{"title":"具有个体和集体强化的相互作用随机系统的同步和波动","authors":"Pierre-Yves Louis, Meghdad Mirebrahimi","doi":"10.1080/15326349.2023.2267663","DOIUrl":null,"url":null,"abstract":"AbstractThe Pólya urn is the most representative example of a reinforced stochastic process. It leads to a random (non degenerated) time-limit. The Friedman urn is a natural generalization whose almost sure (a.s.) time-limit is not random any more. In this work, in the stream of previous recent works, we introduce a new family of (finite size) systems of reinforced stochastic processes, interacting through an additional collective reinforcement of mean field type. The two reinforcement rules strengths (one component-wise, one collective) are tuned through (possibly) two different rates. In special cases, these reinforcements are of Pólya or Friedman type as in urn contexts and may thus lead to limits which may be random or not. Different parameter regimes need to be considered. We state two kind of results. First, we study the time-asymptotic and show that L2 and a.s. convergence always holds. Moreover, all the components share the same time-limit (so called synchronization phenomenon). We study the nature of the limit (random/deterministic) according to the parameters’ regime considered. Second, we study fluctuations by proving central limit theorems. Scaling coefficients vary according to the regime considered. This gives insights into many different rates of convergence. In particular, we identify the regimes where synchronization is faster than convergence toward the shared time-limit.Keywords: Almost sure convergencecentral limit theoremsfluctuationsinteracting random systemsreinforced stochastic processesstable convergencesynchronization2010 Mathematics Subject Classification: Primary 60K35Primary 60F1560F05Secondary 62L20Secondary 62P35 AcknowledgmentsI would like to thank Professor Pierre-Yves Louis for introducing me to the problem and for all the useful comments and discussions. I am also very grateful for extremely constructive feedback from the referee.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":21970,"journal":{"name":"Stochastic Models","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Synchronization and fluctuations for interacting stochastic systems with individual and collective reinforcement\",\"authors\":\"Pierre-Yves Louis, Meghdad Mirebrahimi\",\"doi\":\"10.1080/15326349.2023.2267663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractThe Pólya urn is the most representative example of a reinforced stochastic process. It leads to a random (non degenerated) time-limit. The Friedman urn is a natural generalization whose almost sure (a.s.) time-limit is not random any more. In this work, in the stream of previous recent works, we introduce a new family of (finite size) systems of reinforced stochastic processes, interacting through an additional collective reinforcement of mean field type. The two reinforcement rules strengths (one component-wise, one collective) are tuned through (possibly) two different rates. In special cases, these reinforcements are of Pólya or Friedman type as in urn contexts and may thus lead to limits which may be random or not. Different parameter regimes need to be considered. We state two kind of results. First, we study the time-asymptotic and show that L2 and a.s. convergence always holds. Moreover, all the components share the same time-limit (so called synchronization phenomenon). We study the nature of the limit (random/deterministic) according to the parameters’ regime considered. Second, we study fluctuations by proving central limit theorems. Scaling coefficients vary according to the regime considered. This gives insights into many different rates of convergence. In particular, we identify the regimes where synchronization is faster than convergence toward the shared time-limit.Keywords: Almost sure convergencecentral limit theoremsfluctuationsinteracting random systemsreinforced stochastic processesstable convergencesynchronization2010 Mathematics Subject Classification: Primary 60K35Primary 60F1560F05Secondary 62L20Secondary 62P35 AcknowledgmentsI would like to thank Professor Pierre-Yves Louis for introducing me to the problem and for all the useful comments and discussions. I am also very grateful for extremely constructive feedback from the referee.Disclosure statementNo potential conflict of interest was reported by the author(s).\",\"PeriodicalId\":21970,\"journal\":{\"name\":\"Stochastic Models\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Models\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15326349.2023.2267663\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Models","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15326349.2023.2267663","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Synchronization and fluctuations for interacting stochastic systems with individual and collective reinforcement
AbstractThe Pólya urn is the most representative example of a reinforced stochastic process. It leads to a random (non degenerated) time-limit. The Friedman urn is a natural generalization whose almost sure (a.s.) time-limit is not random any more. In this work, in the stream of previous recent works, we introduce a new family of (finite size) systems of reinforced stochastic processes, interacting through an additional collective reinforcement of mean field type. The two reinforcement rules strengths (one component-wise, one collective) are tuned through (possibly) two different rates. In special cases, these reinforcements are of Pólya or Friedman type as in urn contexts and may thus lead to limits which may be random or not. Different parameter regimes need to be considered. We state two kind of results. First, we study the time-asymptotic and show that L2 and a.s. convergence always holds. Moreover, all the components share the same time-limit (so called synchronization phenomenon). We study the nature of the limit (random/deterministic) according to the parameters’ regime considered. Second, we study fluctuations by proving central limit theorems. Scaling coefficients vary according to the regime considered. This gives insights into many different rates of convergence. In particular, we identify the regimes where synchronization is faster than convergence toward the shared time-limit.Keywords: Almost sure convergencecentral limit theoremsfluctuationsinteracting random systemsreinforced stochastic processesstable convergencesynchronization2010 Mathematics Subject Classification: Primary 60K35Primary 60F1560F05Secondary 62L20Secondary 62P35 AcknowledgmentsI would like to thank Professor Pierre-Yves Louis for introducing me to the problem and for all the useful comments and discussions. I am also very grateful for extremely constructive feedback from the referee.Disclosure statementNo potential conflict of interest was reported by the author(s).
期刊介绍:
Stochastic Models publishes papers discussing the theory and applications of probability as they arise in the modeling of phenomena in the natural sciences, social sciences and technology. It presents novel contributions to mathematical theory, using structural, analytical, algorithmic or experimental approaches. In an interdisciplinary context, it discusses practical applications of stochastic models to diverse areas such as biology, computer science, telecommunications modeling, inventories and dams, reliability, storage, queueing theory, mathematical finance and operations research.