MIT14_15JF09_lec22_23

MIT14_15JF09_lec22_23 - 6.207/14.15: Networks Lectures...

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Unformatted text preview: 6.207/14.15: Networks Lectures 22-23: Social Learning in Networks Daron Acemoglu and Asu Ozdaglar MIT December 2 end 7, 2009 1 Networks: Lectures 22-23 Introduction Outline Recap on Bayesian social learning Non-Bayesian (myopic) social learning in networks Bayesian observational social learning in networks Bayesian communication social learning in networks Reading: Jackson, Chapter 8. EK, Chapter 16. 2 Networks: Lectures 22-23 Introduction Introduction How does network structure and inuence of specific individuals affect opinion formation and learning? To answer this question, we need to extend the simple example of herding from the previous literature to a network setting. Question: is Bayesian social learning the right benchmark? Pro: Natural benchmark and often simple heuristics can replicate it Con: Often complex Non-Bayesian myopic learning: (rule-of-thumb) Pro: Simple and often realistic Con: Arbitrary rules-of-thumb, different performances from different rules, how to choose the right one? 3 Networks: Lectures 22-23 Introduction What Kind of Learning? What do agents observe? Observational learning: observe past actions (as in the example) Most relevant for markets Communication learning: communication of beliefs or estimates Most relevant for friendship networks (such as Facebook) The model of social learning in the previous lecture was a model of Bayesian observational learning. It illustrated the possibility of herding , where everybody copies previous choices, and thus the possibility that dispersely held information may fail to aggregate. 4 Networks: Lectures 22-23 Recap of Herding Recap of Herding Agents arrive in town sequentially and choose to dine in an Indian or in a Chinese restaurant. A restaurant is strictly better, underlying state { Chinese , Indian } . Agents have independent binary private signals. Signals indicate the better option with probability p > 1 / 2. Agents observe prior decisions, but not the signals of others. Realization: Assume = Indian Agent 1 arrives. Her signal indicates Chinese. She chooses Chinese. Agent 2 arrives. His signal indicates Chinese. He chooses Chinese. Agent 3 arrives. Her signal indicates Indian. She disregards her signal and copies the decisions of agents 1 and 2, and so on. 1 Decision = Chinese 2 Decision = Chinese 3 Decision = Chinese 5 Networks: Lectures 22-23 Recap of Herding Potential Challenges Perhaps this is too sophisticated. What about communication? Most agents not only learn from observations, but also by communicating with friends and coworkers. Let us turn to a simple model of myopic (rule-of-thumb) learning and also incorporate network structure....
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MIT14_15JF09_lec22_23 - 6.207/14.15: Networks Lectures...

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