Evidence map›Paper›PMID 26551649›Full record

ArticlePloS one2015

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

Kaida Song, Rui Wang, Yi Liu, Depei Qian, Han Zhang, Jihong Cai

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Kaida SongState Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
Rui WangState Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
Yi LiuSchool of Computer Science and Engineering, Beihang University, Beijing, China.
Depei QianSchool of Computer Science and Engineering, Beihang University, Beijing, China.
Han ZhangScience and Technology on Special System simulation Laboratory, Beijing Simulation Center, Beijing, China.
Jihong CaiScience and Technology on Special System simulation Laboratory, Beijing Simulation Center, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Indexed as

Community NetworksCooperative BehaviorGame TheoryInterpersonal RelationsSocial NetworkingAlgorithmsHumansInternetModels, TheoreticalMotivationSocial Support

Identifiers

PMID26551649
PMCPMC4638359

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.