BibTeX Export for Mean Field Analysis of an Incentive Algorithm for a Closed Stochastic Network

Copy to Clipboard Download

@InProceedings{moreno_et_al:LIPIcs.AofA.2022.13,
  author =	{Moreno, Bianca Marin and Fricker, Christine and Mohamed, Hanene and Philippe, Amaury and Tr\'{e}panier, Martin},
  title =	{{Mean Field Analysis of an Incentive Algorithm for a Closed Stochastic Network}},
  booktitle =	{33rd International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2022)},
  pages =	{13:1--13:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-230-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{225},
  editor =	{Ward, Mark Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.AofA.2022.13},
  URN =		{urn:nbn:de:0030-drops-160998},
  doi =		{10.4230/LIPIcs.AofA.2022.13},
  annote =	{Keywords: Large scale analysis, mean-field, car-sharing, incentive algorithm, stochastic network, cluster, load balancing, closed Jackson networks, product-form distribution}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode