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SOCIAL NORM AND LONG-RUN LEARNING IN PEER-TO-PEER NETWORKS

Full Paper at IEEE Xplore

Bio-inspired Information Processing and Networks

Presented by: Mihaela van der Schaar, Author(s): Yu Zhang, Mihaela van der Schaar, University of California Los Angeles, United States

We start by formulating the resource sharing in peer-to-peer (P2P) networks as a random-matching gift-giving game, where self-interested peers aim at maximizing their own long-term utilities. In order to provide incentives for the peers to voluntarily share their resources, we propose to design protocols that operate according to pre-determined social norms. To optimize their long-term performance when playing such a game, peers can learn to play the best response by solving individual stochastic control problems. We first show that when a peer learns in an environment in which its opponents play a fixed strategy, learning will provide an advantage for this peer (i.e. it will lead to an increased utility for the learning peer). If all the peers in the network learn, we prove that learning remains beneficial for the peers. Moreover, we prove that the network will converge to the “fully-cooperative state” (where a socially optimal outcome is attained) if the update error of the peers’ reputations is sufficiently small and the benefit of participating in the stage game is sufficiently larger than the incurred cost.

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Recorded: 2011-05-24 16:35 - 16:55, Club D
Added: 15. 6. 2011 07:25
Number of views: 17
Video resolution: 1024x576 px, 512x288 px
Video length: 0:20:57
Audio track: MP3 [7.09 MB], 0:20:57