{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:40:44Z","timestamp":1770756044836,"version":"3.50.0"},"reference-count":71,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T00:00:00Z","timestamp":1738886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Econ. Comput."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Most online platforms learn from interactions with users and engage in\n            <jats:italic>exploration<\/jats:italic>\n            : making potentially suboptimal choices to acquire new information. We study the interplay between\n            <jats:italic>exploration<\/jats:italic>\n            and\n            <jats:italic>competition<\/jats:italic>\n            : how such platforms balance the exploration for learning and competition for users.\n          <\/jats:p>\n          <jats:p>We consider a stylized duopoly in which two firms face the same multi-armed bandit problem. Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen. We study whether competition incentivizes the adoption of better algorithms. We find that stark competition disincentivizes exploration, leading to low welfare. However, weaker competition incentivizes better exploration algorithms and increases welfare. We investigate two channels for weakening the competition: stochastic user choice models and a first-mover advantage. Our findings speak to the competition\u2013innovation relationship and the first-mover advantage in the digital economy.<\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3711831","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T11:20:36Z","timestamp":1736508036000},"page":"1-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Competing Bandits: The Perils of Exploration Under Competition"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3109-4241","authenticated-orcid":false,"given":"Guy","family":"Aridor","sequence":"first","affiliation":[{"name":"Marketing, Northwestern University, Evanston, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6891-2645","authenticated-orcid":false,"given":"Yishay","family":"Mansour","sequence":"additional","affiliation":[{"name":"Computer Science, Tel Aviv University, Tel Aviv, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6899-6383","authenticated-orcid":false,"given":"Aleksandrs","family":"Slivkins","sequence":"additional","affiliation":[{"name":"Microsoft Research New York, New York, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8125-8227","authenticated-orcid":false,"given":"Steven","family":"Wu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Alekh Agarwal Sarah Bird Markus Cozowicz Luong Hoang John Langford Stephen Lee Jiaji Li Dan Melamed Gal Oshri Oswaldo Ribas Siddhartha Sen and Alex Slivkins. 2017. 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