Observational learning in an uncertain world
We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal about a payoff-relevant state of the world, and can observe the actions of other agents who have made a decision before...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/63091 https://orcid.org/0000-0002-1827-1285 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0003-0908-7491 |
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| author | Acemoglu, Daron Dahleh, Munther A. Ozdaglar, Asuman E. Tahbaz Salehi, Alireza |
| author2 | Massachusetts Institute of Technology. Department of Economics |
| author_facet | Massachusetts Institute of Technology. Department of Economics Acemoglu, Daron Dahleh, Munther A. Ozdaglar, Asuman E. Tahbaz Salehi, Alireza |
| author_sort | Acemoglu, Daron |
| collection | MIT |
| description | We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal about a payoff-relevant state of the world, and can observe the actions of other agents who have made a decision before her. We assume that agents do not observe the signals and types of others in the society, and are also uncertain about the type distributions. We show that information is correctly aggregated when preferences of different types are closely aligned. On the other hand, if there is sufficient heterogeneity in preferences, uncertainty about type distributions leads to potential identification problems, preventing asymptotic learning. We also show that even though learning is guaranteed to be incomplete ex ante, there are sample paths over which agents become certain about the underlying state of the world. |
| first_indexed | 2024-09-23T16:58:12Z |
| format | Article |
| id | mit-1721.1/63091 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:58:12Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/630912022-09-29T22:45:50Z Observational learning in an uncertain world Acemoglu, Daron Dahleh, Munther A. Ozdaglar, Asuman E. Tahbaz Salehi, Alireza Massachusetts Institute of Technology. Department of Economics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Ozdaglar, Asuman E. Tahbaz Salehi, Alireza Acemoglu, Daron Dahleh, Munther A. Ozdaglar, Asuman E. We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal about a payoff-relevant state of the world, and can observe the actions of other agents who have made a decision before her. We assume that agents do not observe the signals and types of others in the society, and are also uncertain about the type distributions. We show that information is correctly aggregated when preferences of different types are closely aligned. On the other hand, if there is sufficient heterogeneity in preferences, uncertainty about type distributions leads to potential identification problems, preventing asymptotic learning. We also show that even though learning is guaranteed to be incomplete ex ante, there are sample paths over which agents become certain about the underlying state of the world. United States. Air Force Office of Scientific Research (AFOSR grant FA9550-09-1-0420) United States. Army Research Office (ARO grant W911NF-09-1-0556) National Science Foundation (U.S.) (NSF grant SES- 0729361) 2011-05-24T14:31:11Z 2011-05-24T14:31:11Z 2011-02 2010-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-7745-6 0743-1546 http://hdl.handle.net/1721.1/63091 Acemoglu, D. et al. “Observational Learning in an Uncertain World.” Decision and Control (CDC), 2010 49th IEEE Conference On. 2010. 6645-6650. © 2011 IEEE. https://orcid.org/0000-0002-1827-1285 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0003-0908-7491 en_US http://dx.doi.org/10.1109/CDC.2010.5717483 Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 2010 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Acemoglu, Daron Dahleh, Munther A. Ozdaglar, Asuman E. Tahbaz Salehi, Alireza Observational learning in an uncertain world |
| title | Observational learning in an uncertain world |
| title_full | Observational learning in an uncertain world |
| title_fullStr | Observational learning in an uncertain world |
| title_full_unstemmed | Observational learning in an uncertain world |
| title_short | Observational learning in an uncertain world |
| title_sort | observational learning in an uncertain world |
| url | http://hdl.handle.net/1721.1/63091 https://orcid.org/0000-0002-1827-1285 https://orcid.org/0000-0002-1470-2148 https://orcid.org/0000-0003-0908-7491 |
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