Non-Bayesian Social Learning
Non-Bayesian Social Learning
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DOI：
10.2139/ssrn.1916109
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
A. Jadbabaie;Pooya Molavi;Alvaro Sandroni;A. Tahbaz-Salehi
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文献类型：
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作者：
A. Jadbabaie;Pooya Molavi;Alvaro Sandroni;A. Tahbaz-Salehi
当学习回报的参数可能无法与邻居进行交流以从他们的经验中学习时，我们在社交网络中开发了一种动态的意见形成模型。代理商没有以完全贝叶斯的方式融合邻居的观点尽管邻居的观点可能是不准确的）。 ，重复的互动导致他们成功地汇总了信息，并学习了世界的真正潜在状态。更新规则，代理商对来自来源的信息的需求是他们可能不知道的存在，网络中最具说服力的代理人的可能性恰恰是那些最不知情的人，并且具有最糟糕的先前观点，并且假设没有任何代理商可以告诉她自己的观点或邻居的观点更准确。
We develop a dynamic model of opinion formation in social networks when the information required for learning a payoff-relevant parameter may not be at the disposal of any single agent. Individuals engage in communication with their neighbors in order to learn from their experiences. However, instead of incorporating the views of their neighbors in a fully Bayesian manner, agents use a simple updating rule which linearly combines their personal experience and the views of their neighbors (even though the neighbors’ views may be quite inaccurate). This non-Bayesian learning rule is motivated by the formidable complexity required to fully implement Bayesian updating in networks. We show that, as long as individuals take their personal signals into account in a Bayesian way, repeated interactions lead them to successfully aggregate information and learn the true underlying state of the world. This result holds in spite of the apparent na¨ivet´e of agents’ updating rule, the agents’ need for information from sources the existence of which they may not be aware of, the possibility that the most persuasive agents in the network are precisely those least informed and with worst prior views, and the assumption that no agent can tell whether her own views or those of her neighbors are more accurate.