{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T16:43:43Z","timestamp":1782924223227,"version":"3.54.5"},"reference-count":8,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/publication-rights-and-licensing-policy"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGecom Exch."],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    Algorithms increasingly mediate repeated strategic interactions in marketplaces, from automated pricing to auction bidding. When one party commits to a learning algorithm, the other party can respond strategically over time by steering the algorithm's internal state toward a favorable long-run outcome. This note surveys a line of work that studies this \"learning-as-commitment\" perspective via a geometric object we call a\n                    <jats:italic toggle=\"yes\">menu<\/jats:italic>\n                    : the convex set of long-run outcomes an opponent can induce against a fixed learning rule. Menus provide a common language for (i) comparing learning algorithms against strategic opponents, (ii) optimizing over learning rules under uncertainty about opponent objectives, and (iii) characterizing when an opponent can manipulate learning dynamics beyond what they could achieve with a static strategy. Using this machinery, we converge upon no-swap-regret algorithms as an \"optimal\" commitment strategy for robust learning against a strategic opponent. We also identify principled generalizations of no-swap-regret beyond normal-form games that preserve the same strategic guarantees while remaining computationally tractable.\n                  <\/jats:p>","DOI":"10.1145\/3817099.3817104","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:43:59Z","timestamp":1782920639000},"page":"66-75","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Menus: A Framework for Learning Against Strategic Opponents"],"prefix":"10.1145","volume":"23","author":[{"given":"Eshwar Ram","family":"Arunachaleswaran","sequence":"first","affiliation":[{"name":"SESCO Enterprises"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie","family":"Collina","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yishay","family":"Mansour","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehryar","family":"Mohri","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balasubramanian","family":"Sivan","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jon","family":"Schneider","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 26th ACM Conference on Economics and Computation. 130\u2013157","author":"Arunachaleswaran E. R.","unstructured":"Arunachaleswaran, E. R., Collina, N., Mansour, Y., Mohri, M., Schneider, J., and Sivan, B. 2025. Swap regret and correlated equilibria beyond normal-form games. In Proceedings of the 26th ACM Conference on Economics and Computation. 130\u2013157."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 25th ACM Conference on Economics and Computation. 490\u2013510","author":"Arunachaleswaran E. R.","unstructured":"Arunachaleswaran, E. R., Collina, N., and Schneider, J. 2024. Pareto-optimal algorithms for learning in games. In Proceedings of the 25th ACM Conference on Economics and Computation. 490\u2013510."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 26th ACM Conference on Economics and Computation. 478\u2013504","author":"Arunachaleswaran E. R.","unstructured":"Arunachaleswaran, E. R., Collina, N., and Schneider, J. 2025. Learning to play against unknown opponents. In Proceedings of the 26th ACM Conference on Economics and Computation. 478\u2013504."},{"key":"e_1_2_1_4_1","first-page":"6","article-title":"From external to internal regret","volume":"8","author":"Blum A.","year":"2007","unstructured":"Blum, A. and Mansour, Y. 2007. From external to internal regret. Journal of Machine Learning Research 8, 6.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 2018 ACM Conference on Economics and Computation. 523\u2013538","author":"Braverman M.","unstructured":"Braverman, M., Mao, J., Schneider, J., and Weinberg, M. 2018. Selling to a no-regret buyer. In Proceedings of the 2018 ACM Conference on Economics and Computation. 523\u2013538."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. 643\u2013651","author":"Collina N.","unstructured":"Collina, N., Arunachaleswaran, E. R., and Kearns, M. 2023. Efficient stackelberg strategies for finitely repeated games. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. 643\u2013651."},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 56th Annual ACM Symposium on Theory of Computing. 1216\u20131222","author":"Dagan Y.","unstructured":"Dagan, Y., Daskalakis, C., Fishelson, M., and Golowich, N. 2024. From external to swap regret 2.0: An efficient reduction for large action spaces. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing. 1216\u20131222."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 56th Annual ACM Symposium on Theory of Computing. 1223\u20131234","author":"Peng B.","unstructured":"Peng, B. and Rubinstein, A. 2024. Fast swap regret minimization and applications to approximate correlated equilibria. In Proceedings of the 56th Annual ACM Symposium on Theory of Computing. 1223\u20131234."}],"container-title":["ACM SIGecom Exchanges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3817099.3817104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T15:44:42Z","timestamp":1782920682000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3817099.3817104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,1]]},"references-count":8,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1,1]]}},"alternative-id":["10.1145\/3817099.3817104"],"URL":"https:\/\/doi.org\/10.1145\/3817099.3817104","relation":{},"ISSN":["1551-9031"],"issn-type":[{"value":"1551-9031","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,1]]},"assertion":[{"value":"2026-07-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}