Tristan Pollner, M. Roghani, A. Saberi, David Wajc
{"title":"改进了匹配和零工经济应用的在线争用解决方案","authors":"Tristan Pollner, M. Roghani, A. Saberi, David Wajc","doi":"10.1145/3490486.3538295","DOIUrl":null,"url":null,"abstract":"Motivated by applications in the gig economy, we study approximation algorithms for a sequential pricing problem. The input is a bipartite graph [Formula: see text] between individuals I and jobs J. The platform has a value of vj for matching job j to an individual worker. In order to find a matching, the platform can consider the edges [Formula: see text] in any order and make a one-time take-it-or-leave-it offer of a price [Formula: see text] of its choosing to i for completing j. The worker accepts the offer with a known probability pijw; in this case, the job and the worker are irrevocably matched. What is the best way to make offers to maximize revenue and/or social welfare? The optimal algorithm is known to be NP-hard to compute (even if there is only a single job). With this in mind, we design efficient approximations to the optimal policy via a new random-order online contention resolution scheme (RO-OCRS) for matching. Our main result is a 0.456-balanced RO-OCRS in bipartite graphs and a 0.45-balanced RO-OCRS in general graphs. These algorithms improve on the recent bound of [Formula: see text] and improve on the best-known lower bounds for the correlation gap of matching, despite applying to a significantly more restrictive setting. As a consequence of our online contention resolution scheme results, we obtain a 0.456-approximate algorithm for the sequential pricing problem. We further extend our results to settings where workers can only be contacted a limited number of times and show how to achieve improved results for this problem via improved algorithms for the well-studied stochastic probing problem. Funding: This work was supported by the National Science Foundation [Grant CCF2209520] and a gift from Amazon Research.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Improved Online Contention Resolution for Matchings and Applications to the Gig Economy\",\"authors\":\"Tristan Pollner, M. Roghani, A. Saberi, David Wajc\",\"doi\":\"10.1145/3490486.3538295\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by applications in the gig economy, we study approximation algorithms for a sequential pricing problem. The input is a bipartite graph [Formula: see text] between individuals I and jobs J. The platform has a value of vj for matching job j to an individual worker. In order to find a matching, the platform can consider the edges [Formula: see text] in any order and make a one-time take-it-or-leave-it offer of a price [Formula: see text] of its choosing to i for completing j. The worker accepts the offer with a known probability pijw; in this case, the job and the worker are irrevocably matched. What is the best way to make offers to maximize revenue and/or social welfare? The optimal algorithm is known to be NP-hard to compute (even if there is only a single job). With this in mind, we design efficient approximations to the optimal policy via a new random-order online contention resolution scheme (RO-OCRS) for matching. Our main result is a 0.456-balanced RO-OCRS in bipartite graphs and a 0.45-balanced RO-OCRS in general graphs. These algorithms improve on the recent bound of [Formula: see text] and improve on the best-known lower bounds for the correlation gap of matching, despite applying to a significantly more restrictive setting. As a consequence of our online contention resolution scheme results, we obtain a 0.456-approximate algorithm for the sequential pricing problem. We further extend our results to settings where workers can only be contacted a limited number of times and show how to achieve improved results for this problem via improved algorithms for the well-studied stochastic probing problem. Funding: This work was supported by the National Science Foundation [Grant CCF2209520] and a gift from Amazon Research.\",\"PeriodicalId\":209859,\"journal\":{\"name\":\"Proceedings of the 23rd ACM Conference on Economics and Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 23rd ACM Conference on Economics and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3490486.3538295\",\"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 23rd ACM Conference on Economics and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3490486.3538295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Online Contention Resolution for Matchings and Applications to the Gig Economy
Motivated by applications in the gig economy, we study approximation algorithms for a sequential pricing problem. The input is a bipartite graph [Formula: see text] between individuals I and jobs J. The platform has a value of vj for matching job j to an individual worker. In order to find a matching, the platform can consider the edges [Formula: see text] in any order and make a one-time take-it-or-leave-it offer of a price [Formula: see text] of its choosing to i for completing j. The worker accepts the offer with a known probability pijw; in this case, the job and the worker are irrevocably matched. What is the best way to make offers to maximize revenue and/or social welfare? The optimal algorithm is known to be NP-hard to compute (even if there is only a single job). With this in mind, we design efficient approximations to the optimal policy via a new random-order online contention resolution scheme (RO-OCRS) for matching. Our main result is a 0.456-balanced RO-OCRS in bipartite graphs and a 0.45-balanced RO-OCRS in general graphs. These algorithms improve on the recent bound of [Formula: see text] and improve on the best-known lower bounds for the correlation gap of matching, despite applying to a significantly more restrictive setting. As a consequence of our online contention resolution scheme results, we obtain a 0.456-approximate algorithm for the sequential pricing problem. We further extend our results to settings where workers can only be contacted a limited number of times and show how to achieve improved results for this problem via improved algorithms for the well-studied stochastic probing problem. Funding: This work was supported by the National Science Foundation [Grant CCF2209520] and a gift from Amazon Research.