Learning models in social networks
This thesis studies the problem of modeling learning through a set of agents connected via a social network. We first analyze a robust detection problem in a tandem network where agents sequentially receive private signals about the state of the world, as well as the decision of their predecesso...
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| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | https://hdl.handle.net/10356/85449 http://hdl.handle.net/10220/50121 |
| _version_ | 1826115230440095744 |
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| author | Ho, Jack |
| author2 | Tay Wee Peng |
| author_facet | Tay Wee Peng Ho, Jack |
| author_sort | Ho, Jack |
| collection | NTU |
| description | This thesis studies the problem of modeling learning through a set of agents connected via a social network.
We first analyze a robust detection problem in a tandem network where agents sequentially receive private signals about the state of the world, as well as the decision of their predecessors, and then attempt to make a decision about the state of the world. The knowledge they have regarding their predecessors' private signals, however, is incomplete, and we propose a policy to allow agents to minimize the worst-case error probability over all possible distributions of their predecessors' private signals.
We then consider the problem of online advertising, where an online retailer wishes to learn more about users in a social network. We model the problem in a variety of different ways. One of these models is optimizing a string-submodular function with incomplete information about the step-wise gains of each user. Another is through two different multi-armed bandit models. |
| first_indexed | 2024-10-01T03:52:08Z |
| format | Thesis |
| id | ntu-10356/85449 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:52:08Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/854492023-07-04T17:18:21Z Learning models in social networks Ho, Jack Tay Wee Peng School of Electrical and Electronic Engineering Engineering::Mathematics and analysis::Simulations This thesis studies the problem of modeling learning through a set of agents connected via a social network. We first analyze a robust detection problem in a tandem network where agents sequentially receive private signals about the state of the world, as well as the decision of their predecessors, and then attempt to make a decision about the state of the world. The knowledge they have regarding their predecessors' private signals, however, is incomplete, and we propose a policy to allow agents to minimize the worst-case error probability over all possible distributions of their predecessors' private signals. We then consider the problem of online advertising, where an online retailer wishes to learn more about users in a social network. We model the problem in a variety of different ways. One of these models is optimizing a string-submodular function with incomplete information about the step-wise gains of each user. Another is through two different multi-armed bandit models. Doctor of Philosophy 2019-10-09T12:49:47Z 2019-12-06T16:03:53Z 2019-10-09T12:49:47Z 2019-12-06T16:03:53Z 2019 Thesis Ho, J. (2019). Learning models in social networks. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/85449 http://hdl.handle.net/10220/50121 10.32657/10356/85449 en 122 p. application/pdf |
| spellingShingle | Engineering::Mathematics and analysis::Simulations Ho, Jack Learning models in social networks |
| title | Learning models in social networks |
| title_full | Learning models in social networks |
| title_fullStr | Learning models in social networks |
| title_full_unstemmed | Learning models in social networks |
| title_short | Learning models in social networks |
| title_sort | learning models in social networks |
| topic | Engineering::Mathematics and analysis::Simulations |
| url | https://hdl.handle.net/10356/85449 http://hdl.handle.net/10220/50121 |
| work_keys_str_mv | AT hojack learningmodelsinsocialnetworks |