Rediscovery
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916710085296128 |
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| author | Banchio, Martino Malladi, Suraj |
| author_facet | Banchio, Martino Malladi, Suraj |
| contents | We model search in settings where decision makers know what can be found but not where to find it. A searcher faces a set of choices arranged by an observable attribute. Each period, she either selects a choice and pays a cost to learn about its quality, or she concludes search to take her best discovery to date. She knows that similar choices have similar qualities and uses this to guide her search. We identify robustly optimal search policies with a simple structure. Search is directional, recall is never invoked, there is a threshold stopping rule, and the policy at each history depends only on a simple index. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rediscovery Banchio, Martino Malladi, Suraj Theoretical Economics Computer Science and Game Theory We model search in settings where decision makers know what can be found but not where to find it. A searcher faces a set of choices arranged by an observable attribute. Each period, she either selects a choice and pays a cost to learn about its quality, or she concludes search to take her best discovery to date. She knows that similar choices have similar qualities and uses this to guide her search. We identify robustly optimal search policies with a simple structure. Search is directional, recall is never invoked, there is a threshold stopping rule, and the policy at each history depends only on a simple index. |
| title | Rediscovery |
| topic | Theoretical Economics Computer Science and Game Theory |
| url | https://arxiv.org/abs/2504.19761 |