An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS Multiagent and Grid Systems Pub Date : 2023-12-15 DOI:10.3233/mgs-221532
Jianfeng Meng, Gongpeng Zhang, Zihan Li, Hongji Yang
{"title":"An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities","authors":"Jianfeng Meng, Gongpeng Zhang, Zihan Li, Hongji Yang","doi":"10.3233/mgs-221532","DOIUrl":null,"url":null,"abstract":"Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multiagent and Grid Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/mgs-221532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
众包社区知识共享社会偏好的进化机制
众包社区作为互联网时代和用户崛起时代企业获取外部公共创新知识的重要途径,具有非常广阔的应用前景和研究价值。然而,在促进众包社区知识共享的过程中,很少考虑社会偏好的影响。因此,在复杂网络演化博弈理论和社会偏好理论的基础上,根据小世界网络结构的特点,构建了众包社区用户知识共享的演化博弈模型。通过Matlab编程,模拟了该网络结构上众包社区用户知识共享的演化过程和动态均衡,并对不考虑社会偏好和社会偏好的实验结果进行了对比分析,发现社会偏好能显著促进众包社区知识共享的演化。该研究拓展了复杂网络博弈与其他学科结合应用的研究范围,丰富了众包社区知识共享研究的理论视角,对促进众包社区知识共享具有较强的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Multiagent and Grid Systems
Multiagent and Grid Systems COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.50
自引率
0.00%
发文量
13
期刊最新文献
Blockchain applications for Internet of Things (IoT): A review Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image Optimization enabled elastic scaling in cloud based on predicted load for resource management Geese jellyfish search optimization trained deep learning for multiclass plant disease detection using leaf images Adam Adadelta Optimization based bidirectional encoder representations from transformers model for fake news detection on social media
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1