Social Learning with Endogenous Network Formation
Social Learning with Endogenous Network Formation
复制标题
具有内生网络形成的社会学习
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Yangbo Song
中科院分区：
文献类型：
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作者：
Yangbo Song
我在一个主体依次行动的模型中研究社会学习问题。每个主体都会收到一个关于世界潜在状态的私人信号，观察附近个体过去的行动，并选择自己的行动以试图匹配真实状态。该领域早期的研究强调，在某些特殊情况下，羊群行为以正概率发生；近期的研究表明，在更一般的观察结构下渐近学习是可实现的。特别是，在私人信念无界的情况下，当且仅当主体观察到紧邻的前一个主体时，即紧邻前一个主体的行动在极限情况下揭示真实状态时，渐近学习才会发生。然而，这些研究中一个普遍的假设是社会中的观察结构是外生的。与以往大多数文献不同，我在本文中假设观察是内生的且有成本的。更具体地说，每个主体必须支付特定成本才能进行任何观察，并且可以策略性地选择私下观察的行动集。我引入最大学习（相对于成本）的概念，作为渐近学习的自然延伸：当主体在支付观察成本后能以极限概率1学习到真实状态时，社会实现最大学习。我表明，在私人信念无界且成本为正的情况下，仅观察紧邻的前一个主体不再足以学习到真实状态。相反，当且仅当观察规模趋于无穷时，最大学习才会发生。我提供了有趣的比较静态分析，说明模型中的各种设定如何影响学习概率。例如，在正成本下学习到真实状态的概率可能比零成本下更高；此外，在较弱的私人信号下学习到真实状态的概率可能更高。
I study the problem of social learning in a model where agents move sequentially. Each agent receives a private signal about the underlying state of the world, observes the past actions in a neighborhood of individuals, and chooses her action attempting to match the true state. Earlier research in this field emphasizes that herding behavior occurs with a positive probability in certain special cases; recent studies show that asymptotic learning is achievable under a more general observation structure. In particular, with unbounded private beliefs, asymptotic learning occurs if and only if agents observe a close predecessor, i.e., the action of a close predecessor reveals the true state in the limit. However, a prevailing assumption in these studies is that the observation structure in the society is exogenous. In contrast to most of the previous literature, I assume in this paper that observation is endogenous and costly. More specifically, each agent must pay a specific cost to make any observation and can strategically choose the set of actions to observe privately. I introduce the notion of maximal learning (relative to cost) as a natural extension of asymptotic learning: society achieves maximal learning when agents can learn the true state with probability 1 in the limit after paying the cost of observation. I show that observing only a close predecessor is no longer sufficient for learning the true s with unbounded private beliefs and positive costs. Instead, maximal learning occurs if and only if the size of the observations extends to infinity. I provide interesting comparative statics as to how various settings in the model affect the learning probability. For instance, the probability to learn the true state may be higher under positive costs than under zero cost; in addition, the probability to learn the true state may be higher under weaker private signals.