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2 - Indirect Reciprocity Game in Cognitive Networks

from Part I - Indirect Reciprocity

Published online by Cambridge University Press:  01 July 2021

Yan Chen
Affiliation:
University of Science and Technology of China
Chih-Yu Wang
Affiliation:
Academica Sinica, Taipei
Chunxiao Jiang
Affiliation:
Tsinghua University, Beijing
K. J. Ray Liu
Affiliation:
University of Maryland, College Park
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Summary

In cognitive networks, how to stimulate cooperation among nodes is very important. However, most existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy.

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Publisher: Cambridge University Press
Print publication year: 2021

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