{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:15:02Z","timestamp":1750306502189,"version":"3.41.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2015,11,12]],"date-time":"2015-11-12T00:00:00Z","timestamp":1447286400000},"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":["SIGecom Exch."],"published-print":{"date-parts":[[2015,11,12]]},"abstract":"<jats:p>\n            A\n            <jats:italic>Stackelberg game<\/jats:italic>\n            is played between a\n            <jats:italic>leader<\/jats:italic>\n            and a\n            <jats:italic>follower.<\/jats:italic>\n            The leader first chooses an action, and then the follower plays his best response, and the goal of the leader is to pick the action that will maximize his payoff given the follower's best response. Stackelberg games capture, for example, the following interaction between a retailer and a buyer. The retailer chooses the\n            <jats:italic>prices<\/jats:italic>\n            of the goods he produces, and then the buyer chooses to buy a utility-maximizing bundle of goods. The goal of the retailer here is to set prices to maximize his profit---his revenue minus the production cost of the purchased bundle. It is quite natural that the retailer in this example would not know the buyer's utility function. However, he does have access to\n            <jats:italic>revealed preference<\/jats:italic>\n            feedback---he can set prices, and then observe the purchased bundle and his own profit. We give algorithms for efficiently solving, in terms of both computational and query complexity, a broad class of Stackelberg games in which the follower's utility function is unknown, using only \"revealed preference\" access to it. This class includes the profit maximization problem, as well as the optimal tolling problem in nonatomic congestion games, when the latency functions are unknown. Surprisingly, we are able to solve these problems even though the corresponding maximization problems are not concave in the leader's actions.\n          <\/jats:p>","DOI":"10.1145\/2845926.2845934","type":"journal-article","created":{"date-parts":[[2015,11,13]],"date-time":"2015-11-13T14:19:41Z","timestamp":1447424381000},"page":"101-104","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Watch and learn"],"prefix":"10.1145","volume":"14","author":[{"given":"Aaron","family":"Roth","sequence":"first","affiliation":[{"name":"University of Pennsylvania"}]},{"given":"Jonathan","family":"Ullman","sequence":"additional","affiliation":[{"name":"Northeastern University"}]},{"given":"Zhiwei Steven","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Pennsylvania"}]}],"member":"320","published-online":{"date-parts":[[2015,11,12]]},"reference":[{"volume-title":"Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)","author":"Amin K.","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Balcan M.-F. Daniely A. Mehta R. Urner R. and Vazirani V. V. 2014. Learning economic parameters from revealed preferences. In Web and Internet Economics. Springer 338--353.  Balcan M.-F. Daniely A. Mehta R. Urner R. and Vazirani V. V. 2014. Learning economic parameters from revealed preferences. In Web and Internet Economics. Springer 338--353.","DOI":"10.1007\/978-3-319-13129-0_28"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1134707.1134712"},{"key":"e_1_2_1_4_1","unstructured":"Belloni A. Liang T. Narayanan H. and Rakhlin A. 2015. Escaping the local minima via simulated annealing: Optimization of approximately convex functions. CoRR abs\/1501.07242.  Belloni A. Liang T. Narayanan H. and Rakhlin A. 2015. Escaping the local minima via simulated annealing: Optimization of approximately convex functions. CoRR abs\/1501.07242."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2014.12"},{"key":"e_1_2_1_6_1","unstructured":"Mas-Colell A. Whinston M. D. and Green J. R. 1995. Microeconomic Theory. Oxford University Press.  Mas-Colell A. Whinston M. D. and Green J. R. 1995. Microeconomic Theory. Oxford University Press."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Roth A. Ullman J. and Wu Z. S. 2015. Watch and learn: Optimizing from revealed preferences feedback. arXiv preprint arXiv:1504.01033.  Roth A. Ullman J. and Wu Z. S. 2015. Watch and learn: Optimizing from revealed preferences feedback. arXiv preprint arXiv:1504.01033.","DOI":"10.1145\/2897518.2897579"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Rubinstein A. 2012. Lecture notes in microeconomic theory: the economic agent. Princeton University Press.  Rubinstein A. 2012. Lecture notes in microeconomic theory: the economic agent. Princeton University Press.","DOI":"10.1515\/9781400842469"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.2307\/2548836"},{"volume-title":"Samuelsonian economics and the twenty-first century","author":"Varian H. 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