Learning user preferences in mechanism design

Chorppath, Anil Kumar; Alpcan, Tansu

doi:10.1109/cdc.2011.6160642

Cited by 15 publications

(9 citation statements)

References 10 publications

(11 reference statements)

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“…In [12], the authors reduced mechanism design problems to standard algorithmic problems using techniques from sample complexity. In [19], Gaussian regression learning was used to estimate the marginal utilities of the users in a network mechanism design setting by the designer. We follow a similar learning approach here where malicious user learns the selfish user utility functions.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

Adversarial Behavior in Network Games

2014

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“…Remark 3: Obtaining the knowledge of utility functions of consumers is a well studied in [22]) and references therein. However, it is not the main focus of this paper, so, we have assumed that the knowledge of utility functions is already available.…”

Section: Demand Response At Consumer Levelmentioning

confidence: 99%

Game-Theoretic Frameworks for Demand Response in Electricity Markets

Nekouei

Alpcan

Chattopadhyay

2015

IEEE Trans. Smart Grid

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132

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This paper presents game-theoretic frameworks for demand response at both electricity market and consumer levels. First, the interaction between a demand response aggregator (DRA) and electricity generators is modeled as a Stackelberg game in which the DRA, as the leader of the game, makes demand reduction bids, and generators, as followers, compete for maximizing their profits based on the reduced demand. Next, the interaction between the DRA and consumers is modeled as a mechanism design problem wherein the DRA seeks to minimize the aggregate inconvenience of consumers while achieving the targeted load curtailment. The inconvenience function of each consumer is captured by a type value, which is used by the DRA to solve the load curtailment problem. A Vickrey-Clarke-Groves-based mechanism is proposed, which guarantees that each consumer reveals its true type value to the DRA. A case study of the Stackelberg game shows that, in the South Australian electricity market where there is significant renewable penetration, peak period demand response provides the maximum potential profit, but off-peak demand response even in a concentrated market is not financially attractive.

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“…A generalized representer theorem given in [14][15][16] is used to estimate the functions 1 and 2 in Eq. (16) and Eq.…”

Section: B Estimation Of Users' Objectives By Representer Theoremmentioning

confidence: 99%

“…(16) and Eq. (17) via experiences of the prices SWN sent to VWNs and their responses to those prices.…”

Section: B Estimation Of Users' Objectives By Representer Theoremmentioning

confidence: 99%

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Game-theory based power and spectrum virtualization for maximizing spectrum efficiency over mobile cloud-computing wireless networks

Zhu

Zhang

2015

2015 49th Annual Conference on Information Sciences and Systems (CISS)

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Mobile cloud-computing is a wireless network environment that focuses on sharing the publicly available wireless resources. Wireless network virtualization provides an efficient technique to implement the mobile cloud-computing by enabling multiple virtual wireless networks to be mapped onto one physical substrate wireless network. One of the most important challenges of this technique lies in how to efficiently allocate the wireless resources of physical wireless networks to the multiple virtual wireless network users. To overcome these difficulties, in this paper we propose the game-theory based schemes to resolve the wireless resources allocation problem in terms of transmit power and wireless spectrum. We formulate this wireless resources allocation problem as the gaming process where each mobile user bids for the limited wireless resources from physical substrate wireless networks, and competes with the other mobile-user players bidding for the same resources. Regulating the mobile users' bidding aggressiveness, our proposed game is guaranteed to converge to the Nash Equilibrium, where the benefits of physical substrate wireless network and the virtual wireless networks are both optimized, maximizing the aggregate spectrum efficiencies for our resources-virtualized mobile cloud-computing wireless networks. The extensive simulation results obtained validate and evaluate our proposed schemes.Index Terms-Wireless network virtualization, mobile cloudcomputing, power and spectrum allocation, game theory, spectrum efficiency maximization.978-1-4799-8428-2/15/$31.00 ©2015 IEEE

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Learning user preferences in mechanism design

Cited by 15 publications

References 10 publications

Adversarial Behavior in Network Games

Adversarial Behavior in Network Games

Game-Theoretic Frameworks for Demand Response in Electricity Markets

Game-theory based power and spectrum virtualization for maximizing spectrum efficiency over mobile cloud-computing wireless networks

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