@misc{BiloFriedrichLenzneretal.2019,
  author    = {Bilo, Davide and Friedrich, Tobias and Lenzner, Pascal and Melnichenko, Anna},
  title     = {Geometric Network Creation Games},
  series = {SPAA '19: The 31st ACM Symposium on Parallelism in Algorithms and Architectures},
  journal   = {SPAA '19: The 31st ACM Symposium on Parallelism in Algorithms and Architectures},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-6184-2},
  doi       = {10.1145/3323165.3323199},
  pages     = {323 -- 332},
  year      = {2019},
  abstract  = {Network Creation Games are a well-known approach for explaining and analyzing the structure, quality and dynamics of real-world networks like the Internet and other infrastructure networks which evolved via the interaction of selfish agents without a central authority. In these games selfish agents which correspond to nodes in a network strategically buy incident edges to improve their centrality. However, past research on these games has only considered the creation of networks with unit-weight edges. In practice, e.g. when constructing a fiber-optic network, the choice of which nodes to connect and also the induced price for a link crucially depends on the distance between the involved nodes and such settings can be modeled via edge-weighted graphs. We incorporate arbitrary edge weights by generalizing the well-known model by Fabrikant et al. [PODC'03] to edge-weighted host graphs and focus on the geometric setting where the weights are induced by the distances in some metric space. In stark contrast to the state-of-the-art for the unit-weight version, where the Price of Anarchy is conjectured to be constant and where resolving this is a major open problem, we prove a tight non-constant bound on the Price of Anarchy for the metric version and a slightly weaker upper bound for the non-metric case. Moreover, we analyze the existence of equilibria, the computational hardness and the game dynamics for several natural metrics. The model we propose can be seen as the game-theoretic analogue of a variant of the classical Network Design Problem. Thus, low-cost equilibria of our game correspond to decentralized and stable approximations of the optimum network design.},
  language  = {en}
}
@article{BruecknerKanzowScheffer2012,
  author    = {Br{\"u}ckner, Michael and Kanzow, Christian and Scheffer, Tobias},
  title     = {Static prediction games for adversarial learning problems},
  series = {Journal of machine learning research},
  volume    = {13},
  journal   = {Journal of machine learning research},
  publisher = {Microtome Publishing},
  address   = {Cambridge, Mass.},
  issn      = {1532-4435},
  pages     = {2617 -- 2654},
  year      = {2012},
  abstract  = {The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering.},
  language  = {en}
}