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Efficient Best Response Computation for Strategic Network Formation Under Attack

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10504))

Abstract

Inspired by real world examples, e.g. the Internet, researchers have introduced an abundance of strategic games to study natural phenomena in networks. Unfortunately, almost all of these games have the conceptual drawback of being computationally intractable, i.e. computing a best response strategy or checking if an equilibrium is reached is NP-hard. Thus, a main challenge in the field is to find tractable realistic network formation models. We address this challenge by investigating a very recently introduced model by Goyal et al. [14] which focuses on robust networks in the presence of a strong adversary who attacks (and kills) nodes in the network and lets this attack spread virus-like through the network via neighboring nodes.

Our main result is to establish that this natural model is one of the few exceptions which are both realistic and computationally tractable. In particular, we answer an open question of Goyal et al. by providing an efficient algorithm for computing a best response strategy, which implies that deciding whether the game has reached a Nash equilibrium can be done efficiently as well. Our algorithm essentially solves the problem of computing a minimal connection to a network which maximizes the reachability while hedging against severe attacks on the network infrastructure and may thus be of independent interest.

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Notes

  1. 1.

    This question was raised in [13] for the maximum carnage adversary and is replaced in [14] with a reference to our preprint [12] of the present paper.

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Correspondence to Pascal Lenzner .

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Friedrich, T., Ihde, S., Keßler, C., Lenzner, P., Neubert, S., Schumann, D. (2017). Efficient Best Response Computation for Strategic Network Formation Under Attack. In: Bilò, V., Flammini, M. (eds) Algorithmic Game Theory. SAGT 2017. Lecture Notes in Computer Science(), vol 10504. Springer, Cham. https://doi.org/10.1007/978-3-319-66700-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-66700-3_16

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