  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.
