the conceptual scheme of forecasting the evolution of a dynamical system as in the case of weather dynamics. We argue that a recently introduced non-monetary metrics for country competitiveness (fitness) allows for quantifying the hidden growth potential of countries by the means of the comparison of this measure for intangible assets with monetary figures, such as GDP per capita. This comparison defines the fitness-income plane where we observe that country dynamics presents strongly heterogeneous patterns of evolution. The flow in some zones is found to be laminar while in others a chaotic behavior is instead observed. These two regimes correspond to very different predictability features for the evolution of countries: in the former regime, we find strong predictable pattern while the latter scenario exhibits a very low predictability. In such a framework, regressions, the usual tool used in economics, are no more the appropriate strategy to deal with such a heterogeneous scenario and new concepts, borrowed from dynamical systems theory, are mandatory. We therefore propose a data-driven method—the selective predictability scheme—in which we adopt a strategy similar to the methods of analogues, firstly introduced by Lorenz, to assess future evolution of countries.
China Might Still Be Booming
The heterogeneous dynamics of economic complexity
Bloomberg View, 2015-03-01
Physicists make ‘weather forecasts’ for economies in EU-funded project GROWTHCOM
The heterogeneous dynamics of economic complexity
European Commission, 2015-02-25