摘要翻译：
我们研究了一个创新模型，模型中有大量的公司通过组合几个离散的想法来创造新技术。这些想法可以通过私人投资或社会学习获得。公司面临着在保密和开放之间的选择，前者保护现有的知识产权，后者促进向他人学习。他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。更高的互动率对其他企业产生了正外部性和负外部性，因为学习更多，竞争也更多。我们证明了平衡学习网络在稀疏网络和稠密网络之间处于一个临界阈值。在均衡状态下，相互作用产生的正外部性占主导地位：如果网络更密集，创新率甚至平均企业利润都将显著提高。因此，在临界阈值以上增加相互作用速率会有很大的回报。然而，几种自然类型的干预措施未能使平衡远离临界。一个政策解决方案是引入信息中介，如公共创新者，他们没有保密的动机。这些中介机构可以通过将想法从一个私营公司传递到另一个私营公司来促进高创新均衡。
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英文标题：
《Innovation and Strategic Network Formation》
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作者：
Krishna Dasaratha
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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一级分类：Computer Science        计算机科学
二级分类：Social and Information Networks        社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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英文摘要：
  We study a model of innovation with a large number of firms that create new technologies by combining several discrete ideas. These ideas can be acquired by private investment or via social learning. 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 on other firms, 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 even average firm profits 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 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.
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PDF链接：
https://arxiv.org/pdf/1911.06872