title: Optimal Stopping in the Balls-and-Bins Problem
metadata.name: dc.contributor.author
metadata.value: Ivashko, Anna A.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-11-26T19:17:52Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-11-26T19:17:52Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-10
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Ivashko A. A. Optimal Stopping in the Balls-and-Bins Problem. Contributions to Game Theory and Management, XIV, 183-191.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu31.2021.14
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/33696
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This paper considers a multistage balls-and-bins problem with
  optimal stopping connected with the job allocation model. There are N
  steps. The player drops balls (tasks) randomly one at a time into available
  bins (servers). The game begins with only one empty bin. At each step, a
  new bin can appear with probability p. At step n (n = 1, . . . ,N), the player
  can choose to stop and receive the payoff or continue the process and move
  to the next step. If the player stops, then he/she gets 1 for every bin with
  exactly one ball and loses 1/2 for every bin with two or more balls. Empty
  bins do not count. At the last step, the player must stop the process. The
  player's aim is to find the stopping rule which maximizes the expected payoff.
  The optimal payoff at each step are calculated. An approximate strategy
  depending on the number of steps is proposed. It is demonstrated that the
  payo  when using this strategy is close to the optimal payoff.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The study was carried out under state order to the Karelian Research Centre of the
  Russian Academy of Sciences (Institute of Applied Mathematical Research KarRC RAS)
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Contributions to Game Theory and Management;Volume 14
metadata.lang: -
metadata.name: dc.subject
metadata.value: optimal stopping
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: job allocation
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: balls-and-bins problem
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Optimal Stopping in the Balls-and-Bins Problem
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 183-191.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/33696/1/183-191.pdf
resources.format: Adobe PDF
resources.size: 516,56 kB