| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w14040 |
| 来源ID | Working Paper 14040 |
| Bayesian Learning in Social Networks | |
| Daron Acemoglu; Munther A. Dahleh; Ilan Lobel; Asuman Ozdaglar | |
| 发表日期 | 2008-05-30 |
| 出版年 | 2008 |
| 语种 | 英语 |
| 摘要 | We study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). The special case where each individual observes all past actions has been widely studied in the literature. We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, individuals converge (in probability) to taking the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of "expansion in observations". Our main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning. This theorem therefore establishes that, with unbounded private beliefs, there will be asymptotic learning an almost all reasonable social networks. We also show that for most network topologies, when private beliefs are bounded, there will not be asymptotic learning. In addition, in contrast to the special case where all past actions are observed, asymptotic learning is possible even with bounded beliefs in certain stochastic network topologies. |
| 主题 | Microeconomics ; Game Theory ; Economics of Information |
| URL | https://www.nber.org/papers/w14040 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/571714 |
| 推荐引用方式 GB/T 7714 | Daron Acemoglu,Munther A. Dahleh,Ilan Lobel,et al. Bayesian Learning in Social Networks. 2008. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w14040.pdf（481KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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