摘要翻译：
我们考虑一组战略代理人，他们每个人都必须重复采取两种可能的行动中的一种。他们从最初的私人信号中，并通过观察社交网络中邻居的行为来学习这两种行为中哪一种更可取。我们证明了智能体是否有效地学习取决于社会网络的拓扑结构。特别地，我们在社会网络上确定了一个几何上的“平均主义”条件，它保证在无限网络中的学习，或者在大的有限网络中的高概率学习，在任何均衡状态下。我们也给出了学习失败的具有均衡的非平等网络的例子。
---
英文标题：
《Strategic Learning and the Topology of Social Networks》
---
作者：
Elchanan Mossel and Allan Sly and Omer Tamuz
---
最新提交年份：
2015
---
分类信息：

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。
--
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。
--
一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
--

---
英文摘要：
  We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals, and by observing the actions of their neighbors in a social network.   We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of non-egalitarian networks with equilibria in which learning fails.
---
PDF链接：
https://arxiv.org/pdf/1209.5527