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Strategic investment in protection in networked systems

Published online by Cambridge University Press:  03 April 2017

MATT V. LEDUC  and
RUSLAN MOMOT
MATT V. LEDUC
Affiliation:
Stanford University, MS&E, 475 Via Ortega, Stanford, CA 94305-4121, USA and IIASA, Schlossplatz 1, A-2361 Laxenburg, Austria (e-mail: mattvleduc@gmail.com)
RUSLAN MOMOT
Affiliation:
INSEAD, Boulevard de Constance, 77305 Fontainebleau, France (e-mail: ruslan.momot@insead.edu)
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Abstract

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We study the incentives that agents have to invest in costly protection against cascading failures in networked systems. Applications include vaccination, computer security, and airport security. Agents are connected through a network and can fail either intrinsically or as a result of the failure of a subset of their neighbors. We characterize the equilibrium based on an agent's failure probability and derive conditions under which equilibrium strategies are monotone in degree (i.e. in how connected an agent is on the network). We show that different kinds of applications (e.g. vaccination, malware, airport/EU security) lead to very different equilibrium patterns of investments in protection, with important welfare and risk implications. Our equilibrium concept is flexible enough to allow for comparative statics in terms of network properties, and we show that it is also robust to the introduction of global externalities (e.g. price feedback, congestion).

Keywords

network economicsnetwork gameslocal vs global externalitiescascading failuressystemic riskimmunizationairport securitycomputer security
Type
Research Article
Information
Network Science , Volume 5 , Issue 1 , March 2017 , pp. 108 - 139
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2017

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