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Game‐theoretic computing in risk analysis

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Abstract Risk analysis, comprising risk assessment and risk management stages, is one of the most popular and challenging topics of our times because security and privacy, and availability and usability culminating at the trustworthiness of cybersystems and cyber information is at stake. The precautionary need derives from the existence of defenders versus adversaries, in an everlasting Darwinian scenario dating back to early human history of warriors fighting for their sustenance to survive. Fast forwarding to today's information warfare, whether in networks or healthcare or national security, the currently dire situation necessitates more than a hand calculator to optimize (maximize gains or minimize losses) risk due to prevailing scarce economic resources. This article reviews the previous works completed on this specialized topic of game‐theoretic computing, its methods and applications toward the purpose of quantitative risk assessment and cost‐optimal management in many diverse disciplines including entire range of informatics‐related topics. Additionally, this review considers certain game‐theoretic topics in depth historically, and those computationally resourceful such as Neumann's two‐way zero‐sum pure equilibrium and optimal mixed strategy solutions versus Nash equilibria with pure and mixed strategies. Computational examples are provided to highlight the significance of game‐theoretic solutions used in risk assessment and management, particularly in reference to cybersystems and information security. WIREs Comput Stat 2012, 4:227–248. doi: 10.1002/wics.1205 This article is categorized under: Algorithms and Computational Methods > Linear Programming Algorithms and Computational Methods > Networks and Security

The Defend–Attack sequential decision game by Rios, Rios, and Banks, and influence diagram (a), and decision tree (b) representations.

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An example for social network privacy/security risk‐o‐meter tree diagram using RoM.

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General purpose tree diagram (V‐branches, T‐twigs, LCM‐limbs) for the RoM model.

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Risk‐O‐Meter Model of probabilistic, deterministic inputs, and calculated outputs.

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Auction analysis from Daphne's and Apollo's bidding perspectives.

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ID of the sealed bid auction problem.

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Algorithms and Computational Methods > Networks and Security
Algorithms and Computational Methods > Linear Programming

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