TESTING MODELS OF SOCIAL LEARNING ON NETWORKS: EVIDENCE FROM TWO EXPERIMENTS
TESTING MODELS OF SOCIAL LEARNING ON NETWORKS: EVIDENCE FROM TWO EXPERIMENTS
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DOI：
10.3982/ecta14407
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发表时间：
2020-01-01
期刊：
影响因子：
6.1
通讯作者：
Xandri, Juan Pablo
中科院分区：
文献类型：
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作者：
Chandrasekhar, Arun G.;Larreguy, Horacio;Xandri, Juan Pablo
我们从理论上和实证上研究了社会学习的不完全信息模型。特工在观察私人信号后最初猜测世界的二元状态。在随后的几轮中，智能体在再次猜测之前会观察其网络邻居之前的猜测。智能体由多种学习类型组成——贝叶斯学派，他们面临有关其他人类型的不完整信息；德格鲁特学派，对邻居之前的猜测进行平均并跟随大多数人。我们研究（1）在我们的不完全信息模型中两种类型代理的学习特征； （2）哪些网络结构会导致渐近学习失败； (3)现实网络是否表现出这样的结构。我们对印度村庄的 665 名受试者和墨西哥 ITAM 的 350 名学生进行了实验室实验。我们进行了简化形式分析，然后从结构上估计了混合参数，发现印度村民和墨西哥学生样本中贝叶斯代理的比例分别为 10% 和 50%。
We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types-Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.