{"title":"Algorithm for equilibrium in the symmetric two-player Hirshleifer contests","authors":"Boróka Olteán-Péter, Csaba Farkas","doi":"10.1109/SACI58269.2023.10158666","DOIUrl":null,"url":null,"abstract":"In this work we study equilibrium points in the symmetric two-player Hirshleifer contests, which are going to be called Hirshleifer equilibrium point. We extend the Quasi-Newton method to the setting of this type of equilibrium point. The numerical algorithm is developed for multiobjective optimization problems based on the properties of Hirshleifer equilibrium point. Some preliminary numerical results are reported showing the performance of our algorithms.We present a possible way for converting single objective optimization to multiobjective optimization which is discussed in detail in our study.","PeriodicalId":339156,"journal":{"name":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI58269.2023.10158666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this work we study equilibrium points in the symmetric two-player Hirshleifer contests, which are going to be called Hirshleifer equilibrium point. We extend the Quasi-Newton method to the setting of this type of equilibrium point. The numerical algorithm is developed for multiobjective optimization problems based on the properties of Hirshleifer equilibrium point. Some preliminary numerical results are reported showing the performance of our algorithms.We present a possible way for converting single objective optimization to multiobjective optimization which is discussed in detail in our study.