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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.19396 |
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| _version_ | 1866912349687906304 |
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| author | Wu, Shuo Poojary, Pawan Berry, Randall |
| author_facet | Wu, Shuo Poojary, Pawan Berry, Randall |
| contents | We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Observational Learning with a Budget Wu, Shuo Poojary, Pawan Berry, Randall Machine Learning Social and Information Networks We consider a model of Bayesian observational learning in which a sequence of agents receives a private signal about an underlying binary state of the world. Each agent makes a decision based on its own signal and its observations of previous agents. A central planner seeks to improve the accuracy of these signals by allocating a limited budget to enhance signal quality across agents. We formulate and analyze the budget allocation problem and propose two optimal allocation strategies. At least one of these strategies is shown to maximize the probability of achieving a correct information cascade. |
| title | Observational Learning with a Budget |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2504.19396 |