TY - GEN
T1 - Strategic network formation with attack and immunization
AU - Goyal, Sanjeev
AU - Jabbari, Shahin
AU - Kearns, Michael
AU - Khanna, Sanjeev
AU - Morgenstern, Jamie
N1 - Publisher Copyright:
© Springer-Verlag GmbH Germany 2016.
PY - 2016
Y1 - 2016
N2 - Strategic network formation arises in settings where agents receive some benefit from their connectedness to other agents, but also incur costs for forming these links. We consider a new network formation game that incorporates an adversarial attack, as well as immunization or protection against the attack. An agent’s network benefit is the expected size of her connected component post-attack, and agents may also choose to immunize themselves from attack at some additional cost. Our framework can be viewed as a stylized model of settings where reachability rather than centrality is the primary interest (as in many technological networks such as the Internet), and vertices may be vulnerable to attacks (such as viruses), but may also reduce risk via potentially costly measures (such as an anti-virus software). Our main theoretical contributions include a strong bound on the edge density at equilibrium. In particular, we show that under a very mild assumption on the adversary’s attack model, every equilibrium network contains at most only 2n−4 edges for n ≥ 4, where n denotes the number of agents and this upper bound is tight. We also show that social welfare does not significantly erode: every non-trivial equilibrium with respect to several adversarial attack models asymptotically has social welfare at least as that of any equilibrium in the original attack-free model. We complement our sharp theoretical results by a behavioral experiment on our game with over 100 participants, where despite the complexity of the game, the resulting network was surprisingly close to equilibrium.
AB - Strategic network formation arises in settings where agents receive some benefit from their connectedness to other agents, but also incur costs for forming these links. We consider a new network formation game that incorporates an adversarial attack, as well as immunization or protection against the attack. An agent’s network benefit is the expected size of her connected component post-attack, and agents may also choose to immunize themselves from attack at some additional cost. Our framework can be viewed as a stylized model of settings where reachability rather than centrality is the primary interest (as in many technological networks such as the Internet), and vertices may be vulnerable to attacks (such as viruses), but may also reduce risk via potentially costly measures (such as an anti-virus software). Our main theoretical contributions include a strong bound on the edge density at equilibrium. In particular, we show that under a very mild assumption on the adversary’s attack model, every equilibrium network contains at most only 2n−4 edges for n ≥ 4, where n denotes the number of agents and this upper bound is tight. We also show that social welfare does not significantly erode: every non-trivial equilibrium with respect to several adversarial attack models asymptotically has social welfare at least as that of any equilibrium in the original attack-free model. We complement our sharp theoretical results by a behavioral experiment on our game with over 100 participants, where despite the complexity of the game, the resulting network was surprisingly close to equilibrium.
UR - http://www.scopus.com/inward/record.url?scp=85007374104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007374104&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-54110-4_30
DO - 10.1007/978-3-662-54110-4_30
M3 - Conference contribution
AN - SCOPUS:85007374104
SN - 9783662541098
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 429
EP - 443
BT - Web and Internet Economics - 12th International Conference, WINE 2016, Proceedings
A2 - Vetta, Adrian
A2 - Cai, Yang
PB - Springer Verlag
T2 - 12th International Conference on Web and Internet Economics, WINE 2016
Y2 - 11 June 2016 through 14 July 2016
ER -