网络结构与朴素序列学习

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摘要翻译：
研究了一个具有高斯信息结构的朴素智能体网络的序贯学习模型。代理应用启发式规则来聚合前辈的操作。他们根据自己与不同前任的社会关系的强弱来权衡这些行为。我们证明，当代理错误地认为其他人只根据私人信息行事，从而忽略了观察中的冗余时，这一规则就会内生地产生。我们给出了一个简单的用网络路径表示Agent行为的线性公式，并用这个公式来表征朴素Agent最终正确学习的网络集。这一特性意味着，在所有的网络中，当后继代理观察到一个以上的邻居时，存在着影响过大的早期代理，他们可能会对不正确的行为造成羊群行为。超越现有的社会学习结果，我们精确地计算了这种错误学习的概率。这使得我们可以比较网络结构中不正确的羊群行为的可能性，从而比较预期的福利损失。当链路密度较高和网络集成度较高时，错误学习的概率会增加。在部分隔离的网络中，发散的早期信号会导致群体间持续的分歧。
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英文标题：
《Network Structure and Naive Sequential Learning》
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作者：
Krishna Dasaratha, Kevin He
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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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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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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英文摘要：
  We study a sequential-learning model featuring a network of naive agents with Gaussian information structures. Agents apply a heuristic rule to aggregate predecessors' actions. They weigh these actions according the strengths of their social connections to different predecessors. We show this rule arises endogenously when agents wrongly believe others act solely on private information and thus neglect redundancies among observations. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to characterize the set of networks where naive agents eventually learn correctly. This characterization implies that, on all networks where later agents observe more than one neighbor, there exist disproportionately influential early agents who can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This allows us to compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures. The probability of mislearning increases when link densities are higher and when networks are more integrated. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups.
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PDF链接：
https://arxiv.org/pdf/1703.02105

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关键词：网络结构 Applications Contribution disagreement Quantitative networks information 概率 羊群 序贯