Reputation-Based Resource Allocation in P2P Systems: A Game Theoretic Perspective

Goswami, Antriksh; Gupta, Ruchir; Parashari, Gopal Sharan

doi:10.1109/lcomm.2017.2675900

Cited by 31 publications

(17 citation statements)

References 9 publications

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“…In particular, some personal characteristics appear to catalyze reputation social influence (i.e., favoring the decision-making biases based on reputation), while others act in the opposite direction as "protective" factors. Our results can be useful to design web-based services and platforms, in particular in those cases in which reputation biases would represent a factor reducing the system's effectiveness and robustness [55][56][57][58]. Table 7.…”

Section: Discussionmentioning

confidence: 99%

Personality and Reputation: A Complex Relationship in Virtual Environments

et al. 2018

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Online reputational systems are nowadays widely and effectively adopted by several online platforms to support and improve peoples’ interactions and communication. Despite the research approached and modeled social dynamics of reputational systems in different domains, adopting different frameworks, the role played by psycho-social factors, and personality traits, determining the individual susceptibility to online reputation is still elusive. To study such mediation effects, we implemented a modified online version of the Ultimatum Game, in which participants (215 adolescents) played before as proposers, and then as responders, always knowing the reputation of their interactors. Furthermore, after the reception phase, participants could evaluate the received offers, giving positive or negative feedback to their proposers. Despite the participants’ belief they were playing with their schoolmates, the interactors’ role was always fulfilled by bots characterized by standardized behaviors. Our results show how psychological traits influence the participants’ behavior in all the game phases, as well as in the rating dynamics. Reputation seems to have a direct effect only in the allocation behavior, while, in regards the other dynamics of the game (i.e., acceptance and rating), it comes into play in a complex interaction with the psychological dimensions.

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Section: Discussionmentioning

confidence: 99%

Personality and Reputation: A Complex Relationship in Virtual Environments

et al. 2018

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“…to denote the entire sensitivity profile of the 2 agents. Consequently, agents' decision behaviors (6) with the loss-aversion-based sensitivity (7) and the quadratic payoff functions (2) under mode k ∈ K {LL, HL, LH, HH} are described aṡ…”

Section: B Loss-aversion-based Myopic Pseudo-gradient Dynamicsmentioning

confidence: 99%

“…Then, the rotational directions of the trajectories are consistently the same in the entire state space R 2 . Specifically, if a 1 12 < 0 and a 2 12 > 0 (resp., a 1 12 > 0 and a 2 12 < 0), then the trajectory of (6), 7, keeps counterclockwise (resp., clockwise) direction for any…”

Section: Assumptionmentioning

confidence: 99%

Stability Analysis of Nash Equilibrium in Loss-Aversion-Based Noncooperative Dynamical Systems

Yan

Hayakawa

Thanomvajamun

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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The stability property of the loss-aversion-based noncooperative switched systems with quadratic payoffs is investigated. In this system, each agent adopts the lower sensitivity parameter in the myopic pseudo-gradient dynamics for the case of losing utility than gaining utility, and both systems' dynamics and switching events (conditions) are depending on agents' payoff functions. Sufficient conditions under which agents' state converges towards the Nash equilibrium are derived in accordance with the location of the Nash equilibrium. In the analysis, the mode transition sequence and interesting phenomena which we call flash switching instants are characterized. Finally, we present several numerical examples to illustrate the properties of our results.

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“…Gupta et al (2015) described a scheme of reputation-based probabilistic resource allocation for avoiding free riding in unstructured P2P networks. Goswami et al (2017) considered reputation-based resource allocation in P2P networks and analyzed a resource allocation mechanism by using two non-cooperative games: the demand game and the reputation game.…”

Section: Introductionmentioning

confidence: 99%

Utility optimization–based bandwidth allocation for elastic and inelastic services in peer–to–peer networks

Zhang

Wang

et al. 2019

International Journal of Applied Mathematics and Computer Science

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This paper considers reasonable bandwidth allocation for multiclass services in peer-to-peer (P2P) networks, measures the satisfaction of each peer as a customer by a utility function when acquiring one service, and develops an optimization model for bandwidth allocation with the objective of utility maximization. Elastic services with concave utilities are first considered and the exact expression of optimal bandwidth allocation for each peer is deduced. In order to obtain an optimum in distributed P2P networks, we develop a gradient-based bandwidth allocation scheme and illustrate the performance with numerical examples. Then we investigate bandwidth allocation for inelastic services with sigmoidal utilities, which is a nonconvex optimization problem. In order to solve it, we analyze provider capacity provisioning for bandwidth allocation of inelastic services and modify the update rule for prices that service customers should pay. Numerical examples are finally given to illustrate that the improved scheme can also efficiently converge to the global optimum.

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Reputation-Based Resource Allocation in P2P Systems: A Game Theoretic Perspective

Cited by 31 publications

References 9 publications

Personality and Reputation: A Complex Relationship in Virtual Environments

Personality and Reputation: A Complex Relationship in Virtual Environments

Stability Analysis of Nash Equilibrium in Loss-Aversion-Based Noncooperative Dynamical Systems

Utility optimization–based bandwidth allocation for elastic and inelastic services in peer–to–peer networks

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