{"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","has_accepted_license":"1","date_published":"2024-06-25T00:00:00Z","volume":334,"intvolume":" 334","doi":"10.1016/j.artint.2024.104171","file_date_updated":"2024-07-03T13:08:12Z","month":"06","year":"2024","corr_author":"1","publisher":"Elsevier","date_updated":"2024-07-03T13:25:57Z","oa":1,"article_number":"104171","quality_controlled":"1","day":"25","language":[{"iso":"eng"}],"ddc":["000"],"scopus_import":"1","type":"journal_article","publication_identifier":{"issn":["0004-3702"]},"license":"https://creativecommons.org/licenses/by/4.0/","abstract":[{"lang":"eng","text":"In a delegation problem, a principal P with commitment power tries to pick one out of π options.\r\nEach option is drawn independently from a known distribution. Instead of inspecting the options\r\nherself, P delegates the information acquisition to a rational and self-interested agent A. After\r\ninspection, A proposes one of the options, and P can accept or reject.\r\nDelegation is a classic setting in economic information design with many prominent applications,\r\nbut the computational problems are only poorly understood. In this paper, we study a natural\r\nonline variant of delegation, in which the agent searches through the options in an online fashion.\r\nFor each option, he has to irrevocably decide if he wants to propose the current option or discard\r\nit, before seeing information on the next option(s). How can we design algorithms for P that\r\napproximate the utility of her best option in hindsight?\r\nWe show that in general P can obtain a Ξ(1βπ)-approximation and extend this result to ratios\r\nof Ξ(πβπ) in case (1) A has a lookahead of π rounds, or (2) A can propose up to π different\r\noptions. We provide fine-grained bounds independent of π based on three parameters. If the ratio\r\nof maximum and minimum utility for A is bounded by a factor πΌ, we obtain an Ξ©(loglog πΌβ log πΌ)-\r\napproximation algorithm, and we show that this is best possible. Additionally, if P cannot\r\ndistinguish options with the same value for herself, we show that ratios polynomial in 1βπΌ cannot\r\nbe avoided. If there are at most π½ different utility values for A, we show a Ξ(1βπ½)-approximation.\r\nIf the utilities of P and A for each option are related by a factor πΎ, we obtain an Ξ©(1β log πΎ)-\r\napproximation, where π(log log πΎβ log πΎ) is best possible."}],"publication":"Artificial Intelligence","external_id":{"arxiv":["2203.01084"]},"article_processing_charge":"No","department":[{"_id":"MoHe"}],"citation":{"ieee":"P. Braun, N. Hahn, M. Hoefer, and C. Schecker, βDelegated online search,β Artificial Intelligence, vol. 334. Elsevier, 2024.","ista":"Braun P, Hahn N, Hoefer M, Schecker C. 2024. Delegated online search. Artificial Intelligence. 334, 104171.","mla":"Braun, Pirmin, et al. βDelegated Online Search.β Artificial Intelligence, vol. 334, 104171, Elsevier, 2024, doi:10.1016/j.artint.2024.104171.","short":"P. Braun, N. Hahn, M. Hoefer, C. Schecker, Artificial Intelligence 334 (2024).","apa":"Braun, P., Hahn, N., Hoefer, M., & Schecker, C. (2024). Delegated online search. Artificial Intelligence. Elsevier. https://doi.org/10.1016/j.artint.2024.104171","chicago":"Braun, Pirmin, Niklas Hahn, Martin Hoefer, and Conrad Schecker. βDelegated Online Search.β Artificial Intelligence. Elsevier, 2024. https://doi.org/10.1016/j.artint.2024.104171.","ama":"Braun P, Hahn N, Hoefer M, Schecker C. Delegated online search. Artificial Intelligence. 2024;334. doi:10.1016/j.artint.2024.104171"},"article_type":"original","date_created":"2024-06-30T22:01:05Z","author":[{"full_name":"Braun, Pirmin","first_name":"Pirmin","last_name":"Braun"},{"id":"0a01c7b2-b823-11ed-9928-cc3f874f9ffd","first_name":"Niklas","full_name":"Hahn, Niklas","last_name":"Hahn"},{"last_name":"Hoefer","full_name":"Hoefer, Martin","first_name":"Martin"},{"last_name":"Schecker","first_name":"Conrad","full_name":"Schecker, Conrad"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"_id":"17188","publication_status":"published","file":[{"file_id":"17195","creator":"cchlebak","file_size":763686,"access_level":"open_access","date_created":"2024-07-03T13:08:12Z","relation":"main_file","success":1,"date_updated":"2024-07-03T13:08:12Z","checksum":"4095782466775de63bd48fcea4e48418","file_name":"2024_ArtIntel_Braun.pdf","content_type":"application/pdf"}],"status":"public","oa_version":"Published Version","title":"Delegated online search"}