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Technological interdependencies predict innovation dynamics

Anton Pichler, François Lafond () and J. Doyne Farmer

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Abstract: We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and find that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology$'$s neighborhood future innovation rates are known, the average predictability gain is 28$\%$ compared to simpler time series model which do not incorporate network effects. Even when nothing is known about the future, we find positive average predictability gains of 20$\%$. The results have important policy implications, suggesting that the effective support of a given technology must take into account the technological ecosystem surrounding the targeted technology.

Date: 2020-03
New Economics Papers: this item is included in nep-ino
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