解决网络中的信息不对称：一种新的基于熵的排名指数
Tackling information asymmetry in networks: a new entropy-based ranking index
摘要：
信息是社会经济系统中各行为体的宝贵资产，其中很大一部分信息被纳入各行为体之间的联系网络。事实上，代理人建立的不同的相互联系模式可能导致网络结构知识的不对称;由于这需要不同的量化相关系统属性（例如，负债网络中的金融传染风险）的能力，能够更好地估计（否则）无法获得的网络属性的代理人最终具有竞争优势。在本文中，我们首次解决了网络拓扑结构所产生的信息不对称的量化问题。为了这个目的，我们定义了一个新的指数- InfoRank -旨在衡量每个节点所拥有的信息的质量，计算香农熵的合奏条件下的节点特定的信息。此外，我们测试我们的新的排名过程中的（不可访问的）网络结构的重建精度方面的性能，并表明它优于其他流行的中心性措施，在确定“信息最丰富”的节点。最后，我们讨论了网络信息不对称的社会经济影响。
英文摘要：
Information is a valuable asset for agents in socio-economic systems, a significant part of the information being entailed into the very network of connections between agents. The different interlinkages patterns that agents establish may, in fact, lead to asymmetries in the knowledge of the network structure; since this entails a different ability of quantifying relevant systemic properties (e.g. the risk of financial contagion in a network of liabilities), agents capable of providing a better estimate of (otherwise) unaccessible network properties, ultimately have a competitive advantage. In this paper, we address for the first time the issue of quantifying the information asymmetry arising from the network topology. To this aim, we define a novel index - InfoRank - intended to measure the quality of the information possessed by each node, computing the Shannon entropy of the ensemble conditioned on the node-specific information. Further, we test the performance of our novel ranking procedure in terms of the reconstruction accuracy of the (unaccessible) network structure and show that it outperforms other popular centrality measures in identifying the "most informative" nodes. Finally, we discuss the socio-economic implications of network information asymmetry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
