Games and mechanisms for networked systems: incentives and algorithms

Chorppath, Anil Kumar; Alpcan, Tansu; Boche, Holger

doi:10.1017/cbo9781139524421.003

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…In the indirect mechanisms, instead of reporting their utility function to the designer, the players take a best response to the actions of other players and to the allocation and pricing rules set by the designer. Therefore, the allocation and pricing rules are not a function of utility functions unlike direct VCG mechanism, but rather fixed functions of the player strategies [21]. We consider indirect auction mechanisms with scalar bid here since they have only one dimensional communication requirement which is suitable for network resource allocation.…”

Section: Price Of Malice In Indirect Auction Mechanismsmentioning

confidence: 99%

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Adversarial Behavior in Network Games

2014

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This paper studies the effects of and countermeasures against adversarial behavior in network resource allocation mechanisms such as auctions and pricing schemes. It models the heterogeneous behavior of users, which ranges from altruistic to selfish and to malicious, within the analytical framework of game theory. A mechanism design approach is adopted to quantify the effect of adversarial behavior, which ranges from extreme selfishness to destructive maliciousness. First, the well-known result on the Vicrey-Clarke-Groves (VCG) mechanism losing its efficiency property in the presence of malicious users is extended to the case of divisible resource allocation to motivate the need to quantify the effect of malicious behavior. Then, the Price of Malice of the VCG mechanism and of some other network mechanisms are derived. In this context, the dynamics and convergence properties of an iterative distributed pricing algorithm are analyzed. The resistance of a mechanism to collusions is investigated next, and the effect of collusion of some malicious users is quantified. Subsequently, the assumption that the malicious user has information about the utility function of selfish users is relaxed, and a regression-based iterative learning scheme is presented and applied to both pricing and auction mechanisms. Differentiated pricing as a method to counter adversarial behaviors is proposed and briefly discussed. The results obtained are illustrated with numerical examples and simulations.

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Section: Price Of Malice In Indirect Auction Mechanismsmentioning

confidence: 99%

“…Pricing mechanisms [21] do not have explicit allocation rule. Their actions reveal only some information about their utility function.…”

Section: Price Of Malice In Pricing Mechanismsmentioning

confidence: 99%

Adversarial Behavior in Network Games

2014

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“…In this paper, we model the coexistence of selfish and malicious players by introducing an overarching noncooperative game-theoretic framework. Specifically, we adopt a pricing mechanism approach in which a set of rules and incentives [9] are used to control the outcome of the underlying game between the players. The malicious user submits QoS requirement like the regular users in order not to get detected but computes the best response power strategies for a modified utility function with the goal to harm the other links and cause interference to others.…”

Section: Introductionmentioning

confidence: 99%

Pricing for distributed resource allocation in MAC without SIC under QoS requirements with malicious users

Shen

Jorswieck

Chorppath

et al. 2014

2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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We develop the noncooperative game with individual pricing for the general multiple access channel (MAC) system without successive interference cancellation (SIC). Each user allocates its own power by optimizing the individual utility function with clever price adaptation. We show that by the proposed prices, the best response (BR) power allocation of each user converges rapidly. The individual prices are proposed such that the Shannon rate-based quality-of-service (QoS) requirement of each user is achieved at the unique Nash equilibrium (NE) point. We analyse different behavior types of the users, especially the malicious behavior and the resulting NE power allocation and achievable rates of all the users with malicious users. We illustrate the convergence of the BR dynamic and the Price of Malice (PoM) by numerical simulations.

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