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Punishment or Reward: It Is a Problem in Anonymous, Dynamic and Autonomous Networking Environments

  • Yufeng Wang
  • Athanasios V. Vasilakos
  • Jianhua Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6905)

Abstract

Recently, service differentiation based incentive mechanisms are proposed in dynamic networking environments like Peer-to-Peer (P2P), wireless Ad hoc networks, etc.: define small meaningful classes of services and assign participants to these classes according to their overall resource contribution. Basically, there exist two fundamental components: How to define different classes? And who can assign autonomous participants to these classes? We argue that those environments are intrinsically anonymous, dynamic and autonomous, which has the following implications: Users can change their identities with near zero cost (cheap pseudonyms); most interactions in autonomous networks should be one-time (that is, each peer has no idea about other peers’ behavior history, except their current behaviors); and all behaviors and actions are all endogenous, voluntarily chosen and determined by independent and rational peers. Specifically, in the simplest case, service differentiation based incentive mechanisms could be provided with two ways: punish defect behavior (punishment-based scheme), or reward cooperative behavior (reward-based scheme). This paper preliminarily investigated the effectiveness of punishment-based and reward-based incentive mechanisms in anonymous, dynamic and autonomous environments. Our contributions are following: first, under the above networking environment, we found that the traditional service differentiation based incentive schemes could not work, irrespective of punishment-based and reward-based schemes; then, if peers can voluntarily join the system, and small entry fee is set for participation, we got that the performance of punishment-based scheme (first providing high-level service plus punishment) is always better than that of reward-based scheme (first providing low-level service plus reward).

Keywords

Incentive Scheme Incentive Mechanism Payoff Matrix Resource Provision Strategy Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yufeng Wang
    • 1
  • Athanasios V. Vasilakos
    • 2
  • Jianhua Ma
    • 3
  1. 1.Nanjing University of Posts and TelecommunicationsChina
  2. 2.University of Western MacedoniaGreece
  3. 3.Hosei UniversityJapan

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