{"title":"自适应算法与耦合合谋","authors":"M. Banchio, Giacomo Mantegazza","doi":"10.1145/3580507.3597726","DOIUrl":null,"url":null,"abstract":"Learning algorithms are proliferating in a variety of business contexts, ranging from automated bidding in online auctions to pricing on shopping platforms and setting rents. This diffusion has been accompanied by fears that such automation could facilitate collusion. A number of recent papers on algorithmic pricing show in simulations that learning algorithms coordinate on less-than-competitive outcomes.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive Algorithms and Collusion via Coupling\",\"authors\":\"M. Banchio, Giacomo Mantegazza\",\"doi\":\"10.1145/3580507.3597726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Learning algorithms are proliferating in a variety of business contexts, ranging from automated bidding in online auctions to pricing on shopping platforms and setting rents. This diffusion has been accompanied by fears that such automation could facilitate collusion. A number of recent papers on algorithmic pricing show in simulations that learning algorithms coordinate on less-than-competitive outcomes.\",\"PeriodicalId\":210555,\"journal\":{\"name\":\"Proceedings of the 24th ACM Conference on Economics and Computation\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 24th ACM Conference on Economics and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3580507.3597726\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th ACM Conference on Economics and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3580507.3597726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning algorithms are proliferating in a variety of business contexts, ranging from automated bidding in online auctions to pricing on shopping platforms and setting rents. This diffusion has been accompanied by fears that such automation could facilitate collusion. A number of recent papers on algorithmic pricing show in simulations that learning algorithms coordinate on less-than-competitive outcomes.
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