全球智库信息集成服务系统(G2TT): Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w21468
来源ID	Working Paper 21468
	Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field
	Arun G. Chandrasekhar; Horacio Larreguy; Juan Pablo Xandri
发表日期	2015-08-17
出版年	2015
语种	英语
摘要	Agents often use noisy signals from their neighbors to update their beliefs about a state of the world. The effectiveness of social learning relies on the details of how agents aggregate information from others. There are two prominent models of information aggregation in networks: (1) Bayesian learning, where agents use Bayes' rule to assess the state of the world and (2) DeGroot learning, where agents instead consider a weighted average of their neighbors' previous period opinions or actions. Agents who engage in DeGroot learning often double-count information and may not converge in the long run. We conduct a lab experiment in the field with 665 subjects across 19 villages in Karnataka, India, designed to structurally test which model best describes social learning. Seven subjects were placed into a network with common knowledge of the network structure. Subjects attempted to learn the underlying (binary) state of the world, having received independent identically distributed signals in the first period. Thereafter, in each period, subjects made guesses about the state of the world, and these guesses were transmitted to their neighbors at the beginning of the following round. We structurally estimate a model of Bayesian learning, relaxing common knowledge of Bayesian rationality by allowing agents to have incomplete information as to whether others are Bayesian or DeGroot. Our estimates show that, despite the flexibility in modeling learning in these networks, agents are robustly best described by DeGroot-learning models wherein they take a simple majority of previous guesses in their neighborhood.
主题	Econometrics ; Experimental Design ; Microeconomics ; Economics of Information
URL	https://www.nber.org/papers/w21468
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/579143
推荐引用方式 GB/T 7714	Arun G. Chandrasekhar,Horacio Larreguy,Juan Pablo Xandri. Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field. 2015.