Biased Social Learning
Biased Social Learning
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有偏见的社会学习
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Johannes Hörner
中科院分区：
文献类型：
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作者：
H. Herrera;Johannes Hörner
本文探讨了社会学习时，只有两种类型的决定是可观察的。由于代理人随着时间的推移随机到达，并且只有那些投资的人被观察到，所以后来的代理人面临着比标准模型更复杂的推理问题，因为没有投资可能反映出不投资的选择，或者没有到达。我们表明，在标准模型中，学习是完全的，当且仅当信号是无界的。如果信号是有界的，级联可能会发生，它们是否比标准模型更有可能或更不可能取决于信号分布的属性。如果分布的风险比在信号中增加，则更有可能没有人投资于标准模型而不是这个模型，福利更高。如果风险比降低，则结论相反。风险比的单调性是保证标准羊群模型中存在或不存在信息级联的条件。
This paper examines social learning when only one of the two types of decisions is observable. Because agents arrive randomly over time, and only those who invest are observed, later agents face a more complicated inference problem than in the standard model, as the absence of investment might reflect either a choice not to invest, or a lack of arrivals. We show that, as in the standard model, learning is complete if and only if signals are unbounded. If signals are bounded, cascades may occur, and whether they are more or less likely than in the standard model depends on a property of the signal distribution. If the hazard ratio of the distributions increases in the signal, it is more likely that no one invests in the standard model than in this one, and welfare is higher. Conclusions are reversed if the hazard ratio is decreasing. The monotonicity of the hazard ratio is the condition that guarantees the presence or absence of informational cascades in the standard herding model.