2017/15 | LEM Working Paper Series | ||||||||||||||||
Dynamic Increasing Returns and Innovation Diffusion: bringing Polya Urn processes to the empirical data |
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Giovanni Dosi, Alessio Moneta, Elena Stepanova |
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Keywords | |||||||||||||||||
Polya urn schemes, Innovation diffusion, Logistic diffusion pattern, Dynamic increasing returns
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JEL Classifications | |||||||||||||||||
C63, O33
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Abstract | |||||||||||||||||
The patterns of innovation diffusion are well approximated by the
logistic curves. This is the robust empirical fact confirmed by many
studies in innovations dynamics. Here we show that the logistic
pattern of innovation diffusion can be replicated by the
time-dependent stochastic process with positive feedbacks along the
diffusion trajectory. The dynamic increasing returns process is
modeled by generalized Polya urns. So far, urn models have been mostly
used to study the [path-dependent] limit properties. On the contrary,
this work focuses on the transient [finite time] properties studying
the conditions under which urn models capture the logistic
trajectories which often track empirical diffusion process. As
examples, we calibrate the process to match several cases of diffusion
of motor ships in European countries.
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