论文标题
技术相互依赖性预测创新动力学
Technological interdependencies predict innovation dynamics
论文作者
论文摘要
我们提出了一个简单的模型，其中技术领域的创新速率取决于其所依赖的技术领域的创新率。使用1836年至2017年美国专利的数据，我们做出样本外预测，发现当考虑到网络效应时，创新率的可预测性可以大大提高。如果知道技术的未来创新率，那么与不包含网络效果的更简单的时间序列模型相比，平均可预测性增益为28 $ \％$。即使对未来一无所知，我们也会发现正平均可预测性获得20 $ \％$。结果具有重要的政策含义，表明给定技术的有效支持必须考虑到围绕目标技术的技术生态系统。
We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and find that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology$'$s neighborhood future innovation rates are known, the average predictability gain is 28$\%$ 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.
