{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:48:59Z","timestamp":1777733339403,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>Moulin [1980] characterizes the single-facility, deterministic strategy-proof mechanisms for social choice with single-peaked preferences as the set of generalized median rules. In contrast, we have only a limited understanding of multi-facility strategy-proof mechanisms, and recent work has shown negative worst case results for social cost. Our goal is to design strategy-proof, multi-facility mechanisms that minimize expected social cost. We first give a PAC learnability result for the class of multi-facility generalized median rules, and utilize neural networks to learn mechanisms from this class. Even in the absence of characterization results, we develop a computational procedure for learning almost strategy-proof mechanisms that are as good as or better than benchmarks from the literature, such as the best percentile and dictatorial rules.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/36","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"261-267","source":"Crossref","is-referenced-by-count":26,"title":["Deep Learning for Multi-Facility Location Mechanism Design"],"prefix":"10.24963","author":[{"given":"Noah","family":"Golowich","sequence":"first","affiliation":[{"name":"Harvard University, SEAS, 33 Oxford Street, Cambridge, MA, 02138"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harikrishna","family":"Narasimhan","sequence":"additional","affiliation":[{"name":"Harvard University, SEAS, 33 Oxford Street, Cambridge, MA, 02138"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David C.","family":"Parkes","sequence":"additional","affiliation":[{"name":"Harvard University, SEAS, 33 Oxford Street, Cambridge, MA, 02138"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:27Z","timestamp":1530755367000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/36"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/36","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}