{"title":"易于处理的二元竞赛","authors":"Priel Levy, David Sarne, Yonatan Aumann","doi":"10.1145/3630109","DOIUrl":null,"url":null,"abstract":"Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting enables the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest – when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.","PeriodicalId":42216,"journal":{"name":"ACM Transactions on Economics and Computation","volume":"12 1","pages":"0"},"PeriodicalIF":1.1000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tractable Binary Contests\",\"authors\":\"Priel Levy, David Sarne, Yonatan Aumann\",\"doi\":\"10.1145/3630109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting enables the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest – when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.\",\"PeriodicalId\":42216,\"journal\":{\"name\":\"ACM Transactions on Economics and Computation\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Economics and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3630109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Economics and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3630109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g. prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this paper we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting enables the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest – when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.
期刊介绍:
The ACM Transactions on Economics and Computation welcomes submissions of the highest quality that concern the intersection of computer science and economics. Of interest to the journal is any topic relevant to both economists and computer scientists, including but not limited to the following: Agents in networks Algorithmic game theory Computation of equilibria Computational social choice Cost of strategic behavior and cost of decentralization ("price of anarchy") Design and analysis of electronic markets Economics of computational advertising Electronic commerce Learning in games and markets Mechanism design Paid search auctions Privacy Recommendation / reputation / trust systems Systems resilient against malicious agents.