TY - GEN
T1 - Maximizing the collective learning effects in regional economic development
AU - Gao, Jian
N1 - Funding Information:
Jian Gao acknowledges Cesar A. Hidalgo, Bogang Jun, Flavio Pinheiro, and Tao Zhou for helpful discussions, the Collective Learning Group at the MIT Media Lab for providing the networks and computing resources, and the China Scholarship Council for partial financial support.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - Collective learning in economic development has been revealed by recent empirical studies, however, investigations on how to benefit most from its effects remain still lacking. In this paper, we explore the maximization of the collective learning effects using a simple propagation model to study the diversification of industries on real networks built on Brazilian labor data. For the inter-regional learning, we find an optimal strategy that makes a balance between core and periphery industries in the initial activation, considering the core-periphery structure of the industry space - a network representation of the relatedness between industries. For the inter-regional learning, we find an optimal strategy that makes a balance between nearby and distant regions in establishing new spatial connections, considering the spatial structure of the integrated adjacent network that connects all regions. Our findings suggest that the near to by random strategies are likely to make the best use of the collective learning effects in advancing regional economic development practices.
AB - Collective learning in economic development has been revealed by recent empirical studies, however, investigations on how to benefit most from its effects remain still lacking. In this paper, we explore the maximization of the collective learning effects using a simple propagation model to study the diversification of industries on real networks built on Brazilian labor data. For the inter-regional learning, we find an optimal strategy that makes a balance between core and periphery industries in the initial activation, considering the core-periphery structure of the industry space - a network representation of the relatedness between industries. For the inter-regional learning, we find an optimal strategy that makes a balance between nearby and distant regions in establishing new spatial connections, considering the spatial structure of the integrated adjacent network that connects all regions. Our findings suggest that the near to by random strategies are likely to make the best use of the collective learning effects in advancing regional economic development practices.
KW - Collective learning
KW - Core-periphery structure
KW - Economic development
KW - Percolation
KW - Spatial networks
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U2 - 10.1109/ICCWAMTIP.2017.8301509
DO - 10.1109/ICCWAMTIP.2017.8301509
M3 - Conference contribution
AN - SCOPUS:85042725918
T3 - 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017
SP - 337
EP - 341
BT - 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017
Y2 - 15 December 2017 through 17 December 2017
ER -
