Threshold learning dynamics in social networks.
Threshold learning dynamics in social networks.
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DOI：
10.1371/journal.pone.0020207
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
San Miguel M
中科院分区：
文献类型：
--
作者：
González-Avella JC;Eguíluz VM;Marsili M;Vega-Redondo F;San Miguel M
社会学习被定义为一个群体聚集信息的能力，这一过程必须在很大程度上依赖于社会互动机制。消费者选择购买哪种产品，或者选民决定在一个重要问题上采取哪种选择，通常会面对从他们的联系人那里收集到的信息的外部信号。经济模型通常预测，除非某些个体表现出无限的影响力，否则正确的社会学习发生在大量人群中。我们挑战这一结论表明，一个直观的阈值过程的个人调整并不总是导致这样的社会学习。我们发现，具体而言，三个通用制度存在尖锐的不连续的过渡分开。只有在其中一个阈值处于合适的中间范围内时，种群才能学习正确的信息。在另外两种情况下，当阈值过高或过低时，系统要么冻结，要么进入持续通量。这些制度通常在不同的社交网络（复杂或定期），但有限的互动被发现，以促进正确的学习，通过扩大参数区域发生。
Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs.
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