中文摘要
我们研究了一个包含大量企业的创新模型，这些企业通过组合若干离散想法来创造新技术。这些想法通过私人投资产生并在企业之间传播。企业面临着在保密与开放之间的选择：保密保护现有的知识产权，而开放则有助于向他人学习。它们的决策决定了企业之间的互动率，这些互动率作为学习网络中的链接概率进入我们的模型。更高的互动率既带来正外部性也带来负外部性，因为既有更多的学习也有更多的竞争。我们表明，均衡学习网络处于稀疏网络与稠密网络之间的一个临界阈值上。在均衡处，互动的正外部性占主导：如果网络更稠密，创新率和社会福利将大幅提高。因此，将互动率提高到临界阈值以上具有巨大的回报。然而，几种自然类型的干预未能使均衡偏离临界状态。一种有效的政策解决方案是引入信息中介，例如没有保密激励的公共创新者。这些中介可以通过将想法从一家私人企业传递给另一家私人企业，来促进高创新均衡。
Abstract
Abstract We study a model of innovation with a large number of firms that create new technologies by combining several discrete ideas. These ideas are created via private investment and spread between firms. Firms face a choice between secrecy, which protects existing intellectual property, and openness, which facilitates learning from others. Their decisions determine interaction rates between firms, and these interaction rates enter our model as link probabilities in a learning network. Higher interaction rates impose both positive and negative externalities, as there is more learning but also more competition. We show that the equilibrium learning network is at a critical threshold between sparse and dense networks. At equilibrium, the positive externality from interaction dominates: the innovation rate and welfare would be dramatically higher if the network were denser. So there are large returns to increasing interaction rates above the critical threshold. Nevertheless, several natural types of interventions fail to move the equilibrium away from criticality. One effective policy solution is to introduce informational intermediaries, such as public innovators who do not have incentives to be secretive. These intermediaries can facilitate a high-innovation equilibrium by transmitting ideas from one private firm to another.