@misc{SerthPodlesnyBornsteinetal.2017,
  author    = {Serth, Sebastian and Podlesny, Nikolai and Bornstein, Marvin and Lindemann, Jan and Latt, Johanna and Selke, Jan and Schlosser, Rainer and Boissier, Martin and Uflacker, Matthias},
  title     = {An interactive platform to simulate dynamic pricing competition on online marketplaces},
  series = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)},
  journal   = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {New York},
  isbn      = {978-1-5090-3045-3},
  issn      = {2325-6354},
  doi       = {10.1109/EDOC.2017.17},
  pages     = {61 -- 66},
  year      = {2017},
  abstract  = {E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.},
  language  = {en}
}