Incentivizing Exploration with Selective Data Disclosure
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2018
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| _version_ | 1866915903499665408 |
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| author | Immorlica, Nicole Mao, Jieming Slivkins, Aleksandrs Wu, Zhiwei Steven |
| author_facet | Immorlica, Nicole Mao, Jieming Slivkins, Aleksandrs Wu, Zhiwei Steven |
| contents | We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. The recommendation system presents each agent with actions and rewards from a subsequence of past agents, chosen ex ante. Thus, the agents engage in sequential social learning, moderated by these subsequences. We asymptotically attain optimal regret rate for exploration, using a flexible frequentist behavioral model and mitigating rationality and commitment assumptions inherent in prior work. We suggest three components of effective recommendation systems: independent focus groups, group aggregators, and interlaced information structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1811_06026 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Incentivizing Exploration with Selective Data Disclosure Immorlica, Nicole Mao, Jieming Slivkins, Aleksandrs Wu, Zhiwei Steven Computer Science and Game Theory Data Structures and Algorithms Machine Learning We propose and design recommendation systems that incentivize efficient exploration. Agents arrive sequentially, choose actions and receive rewards, drawn from fixed but unknown action-specific distributions. The recommendation system presents each agent with actions and rewards from a subsequence of past agents, chosen ex ante. Thus, the agents engage in sequential social learning, moderated by these subsequences. We asymptotically attain optimal regret rate for exploration, using a flexible frequentist behavioral model and mitigating rationality and commitment assumptions inherent in prior work. We suggest three components of effective recommendation systems: independent focus groups, group aggregators, and interlaced information structures. |
| title | Incentivizing Exploration with Selective Data Disclosure |
| topic | Computer Science and Game Theory Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/1811.06026 |