摘要翻译：
众所周知，社会网络的结构对于Agent能否正确地聚合信息至关重要。本文研究了理性Agent顺序不可撤销行为时支持信息聚合的社会网络。信息是否被汇总，除其他外，取决于代理决定的顺序。因此，为了将序和拓扑解耦，我们的模型研究了一个随机到达序。与固定到达顺序的情况不同，在我们的模型中，代理的决策不太可能受到网络中离他很远的人的影响。这种观察允许我们识别局部学习需求，这是agent邻域上的一种自然条件，它保证无论其他agent表现得多么好，这个agent都能做出正确的决策（以很高的概率）。粗略地说，代理人应该属于许多相互排斥的社会圈子。我们通过构建一个社会网络家族来说明局部学习需求的力量，尽管没有agent是一个社会中心（换句话说，没有意见领袖），但它保证了信息的聚合。尽管社会学习文献的共同智慧表明，信息聚合是非常脆弱的，但局部学习要求的另一个应用表明，即使很大一部分主体没有参与学习过程，也存在学习占上风的网络。在技术层面上，我们构造的网络依赖于扩张图的理论，即从纯数学到纠错码都有广泛应用的高度连通稀疏图。
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英文标题：
《On social networks that support learning》
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作者：
Itai Arieli, Fedor Sandomirskiy, and Rann Smorodinsky
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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：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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英文摘要：
  It is well understood that the structure of a social network is critical to whether or not agents can aggregate information correctly. In this paper, we study social networks that support information aggregation when rational agents act sequentially and irrevocably. Whether or not information is aggregated depends, inter alia, on the order in which agents decide. Thus, to decouple the order and the topology, our model studies a random arrival order.   Unlike the case of a fixed arrival order, in our model, the decision of an agent is unlikely to be affected by those who are far from him in the network. This observation allows us to identify a local learning requirement, a natural condition on the agent\'s neighborhood that guarantees that this agent makes the correct decision (with high probability) no matter how well other agents perform. Roughly speaking, the agent should belong to a multitude of mutually exclusive social circles.   We illustrate the power of the local learning requirement by constructing a family of social networks that guarantee information aggregation despite that no agent is a social hub (in other words, there are no opinion leaders). Although the common wisdom of the social learning literature suggests that information aggregation is very fragile, another application of the local learning requirement demonstrates the existence of networks where learning prevails even if a substantial fraction of the agents are not involved in the learning process. On a technical level, the networks we construct rely on the theory of expander graphs, i.e., highly connected sparse graphs with a wide range of applications from pure mathematics to error-correcting codes.
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